Difference: ApprovedNoisePlotsJetEtMiss (1 vs. 22)

Revision 222010-12-06 - ElmarRitsch

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Missing ET performance on random triggers

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 no calibration is applied (em-scale). See this plot under "Public plots from collision data".
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Etmiss_randomevents.gif
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Etmiss_randomevents.gif
 

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Inclusive distributions of ETmiss for both methods are shown, showing again the better noise suppression of the topocluster method. Tails in the distribution (beyond 8 GeV for topocluster-based, and 16 GeV for cell-based, variables), contributing less than 0.1% of events, have been understood to come from coherent noise in a specific region of LAr presampler.
>
>
Inclusive distributions of ETmiss for both methods are shown, showing again the better noise suppression of the topocluster method. Tails in the distribution (beyond 8 GeV for topocluster-based, and 16 GeV for cell-based, variables), contributing less than 0.1% of events, have been understood to come from coherent noise in a specific region of LAr presampler.
 eps version
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Deviation of the mean of the EXmiss distribution ($\mu$) from its average value <$\mu$> (0.103 ± 0.005 GeV).
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Deviation of the mean of the EXmiss distribution ($\mu$) from its average value <$\mu$> (0.103 ± 0.005 GeV).
 Good stability is seen over the month and a half period.

eps version

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Deviation of the width of the EXmiss distribution ($\sigma$) from its average value <$\sigma$> (1.000 ± 0.005 GeV).
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Deviation of the width of the EXmiss distribution ($\sigma$) from its average value <$\sigma$> (1.000 ± 0.005 GeV).
 Good stability is seen over the month and a half period, with no significant change with time.

eps version

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Deviation of the mean of the EYmiss distribution ($\mu$) from its average value <$\mu$> (0.023 ± 0.004 GeV).
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Deviation of the mean of the EYmiss distribution ($\mu$) from its average value <$\mu$> (0.023 ± 0.004 GeV).
 Good stability is seen over the month and a half period, with no significant change with time.

eps version

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Deviation of the width of the EYmiss distribution ($\sigma$) from its average value <$\sigma$> (0.932 ± 0.003 GeV).
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Deviation of the width of the EYmiss distribution ($\sigma$) from its average value <$\sigma$> (0.932 ± 0.003 GeV).
 Good stability is seen over the month and a half period, with no significant change with time.

eps version

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Deviation of the mean of the $\Sigma$ET distribution ($\mu$); from its average value <$\mu$> (0.780 ± 0.014 GeV).
Good stability is seen over the month and a half period: variations are observed, but are small compared to the width of the distribution (~1.4 GeV).
>
>
Deviation of the mean of the $\Sigma$ET distribution ($\mu$); from its average value <$\mu$> (0.780 ± 0.014 GeV).
Good stability is seen over the month and a half period: variations are observed, but are small compared to the width of the distribution (~1.4 GeV).
 eps version
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Deviation of the width of the $\Sigma$ET distribution ($\sigma$) from its average value <$\sigma$> (1.372 ± 0.006 GeV).
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Deviation of the width of the $\Sigma$ET distribution ($\sigma$) from its average value <$\sigma$> (1.372 ± 0.006 GeV).
 Good stability is seen over the month and a half period, with no significant change with time.

Revision 212010-05-10 - PatrickJussel

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Missing ET performance on random triggers

Revision 202010-01-04 - TancrediCarli

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 The Gaussian noise model parametrises the cell energy distribution, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has a standard deviation which is equal to the respective $\sigma$noise value derived from the CaloNoiseToolDB.

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Etmiss distribution of random events
Missing transverse energy distribution for random triggers (noise)
Shown in red is the sum over all cell above noise and in blue the result of summing over all topological clusters (noise suppression using 4/2/0). Data are shown as markers.
Overlayed is a Monte Carlo simulation where the noise is modeled as simple single Gaussian distribution
Used are randomly triggered events from run 150541; taken the 23th of November 2009.
no calibration is applied (em-scale). See this plot under "Public plots from collision data".
Etmiss_randomevents.gif
 

Revision 192009-12-04 - RichardTeuscher

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Introduction

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Contact the Jet/EtMiss combined performance coordinators (Tancredi.Carli@cernNOSPAMPLEASE.ch, proudfoot@anlNOSPAMPLEASE.gov) in case of questions and/or suggestions.
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Contact the Jet/EtMiss combined performance coordinators (Tancredi.Carli@cernNOSPAMPLEASE.ch, Richard.Teuscher@cernNOSPAMPLEASE.ch) in case of questions and/or suggestions.
  The ATLAS calorimeters have recorded millions of cosmic ray and random triggered events. Detailed understanding and improvement of the signal reconstruction has made it possible to use these events to study the performance of higher level quantities such as missing ET. Data taken with close to full detector readout in September and October 2008 were reprocessed at the end of 2008. The performance of standard calorimeter missing ET algorithms, as planned to be used for the analysis of collision data, on is shown here on a sample of events collected with the random trigger.

Revision 182009-11-24 - PatrickJussel

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Missing ET performance on random triggers

Revision 172009-04-20 - DaveCharlton

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Major updates:
-- DimitrisVarouchas - 27 Mar 2009 -- ElisabethPetit - 29 Mar 2009
 
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Tancredi Carli and Jimmy Proudfoot
 
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Revision 162009-04-09 - DaveCharlton

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Missing ET performance on random triggers. UNAPPROVED, FOR INTERNAL DISCUSSIONS ONLY

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Missing ET performance on random triggers

 
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This wiki is meant for preparation of plots and text to be approved for studies of Missing ET performance on random triggers. For internal iteration within working group only, final version will be made available in proper public place.

Introduction

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Introduction

 Contact the Jet/EtMiss combined performance coordinators (Tancredi.Carli@cernNOSPAMPLEASE.ch, proudfoot@anlNOSPAMPLEASE.gov) in case of questions and/or suggestions.
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The ATLAS calorimeters have recorded millions of cosmic ray and random trigger. Detailed understanding and improvement of the signal reconstruction has made it possible to study the performance on these events of higher level quantities such as missing ET. Data taken with close to full detector readout in September and October 2008 has been reprocessed at the end of 2008. The performance of standard calorimeter MissingET algorithms, as planned to be used for the analysis of the collision data, on random trigger is shown here.
>
>
The ATLAS calorimeters have recorded millions of cosmic ray and random triggered events. Detailed understanding and improvement of the signal reconstruction has made it possible to use these events to study the performance of higher level quantities such as missing ET. Data taken with close to full detector readout in September and October 2008 were reprocessed at the end of 2008. The performance of standard calorimeter missing ET algorithms, as planned to be used for the analysis of collision data, on is shown here on a sample of events collected with the random trigger.
  The missing vectorial and scalar transverse energies in the calorimeters are reconstructed using two methods:
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  • The cell based method uses all cells above a noise threshold of two standard deviations (|E|>2*$\sigma_{noise}$). This method is a simple one that characterises the basic detector performance.
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  • The cell-based method uses all cells above a noise threshold of two standard deviations (|E|>2*$\sigma_{noise}$). This method is a simple one that characterises the basic detector performance.
 
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  • The topo-cluster based method uses topological clusters measured in the calorimeter. Clusters are derived from calorimeter cells by adding the energy in neighbouring cells with a dynamical topological cluster algorithm. All the cells in the neighbourhood of the cluster are included, if they have an energy larger than a predefined threshold (neighbour threshold). The procedure is repeated until no cells in the neighbourhood of the cluster are found. As a last step all cells surrounding the cluster are merged to the cluster. The default configuration uses a seed threshold of 4 standard deviations and a neighbour threshold of 2 standard deviations
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  • The topocluster-based method uses topological clusters measured in the calorimeter. Clusters are derived from calorimeter cells by adding the energy in neighbouring cells with a dynamical topological cluster algorithm. All the cells in the neighbourhood of the cluster are included, if they have an energy larger than a predefined threshold (neighbour threshold). The procedure is repeated until no cells in the neighbourhood of the cluster are found. As a last step all cells surrounding the cluster are merged to the cluster. The default configuration uses a seed threshold of 4 standard deviations and a neighbour threshold of 2 standard deviations.
 
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The width of the energy distribution in each cell, $\sigma$noise, has been estimated previously on a cell by cell basis for both LAr and Tile calorimeters as the RMS of the energy distribution in one early calibration run, and recorded in the database used at reconstruction level (known in Atlas as CaloNoiseToolDB). Cells with very high noise are masked early in calorimeter reconstruction.
>
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The width of the energy distribution in each cell, $\sigma$noise, has been estimated on a cell by cell basis for both LAr and Tile calorimeters as the RMS of the energy distribution in one early calibration run, and recorded in the database used at reconstruction level (known in ATLAS as CaloNoiseToolDB). Cells with very high noise are masked early in calorimeter reconstruction.
 
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Different missing ET variables are being looked at:
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Different ETmiss-related variables are studied here:
 
  • EXmiss = - $\Sigma$E sin$\theta$cos$\varphi$
  • EYmiss = - $\Sigma$E sin$\theta$sin$\varphi$
  • $\Sigma$ET = $\Sigma$E sin$\theta$
  • ETmiss = √((EXmiss)2+(EYmiss)2)
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Since random triggers are used, no real energy is expected to be deposited in the calorimeters. So the only contribution to missing ET is electronic noise. Hence EXmiss, EYmiss and $\Sigma$ET are expected to follow Gaussian distributions centered on 0.
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Since random triggers are used, no real energy is expected to be deposited in the calorimeters. So the only contribution to the missing ET is electronic noise. Hence EXmiss, EYmiss and $\Sigma$ET may be expected to follow Gaussian distributions centered on 0.
 
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MissingET variables with random triggers

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Missing ET variables with random triggers

  Detailed analysis has been made with 50292 random events from run 91639, taken the 14th of October 2008.
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The cell based algorithm is a simple one that is used to assess the basic calorimeter performances. The topological clustering algorithm provides a better noise suppression and therefore a better ETmiss resolution. This algorithm is close to the default ETmiss reconstruction algorithm that will be used for the analysis of collision data since it provides more refined results. However it requires a more accurate description of the noise in the calorimeter.

The Gaussian noise model parametrises the cell energy distribution, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has an standard deviation which is equal to the respective $\sigma$noise value derived from the CaloNoiseToolDB.

>
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The cell based algorithm is a simple one that is used to assess the basic calorimeter performance. The topological clustering algorithm provides a better noise suppression and therefore a better missing ET resolution. This algorithm is close to the default missing ET reconstruction algorithm that will be used for the analysis of collision data since it provides more refined results. However it requires a more accurate description of the noise in the calorimeter.
 
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The Gaussian noise model parametrises the cell energy distribution, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has a standard deviation which is equal to the respective $\sigma$noise value derived from the CaloNoiseToolDB.
 

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Cell and topo cluster based EXmiss showing a good control of the energy reconstruction in the 187000 cells of the Calorimeter. The topo cluster based missing energy has better noise suppression. eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
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Cell- and topocluster-based EXmiss and EYmiss distributions, showing a good control of the energy reconstruction in the 187000 cells of the Calorimeter. The topocluster-based distributions show better noise suppression than when using the cell-based method.
 
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EXmiss<br />
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EYmiss is very similar to EXmiss as expected. eps version
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EXmiss
 

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EYmiss
 
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$\Sigma$ET also has the expected Gaussian shape, with similar improvement of topocluster based missing ET w.r.t cell based. A small shift (w.r.t.) RMS of the cell based $\Sigma$ET is being scrutinised. eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
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$\Sigma$ET also has the expected Gaussian shape, with a similar improvement of the topocluster-based evaluation compared to the cell-based one. A small shift (compared to the RMS of the distribution) of the cell-based $\Sigma$ET is visible, and is being studied further.

eps version

 
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sumET<br />
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sumET
 

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Inclusive distributions of ETmiss for both methods are shown, showing again the better noise suppression of the topocluster method. Tails in the distribution (beyond 8 GeV for topo clusters based and 16 GeV for cell based) contributing to less than 0.1% of events have been understood to come from coherent noise in a specific region of LAr presampler (reference to LAr approved plots needed there when available). eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
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Inclusive distributions of ETmiss for both methods are shown, showing again the better noise suppression of the topocluster method. Tails in the distribution (beyond 8 GeV for topocluster-based, and 16 GeV for cell-based, variables), contributing less than 0.1% of events, have been understood to come from coherent noise in a specific region of LAr presampler.
 
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MET_data
 

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The expected ETmiss distribution obtained by a randomisation of the cell energy with a Gaussian noise of width $\sigma$noise is superimposed to the measured one, showing a good agreement. Similar studies for the topocluster based missing ET requires an accurate description of the noise up to and beyond 4 $\sigma$, which is being worked on.

eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch

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The expected ETmiss distribution obtained by a randomisation of the cell energy with a Gaussian noise of width $\sigma$noise, superimposed on the measured cell-based ETmiss distribution. A good description of the observed distribution is seen. Similar studies for the topocluster-based missing ET require an accurate description of the noise up to and beyond 4 $\sigma$: this work is in progress.
 
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MET_CellBased
 
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Evolution of topocluster-based missingET variables with time

Use of 35 runs taken between the 10th of September and the 23th of October. Thus run 91639 corresponds to day 36.
For each run, reconstruct missing ET variables computed with standard topocluster algorithm, and fit with Gaussian distribution to extract mean ($\mu$) and standard deviation ($\sigma$).

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Stability of topocluster-based missing ET variables with time

 
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The following plots use data taken in 35 runs between 10th September and 23rd October 2008. Run 91639 was taken on the following day (day 36).
 
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For each run, missing ET variables were computed with the standard topocluster algorithm, and fit with Gaussian distributions to extract the mean ($\mu$) and standard deviation ($\sigma$).
 
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 Deviation of the mean of the EXmiss distribution ($\mu$) from its average value <$\mu$> (0.103 ± 0.005 GeV).
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Great stability over one month and a half.

eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch

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Good stability is seen over the month and a half period.
 
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EXmiss_mean
 
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 Deviation of the width of the EXmiss distribution ($\sigma$) from its average value <$\sigma$> (1.000 ± 0.005 GeV).
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Great stability over one month and a half. No significant change in resolution with time.

eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch

EXmiss_width<br />
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Good stability is seen over the month and a half period, with no significant change with time.
 
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EXmiss_width
 
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 Deviation of the mean of the EYmiss distribution ($\mu$) from its average value <$\mu$> (0.023 ± 0.004 GeV).
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Great stability over one month and a half.
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Good stability is seen over the month and a half period, with no significant change with time.
 
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eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch

EYmiss_mean<br />
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 Deviation of the width of the EYmiss distribution ($\sigma$) from its average value <$\sigma$> (0.932 ± 0.003 GeV).
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Great stability over one month and a half. No significant change in resolution with time.

eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch

EYmiss_width<br />
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Good stability is seen over the month and a half period, with no significant change with time.
 
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Deviation of the mean of the sumET distribution ($\mu$); from its average value <$\mu$> (0.780 ± 0.014 GeV).
Good stability over one month and a half: variations are small compared to absolute value of Gaussian width.

eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch

sumET_mean<br />

Deviation of the width of the sumET distribution ($\sigma$) from its average value <$\sigma$> (1.372 ± 0.006 GeV).
Great stability over one month and a half. No significant change in resolution with time.

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eps version
EYmiss_width
 
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Deviation of the mean of the $\Sigma$ET distribution ($\mu$); from its average value <$\mu$> (0.780 ± 0.014 GeV).
Good stability is seen over the month and a half period: variations are observed, but are small compared to the width of the distribution (~1.4 GeV).
 
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Deviation of the width of the $\Sigma$ET distribution ($\sigma$) from its average value <$\sigma$> (1.372 ± 0.006 GeV).
Good stability is seen over the month and a half period, with no significant change with time.
 
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Revision 152009-04-06 - ElisabethPetit

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Evolution of topocluster-based missingET variables with time

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Use of 35 runs taken between the 1Oth of September and the 23th of October. Thus run 91639 corresponds to day 36.
For each run, reconstruct missing ET variables computed with standard topocluster algorithm, and fit with Gaussian distribution to extract mean ($$\mu$) and standard deviation ($\sigma$).
Latex rendering error!! dvi file was not created.
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Use of 35 runs taken between the 10th of September and the 23th of October. Thus run 91639 corresponds to day 36.
For each run, reconstruct missing ET variables computed with standard topocluster algorithm, and fit with Gaussian distribution to extract mean ($\mu$) and standard deviation ($\sigma$).
 

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Deviation of the mean of the EXmiss distrobution ($$\mu$) from its average value <$$\mu$> (0.10 ± 0.01 GeV).

Latex rendering error!! dvi file was not created.
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Deviation of the mean of the EXmiss distribution ($\mu$) from its average value <$\mu$> (0.103 ± 0.005 GeV).
 Great stability over one month and a half.

eps version

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Deviation of the width of the EXmiss distribution ($\sigma$) from its average value <$\sigma$> (1.00 ± 0.00 GeV).
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Deviation of the width of the EXmiss distribution ($\sigma$) from its average value <$\sigma$> (1.000 ± 0.005 GeV).
 Great stability over one month and a half. No significant change in resolution with time.
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Deviation of the mean of the EYmiss distribution ($$\mu$) from its average value <$$\mu$> (0.02 ± 0.00 GeV).

Latex rendering error!! dvi file was not created.
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Deviation of the mean of the EYmiss distribution ($\mu$) from its average value <$\mu$> (0.023 ± 0.004 GeV).
 Great stability over one month and a half.
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Deviation of the width of the EYmiss distribution ($\sigma$) from its average value <$\sigma$> (0.93 ± 0.00 GeV).
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Deviation of the width of the EYmiss distribution ($\sigma$) from its average value <$\sigma$> (0.932 ± 0.003 GeV).
 Great stability over one month and a half. No significant change in resolution with time.
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Deviation of the mean of the sumET distribution (&mu); from its average value <$$\mu$> (0.78 ± 0.01 GeV).

Latex rendering error!! dvi file was not created.
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Deviation of the mean of the sumET distribution ($\mu$); from its average value <$\mu$> (0.780 ± 0.014 GeV).
 Good stability over one month and a half: variations are small compared to absolute value of Gaussian width.

eps version

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Deviation of the width of the sumET distribution ($\sigma$) from its average value <$\sigma$> (1.37 ± 0.01 GeV).
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Deviation of the width of the sumET distribution ($\sigma$) from its average value <$\sigma$> (1.372 ± 0.006 GeV).
 Great stability over one month and a half. No significant change in resolution with time.

Revision 142009-04-03 - ElisabethPetit

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  Deviation of the width of the EXmiss distribution ($\sigma$) from its average value <$\sigma$> (1.00 ± 0.00 GeV).
Great stability over one month and a half.
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Slight degradation of resolution with time.
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No significant change in resolution with time.
  eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
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  Deviation of the width of the EYmiss distribution ($\sigma$) from its average value <$\sigma$> (0.93 ± 0.00 GeV).
Great stability over one month and a half.
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Slight degradation of resolution with time.
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No significant change in resolution with time.
  eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
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  Deviation of the width of the sumET distribution ($\sigma$) from its average value <$\sigma$> (1.37 ± 0.01 GeV).
Great stability over one month and a half.
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Slight degradation of resolution with time.
>
>
No significant change in resolution with time.
 

eps version

Revision 132009-03-30 - ElisabethPetit

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META TOPICPARENT name="EtMissDocuments"
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  The missing vectorial and scalar transverse energies in the calorimeters are reconstructed using two methods:
Changed:
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<
  • The cell based method uses all cells above a noise threshold of two standard deviations (|E|>2*σnoise). This method is a simple one that characterises the basic detector performance.
>
>
  • The cell based method uses all cells above a noise threshold of two standard deviations (|E|>2*$\sigma_{noise}$). This method is a simple one that characterises the basic detector performance.
 
  • The topo-cluster based method uses topological clusters measured in the calorimeter. Clusters are derived from calorimeter cells by adding the energy in neighbouring cells with a dynamical topological cluster algorithm. All the cells in the neighbourhood of the cluster are included, if they have an energy larger than a predefined threshold (neighbour threshold). The procedure is repeated until no cells in the neighbourhood of the cluster are found. As a last step all cells surrounding the cluster are merged to the cluster. The default configuration uses a seed threshold of 4 standard deviations and a neighbour threshold of 2 standard deviations
Changed:
<
<
The width of the energy distribution in each cell, σnoise, has been estimated previously on a cell by cell basis for both LAr and Tile calorimeters as the RMS of the energy distribution in one early calibration run, and recorded in the database used at reconstruction level (known in Atlas as CaloNoiseToolDB). Cells with very high noise are masked early in calorimeter reconstruction.
>
>
The width of the energy distribution in each cell, $\sigma$noise, has been estimated previously on a cell by cell basis for both LAr and Tile calorimeters as the RMS of the energy distribution in one early calibration run, and recorded in the database used at reconstruction level (known in Atlas as CaloNoiseToolDB). Cells with very high noise are masked early in calorimeter reconstruction.
 

Different missing ET variables are being looked at:

Changed:
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<
  • EXmiss = - ΣE sinθcosφ
  • EYmiss = - ΣE sinθsinφ
  • Σ ET = ΣE sinθ
>
>
  • EXmiss = - $\Sigma$E sin$\theta$cos$\varphi$
  • EYmiss = - $\Sigma$E sin$\theta$sin$\varphi$
  • $\Sigma$ET = $\Sigma$E sin$\theta$
 
  • ETmiss = √((EXmiss)2+(EYmiss)2)

Since random triggers are used, no real energy is expected to be deposited in the calorimeters. So the only contribution to missing ET is electronic noise.

Changed:
<
<
Hence EXmiss, EYmiss and ΣET are expected to follow Gaussian distributions centered on 0.
>
>
Hence EXmiss, EYmiss and $\Sigma$ET are expected to follow Gaussian distributions centered on 0.
 
Line: 50 to 50
 The topological clustering algorithm provides a better noise suppression and therefore a better ETmiss resolution. This algorithm is close to the default ETmiss reconstruction algorithm that will be used for the analysis of collision data since it provides more refined results. However it requires a more accurate description of the noise in the calorimeter.
Changed:
<
<
The Gaussian noise model parametrises the cell energy distribution, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has an standard deviation which is equal to the respective σnoise value derived from the CaloNoiseToolDB.
>
>
The Gaussian noise model parametrises the cell energy distribution, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has an standard deviation which is equal to the respective $\sigma$noise value derived from the CaloNoiseToolDB.
 

Line: 91 to 91
 

Changed:
<
<
ΣET also has the expected Gaussian shape, with similar improvement of topocluster based missing ET w.r.t cell based. A small shift (w.r.t.) RMS of the cell based ΣET is being scrutinised.
>
>
$\Sigma$ET also has the expected Gaussian shape, with similar improvement of topocluster based missing ET w.r.t cell based. A small shift (w.r.t.) RMS of the cell based $\Sigma$ET is being scrutinised.
 eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
Line: 121 to 121
 

Changed:
<
<
The expected ETmiss distribution obtained by a randomisation of the cell energy with a Gaussian noise of width σnoise is superimposed to the measured one, showing a good agreement. Similar studies for the topocluster based missing ET requires an accurate description of the noise up to and beyond 4 σ, which is being worked on.
>
>
The expected ETmiss distribution obtained by a randomisation of the cell energy with a Gaussian noise of width $\sigma$noise is superimposed to the measured one, showing a good agreement. Similar studies for the topocluster based missing ET requires an accurate description of the noise up to and beyond 4 $\sigma$, which is being worked on.
  eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
Line: 138 to 138
 

Evolution of topocluster-based missingET variables with time

Use of 35 runs taken between the 1Oth of September and the 23th of October. Thus run 91639 corresponds to day 36.

Changed:
<
<
For each run, reconstruct missing ET variables computed with standard topocluster algorithm, and fit with Gaussian distribution to extract mean (μ) and standard deviation (σ).
>
>
For each run, reconstruct missing ET variables computed with standard topocluster algorithm, and fit with Gaussian distribution to extract mean ($$\mu$) and standard deviation ($\sigma$).
Latex rendering error!! dvi file was not created.
 

Changed:
<
<
Deviation of the mean of the EXmiss distrobution (μ) from its average value <μ> (0.10 ± 0.01 GeV).
>
>
Deviation of the mean of the EXmiss distrobution ($$\mu$) from its average value <$$\mu$> (0.10 ± 0.01 GeV).

Latex rendering error!! dvi file was not created.
 Great stability over one month and a half.

eps version

Line: 154 to 154
 
Changed:
<
<
Deviation of the width of the EXmiss distribution (σ) from its average value <σ> (1.00 ± 0.00 GeV).
>
>
Deviation of the width of the EXmiss distribution ($\sigma$) from its average value <$\sigma$> (1.00 ± 0.00 GeV).
 Great stability over one month and a half. Slight degradation of resolution with time.
Line: 168 to 168
 
Changed:
<
<
Deviation of the mean of the EYmiss distribution (μ) from its average value <μ> (0.02 ± 0.00 GeV).
>
>
Deviation of the mean of the EYmiss distribution ($$\mu$) from its average value <$$\mu$> (0.02 ± 0.00 GeV).

Latex rendering error!! dvi file was not created.
 Great stability over one month and a half.
Line: 180 to 180
 
Changed:
<
<
Deviation of the width of the EYmiss distribution (σ) from its average value <σ> (0.93 ± 0.00 GeV).
>
>
Deviation of the width of the EYmiss distribution ($\sigma$) from its average value <$\sigma$> (0.93 ± 0.00 GeV).
 Great stability over one month and a half. Slight degradation of resolution with time.
Line: 193 to 193
 
Changed:
<
<
Deviation of the mean of the sumET distribution (&mu); from its average value <μ> (0.78 ± 0.01 GeV).
>
>
Deviation of the mean of the sumET distribution (&mu); from its average value <$$\mu$> (0.78 ± 0.01 GeV).

Latex rendering error!! dvi file was not created.
 Good stability over one month and a half: variations are small compared to absolute value of Gaussian width.

eps version

Line: 204 to 204
 
Changed:
<
<
Deviation of the width of the sumET distribution (σ) from its average value <σ> (1.37 ± 0.01 GeV).
>
>
Deviation of the width of the sumET distribution ($\sigma$) from its average value <$\sigma$> (1.37 ± 0.01 GeV).
 Great stability over one month and a half. Slight degradation of resolution with time.

Revision 122009-03-30 - DimitrisVarouchas

Line: 1 to 1
 
META TOPICPARENT name="EtMissDocuments"
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Cell and topo cluster based EXmiss showing a good control of the energy reconstruction in the 187000 cells of the Calorimeter. The topo cluster based missing energy has better noise suppression.
Changed:
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<
eps version
>
>
eps version
 Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
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EYmiss is very similar to EXmiss as expected.
Changed:
<
<
eps version
>
>
eps version
 Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch

Line: 92 to 92
 
ΣET also has the expected Gaussian shape, with similar improvement of topocluster based missing ET w.r.t cell based. A small shift (w.r.t.) RMS of the cell based ΣET is being scrutinised.
Changed:
<
<
eps version
>
>
eps version
 Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
Line: 106 to 106
 
Inclusive distributions of ETmiss for both methods are shown, showing again the better noise suppression of the topocluster method. Tails in the distribution (beyond 8 GeV for topo clusters based and 16 GeV for cell based) contributing to less than 0.1% of events have been understood to come from coherent noise in a specific region of LAr presampler (reference to LAr approved plots needed there when available).
Changed:
<
<
eps version
>
>
eps version
 Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
Line: 124 to 124
 The expected ETmiss distribution obtained by a randomisation of the cell energy with a Gaussian noise of width σnoise is superimposed to the measured one, showing a good agreement. Similar studies for the topocluster based missing ET requires an accurate description of the noise up to and beyond 4 σ, which is being worked on.
Changed:
<
<
eps version
>
>
eps version
 Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
Line: 259 to 258
 
META FILEATTACHMENT attachment="EYmiss_METtopo_mean_time.eps" attr="" comment="" date="1238160355" name="EYmiss_METtopo_mean_time.eps" path="EYmiss_METtopo_mean_time.eps" size="8142" stream="EYmiss_METtopo_mean_time.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EYmiss_METtopo_width_time.eps" attr="" comment="" date="1238160406" name="EYmiss_METtopo_width_time.eps" path="EYmiss_METtopo_width_time.eps" size="8145" stream="EYmiss_METtopo_width_time.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="sumET_METtopo_mean_time.eps" attr="" comment="" date="1238160458" name="sumET_METtopo_mean_time.eps" path="sumET_METtopo_mean_time.eps" size="8137" stream="sumET_METtopo_mean_time.eps" user="Main.DimitrisVarouchas" version="1"
Added:
>
>
META FILEATTACHMENT attachment="MET_CellBased.eps" attr="" comment="" date="1238406494" name="MET_CellBased.eps" path="MET_CellBased.eps" size="15654" stream="MET_CellBased.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="MET_data.eps" attr="" comment="" date="1238406518" name="MET_data.eps" path="MET_data.eps" size="19155" stream="MET_data.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="Py_data.eps" attr="" comment="" date="1238406537" name="Py_data.eps" path="Py_data.eps" size="13640" stream="Py_data.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="Px_data.eps" attr="" comment="" date="1238406558" name="Px_data.eps" path="Px_data.eps" size="13592" stream="Px_data.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="SumEt_data.eps" attr="" comment="" date="1238406575" name="SumEt_data.eps" path="SumEt_data.eps" size="13476" stream="SumEt_data.eps" user="Main.DimitrisVarouchas" version="1"

Revision 112009-03-30 - ElisabethPetit

Line: 1 to 1
 
META TOPICPARENT name="EtMissDocuments"
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Introduction

Changed:
<
<
Please do not add figures on your own. Contact the Jet/EtMiss combined performance coordinators (Tancredi.Carli@cernNOSPAMPLEASE.ch, proudfoot@anlNOSPAMPLEASE.gov) in case of questions and/or suggestions.
>
>
Contact the Jet/EtMiss combined performance coordinators (Tancredi.Carli@cernNOSPAMPLEASE.ch, proudfoot@anlNOSPAMPLEASE.gov) in case of questions and/or suggestions.
 
Changed:
<
<
The ATLAS calorimeters have recorded millions of cosmic ray and random trigger. Detailed understanding and improvement of the signal reconstruction has made it possible to study the performance on these events of higher level quantities such as missing ET. Data taken with close to full detector readout in September and October 2008 has been reprocessed in Christmas 2008. The performance of standard calorimeter MissingET algorithms (as planned to be used for the analysis of the collision data) on random trigger is shown here, using official AOD/ESD containers.
>
>
The ATLAS calorimeters have recorded millions of cosmic ray and random trigger. Detailed understanding and improvement of the signal reconstruction has made it possible to study the performance on these events of higher level quantities such as missing ET. Data taken with close to full detector readout in September and October 2008 has been reprocessed at the end of 2008. The performance of standard calorimeter MissingET algorithms, as planned to be used for the analysis of the collision data, on random trigger is shown here.
 

The missing vectorial and scalar transverse energies in the calorimeters are reconstructed using two methods:

Line: 28 to 28
 
  • The topo-cluster based method uses topological clusters measured in the calorimeter. Clusters are derived from calorimeter cells by adding the energy in neighbouring cells with a dynamical topological cluster algorithm. All the cells in the neighbourhood of the cluster are included, if they have an energy larger than a predefined threshold (neighbour threshold). The procedure is repeated until no cells in the neighbourhood of the cluster are found. As a last step all cells surrounding the cluster are merged to the cluster. The default configuration uses a seed threshold of 4 standard deviations and a neighbour threshold of 2 standard deviations
Changed:
<
<
σnoise has been estimated previously on a cell by cell basis for both LAr and Tile calorimeter as the RMS of the energy distribution in one early calibration run, and entered in the database used at reconstruction level (known in Atlas as CaloNoiseToolDB). Cells with very high noise are masked early in calorimeter reconstruction.
>
>
The width of the energy distribution in each cell, σnoise, has been estimated previously on a cell by cell basis for both LAr and Tile calorimeters as the RMS of the energy distribution in one early calibration run, and recorded in the database used at reconstruction level (known in Atlas as CaloNoiseToolDB). Cells with very high noise are masked early in calorimeter reconstruction.
 

Different missing ET variables are being looked at:

Line: 44 to 44
 

MissingET variables with random triggers

Changed:
<
<
Detailed analysis has been made with 50292 random events from run 91639.
>
>
Detailed analysis has been made with 50292 random events from run 91639, taken the 14th of October 2008.
  The cell based algorithm is a simple one that is used to assess the basic calorimeter performances.
Changed:
<
<
The topological clustering algorithm provides a better noise suppression and therefore a better ETmiss resolution. This algorithm is close to the default ETmiss reconstruction algorithm that will be used for the analysis of collision data since it provides more refined results, however it requires a more accurate description of the noise in the calorimeter.
>
>
The topological clustering algorithm provides a better noise suppression and therefore a better ETmiss resolution. This algorithm is close to the default ETmiss reconstruction algorithm that will be used for the analysis of collision data since it provides more refined results. However it requires a more accurate description of the noise in the calorimeter.
 
Changed:
<
<
The Gaussian noise model parametrises cell energy, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has an standard deviation which is equal to the respective σnoise value derived from the CaloNoiseToolDB.
>
>
The Gaussian noise model parametrises the cell energy distribution, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has an standard deviation which is equal to the respective σnoise value derived from the CaloNoiseToolDB.
 

Line: 59 to 59
 
Cell and topo cluster based EXmiss showing a good control of the energy reconstruction in the 187000 cells of the Calorimeter. The topo cluster based missing energy has better noise suppression.
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

Line: 75 to 76
 
EYmiss is very similar to EXmiss as expected.
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

Line: 89 to 92
 
ΣET also has the expected Gaussian shape, with similar improvement of topocluster based missing ET w.r.t cell based. A small shift (w.r.t.) RMS of the cell based ΣET is being scrutinised.
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

Line: 102 to 106
 
Inclusive distributions of ETmiss for both methods are shown, showing again the better noise suppression of the topocluster method. Tails in the distribution (beyond 8 GeV for topo clusters based and 16 GeV for cell based) contributing to less than 0.1% of events have been understood to come from coherent noise in a specific region of LAr presampler (reference to LAr approved plots needed there when available).
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

Line: 119 to 124
 The expected ETmiss distribution obtained by a randomisation of the cell energy with a Gaussian noise of width σnoise is superimposed to the measured one, showing a good agreement. Similar studies for the topocluster based missing ET requires an accurate description of the noise up to and beyond 4 σ, which is being worked on.
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

Line: 131 to 137
 

Evolution of topocluster-based missingET variables with time

Changed:
<
<
Use of 35 runs taken between the 1Oth of September and the 23th of October.
>
>
Use of 35 runs taken between the 1Oth of September and the 23th of October. Thus run 91639 corresponds to day 36.
 For each run, reconstruct missing ET variables computed with standard topocluster algorithm, and fit with Gaussian distribution to extract mean (μ) and standard deviation (σ).

Changed:
<
<
Deviation of EXmiss mean μ from its average value <μ> (0.10 ± 0.01 GeV).
>
>
Deviation of the mean of the EXmiss distrobution (μ) from its average value <μ> (0.10 ± 0.01 GeV).
 Great stability over one month and a half.
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

EXmiss_mean<br />
Changed:
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<
Deviation of EXmiss width σ from its average value <σ> (1.00 ± 0.00 GeV).
>
>
Deviation of the width of the EXmiss distribution (σ) from its average value <σ> (1.00 ± 0.00 GeV).
 Great stability over one month and a half. Slight degradation of resolution with time.
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

Line: 160 to 168
 
Changed:
<
<
Deviation of EYmiss mean μ from its average value <μ> (0.02 ± 0.00 GeV).
>
>
Deviation of the mean of the EYmiss distribution (μ) from its average value <μ> (0.02 ± 0.00 GeV).
 Great stability over one month and a half.
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

EYmiss_mean<br />
Changed:
<
<
Deviation of EYmiss width σ from its average value <σ> (0.93 ± 0.00 GeV).
>
>
Deviation of the width of the EYmiss distribution (σ) from its average value <σ> (0.93 ± 0.00 GeV).
 Great stability over one month and a half. Slight degradation of resolution with time.
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

Line: 183 to 193
 
Changed:
<
<
Deviation of sumET mean μ from its average value <μ> (0.78 ± 0.01 GeV).
>
>
Deviation of the mean of the sumET distribution (&mu); from its average value <μ> (0.78 ± 0.01 GeV).
 Good stability over one month and a half: variations are small compared to absolute value of Gaussian width.
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

sumET_mean<br />
Changed:
<
<
Deviation of sumET width σ from its average value <σ> (1.37 ± 0.01 GeV).
>
>
Deviation of the width of the sumET distribution (σ) from its average value <σ> (1.37 ± 0.01 GeV).
 Great stability over one month and a half. Slight degradation of resolution with time.
Changed:
<
<
eps version
>
>
eps version
Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch
 

Revision 102009-03-30 - DavidRousseau

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META TOPICPARENT name="EtMissDocuments"
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<!--Person responsible for the page: 
Either leave as is - the creator's name will be inserted; 
Or replace the complete REVINFO tag (including percentages symbols) with a name in the form TwikiUsersName-->
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Responsible: %REVINFO{"$wikiusername" rev="1.1"}%
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Tancredi Carli and Jimmy Proudfoot
 
<!--Once this page has been reviewed, please add the name and the date e.g. StephenHaywood - 31 Oct 2006 -->
Last reviewed by: Never reviewed

Revision 92009-03-30 - ElisabethPetit

Line: 1 to 1
 
META TOPICPARENT name="EtMissDocuments"
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Introduction

Please do not add figures on your own. Contact the Jet/EtMiss combined performance coordinators (Tancredi.Carli@cernNOSPAMPLEASE.ch, proudfoot@anlNOSPAMPLEASE.gov) in case of questions and/or suggestions.
Changed:
<
<
The ATLAS calorimeters have recorded millions of cosmic ray and random trigger. Detailed understanding and improvement of the signal reconstruction has made it possible to study the performance on these events of higher level quantities such as missing ET. Data taken with close to full detector readout in september and october 2008 has been reprocessed in Christmas 2008. were run. The performance of standard calorimeter MissingET algorithms on random trigger is shown here, using official AOD/ESD containers.
>
>
The ATLAS calorimeters have recorded millions of cosmic ray and random trigger. Detailed understanding and improvement of the signal reconstruction has made it possible to study the performance on these events of higher level quantities such as missing ET. Data taken with close to full detector readout in September and October 2008 has been reprocessed in Christmas 2008. The performance of standard calorimeter MissingET algorithms (as planned to be used for the analysis of the collision data) on random trigger is shown here, using official AOD/ESD containers.
 

The missing vectorial and scalar transverse energies in the calorimeters are reconstructed using two methods:

Line: 28 to 28
 
  • The topo-cluster based method uses topological clusters measured in the calorimeter. Clusters are derived from calorimeter cells by adding the energy in neighbouring cells with a dynamical topological cluster algorithm. All the cells in the neighbourhood of the cluster are included, if they have an energy larger than a predefined threshold (neighbour threshold). The procedure is repeated until no cells in the neighbourhood of the cluster are found. As a last step all cells surrounding the cluster are merged to the cluster. The default configuration uses a seed threshold of 4 standard deviations and a neighbour threshold of 2 standard deviations
Changed:
<
<
σnoise has been estimated previously on a cell by cell basis for both LAr and Tile calorimeter as the RMS of the energy distribution in one early calibration run, and entered in the database used at reconstruction level (known in atlas as CaloNoiseToolDB). Cells with very high noise are masked early in calorimeter reconstruction.
>
>
σnoise has been estimated previously on a cell by cell basis for both LAr and Tile calorimeter as the RMS of the energy distribution in one early calibration run, and entered in the database used at reconstruction level (known in Atlas as CaloNoiseToolDB). Cells with very high noise are masked early in calorimeter reconstruction.
 

Different missing ET variables are being looked at:

Line: 38 to 38
 
  • ETmiss = √((EXmiss)2+(EYmiss)2)

Since random triggers are used, no real energy is expected to be deposited in the calorimeters. So the only contribution to missing ET is electronic noise.

Changed:
<
<
Hence EXmiss, EYmiss and &Sigma ET are expected to follow Gaussian distributions centered on 0.
>
>
Hence EXmiss, EYmiss and ΣET are expected to follow Gaussian distributions centered on 0.
 
Line: 88 to 88
 

Changed:
<
<
ΣET also has the expected gaussian shape, with similar improvement of topo cluster based missing ET w.r.t cell based. A small shift (w.r.t.) RMS of the cell based ΣET is being scrutinised.
>
>
ΣET also has the expected Gaussian shape, with similar improvement of topocluster based missing ET w.r.t cell based. A small shift (w.r.t.) RMS of the cell based ΣET is being scrutinised.
 eps version
Line: 116 to 116
 

Changed:
<
<
The expected ETmiss distribution obtained by a randomisation of the cell energy with a gaussian noise of width σnoise is superimposed to the measured one, showing a good agreement. Similar studies for the topo cluster base missing ET requires an accurate description of the noise up to and beyond 4 σ, which is being worked on.
>
>
The expected ETmiss distribution obtained by a randomisation of the cell energy with a Gaussian noise of width σnoise is superimposed to the measured one, showing a good agreement. Similar studies for the topocluster based missing ET requires an accurate description of the noise up to and beyond 4 σ, which is being worked on.
  eps version
Line: 137 to 137
 
Changed:
<
<
Offset of EXmiss mean μ from its average value <μ> (0.10 ± 0.01 GeV).
>
>
Deviation of EXmiss mean μ from its average value <μ> (0.10 ± 0.01 GeV).
 Great stability over one month and a half.

eps version

Line: 147 to 147
 
Changed:
<
<
Offset of EXmiss width σ from its average value <σ> (1.00 ± 0.00 GeV).
>
>
Deviation of EXmiss width σ from its average value <σ> (1.00 ± 0.00 GeV).
 Great stability over one month and a half. Slight degradation of resolution with time.
Line: 160 to 160
 
Changed:
<
<
Offset of EYmiss mean μ from its average value <μ> (0.02 ± 0.00 GeV).
>
>
Deviation of EYmiss mean μ from its average value <μ> (0.02 ± 0.00 GeV).
 Great stability over one month and a half.
Line: 171 to 171
 
Changed:
<
<
Offset of EYmiss width σ from its average value <σ> (0.93 ± 0.00 GeV).
>
>
Deviation of EYmiss width σ from its average value <σ> (0.93 ± 0.00 GeV).
 Great stability over one month and a half. Slight degradation of resolution with time.
Line: 183 to 183
 
Changed:
<
<
Offset of sumET mean μ from its average value <μ> (0.78 ± 0.01 GeV).
Good stability over one month and a half: variations are small compared to width.
>
>
Deviation of sumET mean μ from its average value <μ> (0.78 ± 0.01 GeV).
Good stability over one month and a half: variations are small compared to absolute value of Gaussian width.
  eps version
Line: 193 to 193
 
Changed:
<
<
Offset of sumET width σ from its average value <σ> (1.37 ± 0.01 GeV).
>
>
Deviation of sumET width σ from its average value <σ> (1.37 ± 0.01 GeV).
 Great stability over one month and a half. Slight degradation of resolution with time.

Revision 82009-03-29 - ElisabethPetit

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The expected ETmiss distribution obtained by a randomisation of the cell energy with a gaussian noise of width σnoise is superimposed to the measured one, showing a good agreement.
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Similar studies for the topo cluster base missing ET requires an accurate description of the noise up to and beyond 4 sigma, which is being worked on.
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Similar studies for the topo cluster base missing ET requires an accurate description of the noise up to and beyond 4 σ, which is being worked on.
  eps version
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Evolution of MissingET variables with time

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Evolution of topocluster-based missingET variables with time

Use of 35 runs taken between the 1Oth of September and the 23th of October. For each run, reconstruct missing ET variables computed with standard topocluster algorithm, and fit with Gaussian distribution to extract mean (μ) and standard deviation (σ).

 
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Offset of EXmiss mean μ from its average value <μ> (0.10 ± 0.01 GeV).
Great stability over one month and a half.
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Good Stability

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Offset of EXmiss width σ from its average value <σ> (1.00 ± 0.00 GeV).
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Offset of EYmiss mean μ from its average value <μ> (0.02 ± 0.00 GeV).
Great stability over one month and a half.
  eps version
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Good Stability

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Offset of EYmiss width σ from its average value <σ> (0.93 ± 0.00 GeV).
Great stability over one month and a half. Slight degradation of resolution with time.
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Good Stability

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Offset of sumET mean μ from its average value <μ> (0.78 ± 0.01 GeV).
Good stability over one month and a half: variations are small compared to width.
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Offset of sumET width σ from its average value <σ> (1.37 ± 0.01 GeV).
Great stability over one month and a half. Slight degradation of resolution with time.
 
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  eps version
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<!--For significant updates to the topic, consider adding your 'signature' (beneath this editing box)-->
Major updates:
-- DimitrisVarouchas - 27 Mar 2009
Added:
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-- ElisabethPetit - 29 Mar 2009
 
<!--Person responsible for the page: 
Either leave as is - the creator's name will be inserted; 

Revision 72009-03-27 - DimitrisVarouchas

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META TOPICPARENT name="EtMissDocuments"
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Introduction

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Please do not add figures on your own. Contact the Jet/EtMiss combined performance coordinators (Tancredi.Carli@cernNOSPAMPLEASE.ch, proudfoot@anlNOSPAMPLEASE.gov) in case of questions and/or suggestions.
  The ATLAS calorimeters have recorded millions of cosmic ray and random trigger. Detailed understanding and improvement of the signal reconstruction has made it possible to study the performance on these events of higher level quantities such as missing ET. Data taken with close to full detector readout in september and october 2008 has been reprocessed in Christmas 2008. were run. The performance of standard calorimeter MissingET algorithms on random trigger is shown here, using official AOD/ESD containers.
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 Different missing ET variables are being looked at:
  • EXmiss = - ΣE sinθcosφ
  • EYmiss = - ΣE sinθsinφ
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  • Σ ET = ΣE sin&theta
  • ETmiss = - Σ((EXmiss)2+(EYmiss)2)
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  • Σ ET = ΣE sinθ
  • ETmiss = √((EXmiss)2+(EYmiss)2)
  Since random triggers are used, no real energy is expected to be deposited in the calorimeters. So the only contribution to missing ET is electronic noise. Hence EXmiss, EYmiss and &Sigma ET are expected to follow Gaussian distributions centered on 0.
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META FILEATTACHMENT attachment="Image_1.png" attr="" comment="" date="1238143422" name="Image_1.png" path="Image 1.png" size="585207" stream="Image 1.png" user="Main.DavidRousseau" version="1"
META FILEATTACHMENT attachment="EXmiss_METtopo_mean_time.gif" attr="" comment="" date="1238157316" name="EXmiss_METtopo_mean_time.gif" path="EXmiss_METtopo_mean_time.gif" size="9038" stream="EXmiss_METtopo_mean_time.gif" user="Main.DimitrisVarouchas" version="1"

Revision 62009-03-27 - DavidRousseau

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ApprovedNoisePlotsJetEtMiss

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Missing ET performance on random triggers. UNAPPROVED, FOR INTERNAL DISCUSSIONS ONLY

 
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This wiki is meant for preparation of plots and text to be approved for studies of Missing ET performance on random triggers. For internal iteration within working group only, final version will be made available in proper public place.
 
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PRELIMINARY PRELIMINARY PLEASE IGNORE: MISSING ET PERFORMANCE IN RANDOM TRIGGERS

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Introduction

 
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The ATLAS calorimeters have recorded millions of cosmic ray and random trigger. Detailed understanding and improvement of the signal reconstruction has made it possible to study the performance on these events of higher level quantities such as missing ET. Data taken with close to full detector readout in september and october 2008 has been reprocessed in Christmas 2008. were run. The performance of standard calorimeter MissingET algorithms on random trigger is shown here, using official AOD/ESD containers.
 
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In the following the missing vectorial and scalar transverse energies in the calorimeters are reconstructed using two methods:
 
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  • The cell based method uses all cells above a noise threshold of two standard deviations (|E|>2*σnoise). This method is a simple one that characterises the basic detector performance.
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The missing vectorial and scalar transverse energies in the calorimeters are reconstructed using two methods:

  • The cell based method uses all cells above a noise threshold of two standard deviations (|E|>2*σnoise). This method is a simple one that characterises the basic detector performance.
 
  • The topo-cluster based method uses topological clusters measured in the calorimeter. Clusters are derived from calorimeter cells by adding the energy in neighbouring cells with a dynamical topological cluster algorithm. All the cells in the neighbourhood of the cluster are included, if they have an energy larger than a predefined threshold (neighbour threshold). The procedure is repeated until no cells in the neighbourhood of the cluster are found. As a last step all cells surrounding the cluster are merged to the cluster. The default configuration uses a seed threshold of 4 standard deviations and a neighbour threshold of 2 standard deviations
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We look at different missing ET variables:
  •   EXmiss = - ΣE.sinθ.cosφ
  •   EYmiss = - ΣE.sinθ.sinφ
  •   ΣET = ΣE.sinθ
  •   ETmiss = - √((EXmiss)2+(EYmiss)2)
>
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σnoise has been estimated previously on a cell by cell basis for both LAr and Tile calorimeter as the RMS of the energy distribution in one early calibration run, and entered in the database used at reconstruction level (known in atlas as CaloNoiseToolDB). Cells with very high noise are masked early in calorimeter reconstruction.
 
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Since we use random triggers, there is no real energy deposited in the calorimeters. So the only contribution to missing ET is electronic noise. That is why we expect EXmiss, EYmiss and ΣET to follow Gaussian distributions centered on 0.
 
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Here, only high noisy channels are masked during the missing ET reconstruction using the standard list of cells as given in the database.
>
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Different missing ET variables are being looked at:
  • EXmiss = - ΣE sinθcosφ
  • EYmiss = - ΣE sinθsinφ
  • Σ ET = ΣE sin&theta
  • ETmiss = - Σ((EXmiss)2+(EYmiss)2)
 
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All data comes from Christmas reprocessing, using the official AOD/ESD containers
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Since random triggers are used, no real energy is expected to be deposited in the calorimeters. So the only contribution to missing ET is electronic noise. Hence EXmiss, EYmiss and &Sigma ET are expected to follow Gaussian distributions centered on 0.
 
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MissingET variables with random triggers

  • data: 50292 random events from run 91639
    • red markers : METbase, cell based algorithm
    • blue markers : METtopo, topocluster based alogorithm
 
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The cell based algorithm is a simple one that is used to assess the basic calorimeter performances. It will be used for early data.
 
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The topological clustering algorithm provides a better noise suppression and therefore a better ETmiss resolution. This algorithm is close to the default ETmiss reconstruction algorithm that will be used for the analysis of collision data since it provides more refined results.
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MissingET variables with random triggers

 
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Both algorithms demand a very good noise description in the calorimeter.
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Detailed analysis has been made with 50292 random events from run 91639.
 
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The Gaussian noise model parametrises cell energy, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has an standard deviation which is equal to the respective σnoise value derived from the CaloNoiseToolDB.
>
>
The cell based algorithm is a simple one that is used to assess the basic calorimeter performances. The topological clustering algorithm provides a better noise suppression and therefore a better ETmiss resolution. This algorithm is close to the default ETmiss reconstruction algorithm that will be used for the analysis of collision data since it provides more refined results, however it requires a more accurate description of the noise in the calorimeter.
 
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Text, Text, Text
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The Gaussian noise model parametrises cell energy, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has an standard deviation which is equal to the respective σnoise value derived from the CaloNoiseToolDB.
 
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Very good Gaussian shape of EXmiss Message of this plot: a very good performance of 187000 cells in Calorimeter for missingET variables. Text, Text, Text
 
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Cell and topo cluster based EXmiss showing a good control of the energy reconstruction in the 187000 cells of the Calorimeter. The topo cluster based missing energy has better noise suppression.
 eps version
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EXmiss<br />
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EXmiss<br />
 
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Very good Gaussian shape of EYmiss. Message of this plot: a very good performance of 187000 cells in Calorimeter for missingET variables. Small shift (compared to RMS) appearing in EXmiss shows some non understood issues which do not effect the ETmiss plot.
 
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EYmiss is very similar to EXmiss as expected.
 eps version
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EYmiss<br />
 
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EYmiss<br />
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Very good Gaussian shape of   ΣET. Message of this plot: a very good performance of 187000 cells in Calorimeter for missingET variables. Small shift (compared to RMS) appearing in   ΣET shows some non understood issues which do not effect the ETmiss plot. Text, Text, Text
>
>
 
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ΣET also has the expected gaussian shape, with similar improvement of topo cluster based missing ET w.r.t cell based. A small shift (w.r.t.) RMS of the cell based ΣET is being scrutinised.
 eps version
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sumET<br />
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sumET<br />
 
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Tails in ETmiss plot understood (less than 0.1% of events). Their origin is a specific region of LAr presampler, where a coherent noise causes an excess of selected cells. The aim for the moment is to show the official ATLAS output. Text, Text, Text
>
>
 
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Inclusive distributions of ETmiss for both methods are shown, showing again the better noise suppression of the topo cluster method. Tails in the distribution (beyond 8 GeV for topo clusters based and 16 GeV for cell based) contributing to less than 0.1% of events have been understood to come from coherent noisein a specific region of LAr presampler (reference to LAr approved plots needed there when available).
 eps version
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MET_data<br />
 
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Gaussian Noise Model
 
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The expected ETmiss distribution obtained by a randomisation of the cell energy with a gaussian noise of width σnoise is superimposed to the measured one, showing a good agreement. Similar studies for the topo cluster base missing ET requires an accurate description of the noise up to and beyond 4 sigma, which is being worked on.
  eps version
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 MET_CellBased<br />

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Evolution of MissingET variables with time

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Evolution of MissingET variables with time

 
Text, Text, Text

Revision 52009-03-27 - DimitrisVarouchas

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META TOPICPARENT name="EtMissDocuments"
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PRELIMINARY PRELIMINARY PLEASE IGNORE

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PRELIMINARY PRELIMINARY PLEASE IGNORE: MISSING ET PERFORMANCE IN RANDOM TRIGGERS

 

In the following the missing vectorial and scalar transverse energies in the calorimeters are reconstructed using two methods:

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Text, Text, Text
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Very good Gaussian shape of EXmiss
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Very good Gaussian shape of EXmiss
 Message of this plot: a very good performance of 187000 cells in Calorimeter for missingET variables. Text, Text, Text
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Text, Text, Text
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Very good Gaussian shape of EYmiss. Message of this plot: a very good performance of 187000 cells in Calorimeter for missingET variables. Small shift (compared to RMS) appearing in EYmiss shows some non understood issues which do not effect the ETmiss plot.
>
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Very good Gaussian shape of EYmiss. Message of this plot: a very good performance of 187000 cells in Calorimeter for missingET variables. Small shift (compared to RMS) appearing in EXmiss shows some non understood issues which do not effect the ETmiss plot.
  Text, Text, Text
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Text, Text, Text
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Very good Gaussian shape of sumET. Message of this plot: a very good performance of 187000 cells in Calorimeter for missingET variables. Small shift (compared to RMS) appearing in sumET shows some non understood issues which do not effect the ETmiss plot.
>
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Very good Gaussian shape of   ΣET. Message of this plot: a very good performance of 187000 cells in Calorimeter for missingET variables. Small shift (compared to RMS) appearing in   ΣET shows some non understood issues which do not effect the ETmiss plot.
 Text, Text, Text

eps version

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Tails in ETmiss plot understood (less than 0.1% of events). Their origin is a specific region of LAr presampler, where a coherent noise causes an excess of selected cells. The aim for the moment is to show the official ATLAS output.
>
>
Tails in ETmiss plot understood (less than 0.1% of events). Their origin is a specific region of LAr presampler, where a coherent noise causes an excess of selected cells. The aim for the moment is to show the official ATLAS output.
 Text, Text, Text

eps version

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MET_data<br />
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Gaussian Noise Model

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MET_CellBased<br />
 

Evolution of MissingET variables with time

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  Good Stability
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EXmiss_mean<br />
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EYmiss_mean<br />
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sumET_mean<br />
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META FILEATTACHMENT attachment="Image_1.png" attr="" comment="" date="1238143422" name="Image_1.png" path="Image 1.png" size="585207" stream="Image 1.png" user="Main.DavidRousseau" version="1"
META FILEATTACHMENT attachment="EXmiss_METtopo_mean_time.gif" attr="" comment="" date="1238157316" name="EXmiss_METtopo_mean_time.gif" path="EXmiss_METtopo_mean_time.gif" size="9038" stream="EXmiss_METtopo_mean_time.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EYmiss_METtopo_mean_time.gif" attr="" comment="" date="1238150784" name="EYmiss_METtopo_mean_time.gif" path="EYmiss_METtopo_mean_time.gif" size="9073" stream="EYmiss_METtopo_mean_time.gif" user="Main.DimitrisVarouchas" version="1"
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META FILEATTACHMENT attachment="Py_data.gif" attr="" comment="" date="1238152094" name="Py_data.gif" path="Py_data.gif" size="10891" stream="Py_data.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="SumEt_data.gif" attr="" comment="" date="1238152141" name="SumEt_data.gif" path="SumEt_data.gif" size="10445" stream="SumEt_data.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="MET_data.gif" attr="" comment="" date="1238157819" name="MET_data.gif" path="MET_data.gif" size="17364" stream="MET_data.gif" user="Main.DimitrisVarouchas" version="1"
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META FILEATTACHMENT attachment="MET_CellBased.gif" attr="" comment="" date="1238159667" name="MET_CellBased.gif" path="MET_CellBased.gif" size="15348" stream="MET_CellBased.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="sumET_METtopo_width_time.eps" attr="" comment="" date="1238160241" name="sumET_METtopo_width_time.eps" path="sumET_METtopo_width_time.eps" size="8136" stream="sumET_METtopo_width_time.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EXmiss_METtopo_mean_time.eps" attr="" comment="" date="1238160289" name="EXmiss_METtopo_mean_time.eps" path="EXmiss_METtopo_mean_time.eps" size="8142" stream="EXmiss_METtopo_mean_time.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EXmiss_METtopo_width_time.eps" attr="" comment="" date="1238160323" name="EXmiss_METtopo_width_time.eps" path="EXmiss_METtopo_width_time.eps" size="8145" stream="EXmiss_METtopo_width_time.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EYmiss_METtopo_mean_time.eps" attr="" comment="" date="1238160355" name="EYmiss_METtopo_mean_time.eps" path="EYmiss_METtopo_mean_time.eps" size="8142" stream="EYmiss_METtopo_mean_time.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EYmiss_METtopo_width_time.eps" attr="" comment="" date="1238160406" name="EYmiss_METtopo_width_time.eps" path="EYmiss_METtopo_width_time.eps" size="8145" stream="EYmiss_METtopo_width_time.eps" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="sumET_METtopo_mean_time.eps" attr="" comment="" date="1238160458" name="sumET_METtopo_mean_time.eps" path="sumET_METtopo_mean_time.eps" size="8137" stream="sumET_METtopo_mean_time.eps" user="Main.DimitrisVarouchas" version="1"

Revision 42009-03-27 - DavidRousseau

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  • EXmiss_METtopo_width_time.gif:
    EXmiss_METtopo_width_time.gif

  • EYmiss_METtopo_width_time.gif:
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  • sumET_METtopo_mean_time.gif:
    sumET_METtopo_mean_time.gif

  • sumET_METtopo_width_time.gif:
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  • EXmiss_METtopo_mean_time.gif:
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  • MET_data.gif:
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META FILEATTACHMENT attachment="Image_1.png" attr="" comment="" date="1238143422" name="Image_1.png" path="Image 1.png" size="585207" stream="Image 1.png" user="Main.DavidRousseau" version="1"
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META FILEATTACHMENT attachment="EYmiss_METtopo_mean_time.gif" attr="" comment="" date="1238150784" name="EYmiss_METtopo_mean_time.gif" path="EYmiss_METtopo_mean_time.gif" size="9073" stream="EYmiss_METtopo_mean_time.gif" user="Main.DimitrisVarouchas" version="1"

Revision 32009-03-27 - DimitrisVarouchas

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Text, Text, Text
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  eps version
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 EXmiss<br />

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Very good Gaussian shape of EYmiss. Message of this plot: a very good performance of 187000 cells in Calorimeter for missingET variables. Small shift (compared to RMS) appearing in EYmiss shows some non understood issues which do not effect the ETmiss plot.

Text, Text, Text

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 EYmiss<br />

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Very good Gaussian shape of sumET. Message of this plot: a very good performance of 187000 cells in Calorimeter for missingET variables. Small shift (compared to RMS) appearing in sumET shows some non understood issues which do not effect the ETmiss plot. Text, Text, Text
  eps version
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 sumET<br />

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Tails in ETmiss plot understood (less than 0.1% of events). Their origin is a specific region of LAr presampler, where a coherent noise causes an excess of selected cells. The aim for the moment is to show the official ATLAS output. Text, Text, Text
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MET_data<br />
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Evolution of MissingET variables with time

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sumET_width<br />
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<!--For significant updates to the topic, consider adding your 'signature' (beneath this editing box)-->
Major updates:
Changed:
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-- DomiziaOrestano - 09 Mar 2009
>
>
-- DimitrisVarouchas - 27 Mar 2009
 
<!--Person responsible for the page: 
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  • EXmiss_METtopo_mean_time.gif:
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META FILEATTACHMENT attachment="EXmiss_METtopo_mean_time.gif" attr="" comment="" date="1238157316" name="EXmiss_METtopo_mean_time.gif" path="EXmiss_METtopo_mean_time.gif" size="9038" stream="EXmiss_METtopo_mean_time.gif" user="Main.DimitrisVarouchas" version="1"
 
META FILEATTACHMENT attachment="EYmiss_METtopo_mean_time.gif" attr="" comment="" date="1238150784" name="EYmiss_METtopo_mean_time.gif" path="EYmiss_METtopo_mean_time.gif" size="9073" stream="EYmiss_METtopo_mean_time.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EXmiss_METtopo_width_time.gif" attr="" comment="" date="1238150817" name="EXmiss_METtopo_width_time.gif" path="EXmiss_METtopo_width_time.gif" size="9124" stream="EXmiss_METtopo_width_time.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EYmiss_METtopo_width_time.gif" attr="" comment="" date="1238150838" name="EYmiss_METtopo_width_time.gif" path="EYmiss_METtopo_width_time.gif" size="9046" stream="EYmiss_METtopo_width_time.gif" user="Main.DimitrisVarouchas" version="1"
Line: 232 to 242
 
META FILEATTACHMENT attachment="Px_data.gif" attr="" comment="" date="1238152060" name="Px_data.gif" path="Px_data.gif" size="10685" stream="Px_data.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="Py_data.gif" attr="" comment="" date="1238152094" name="Py_data.gif" path="Py_data.gif" size="10891" stream="Py_data.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="SumEt_data.gif" attr="" comment="" date="1238152141" name="SumEt_data.gif" path="SumEt_data.gif" size="10445" stream="SumEt_data.gif" user="Main.DimitrisVarouchas" version="1"
Added:
>
>
META FILEATTACHMENT attachment="MET_data.gif" attr="" comment="" date="1238157819" name="MET_data.gif" path="MET_data.gif" size="17364" stream="MET_data.gif" user="Main.DimitrisVarouchas" version="1"

Revision 22009-03-27 - DimitrisVarouchas

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META TOPICPARENT name="EtMissDocuments"
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PRELIMINARY PRELIMINARY PLEASE IGNORE

Deleted:
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The combined muon performance plots below are approved to be shown by ATLAS speakers at conferences and similar events.
 
Changed:
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<
Please do not add figures on your own. Contact the the muon combined performance coordinators (Domizia.Orestano@cernNOSPAMPLEASE.ch, Carlo.Schiavi@cernNOSPAMPLEASE.ch) in case of questions and/or suggestions.
>
>
In the following the missing vectorial and scalar transverse energies in the calorimeters are reconstructed using two methods:
 
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<

Plots from first reprocessing of cosmics 2008 data

>
>
  • The cell based method uses all cells above a noise threshold of two standard deviations (|E|>2*σnoise). This method is a simple one that characterises the basic detector performance.
 
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The plots show, on cosmic data from autumn 2008, the comparison between quantities reconstructed for inner detector tracks and for matching muon spectrometer ones. The observed differences are in agreement with the expected multiple scattering and energy loss in the calorimeters and are well reproduced by the Monte Carlo simulation. No correction has been yet applied for the relative alignment of the two subdetectors, which appears to be already quite good.

The plots were obtained using 18k data events from ATLAS combined cosmic run 91890, taken with both solenoid and toroid fields on. These events, produced by the December 2008 re-processing at Tier1's, were pre-selected requiring a reconstructed track in the Inner Detector (ID). Simulated events use the latest samples available at the time, made with release 14.4.0. One of the main improvements with respect to previous simulations was the use of the Xe-gas mixture for the transition radiation tracker (TRT) and an updated detector description geometry. The relevant parts of the reconstruction chain were re-run with release 14.5.0.5, with a special fix for an incorrect drift function also used for the simulated data. In addition, one RPC sector (6) was masked in simulation, corresponding to the real detector configuration in the run analysed.

The following event selection was applied in order to emulate the stream and trigger configuration used:

  • At least one track reconstructed in the ID.
  • At least one combined ID-MS reconstructed track with 3 or more RPC Phi hits.

After all these cuts, the number of events in the analysis is:

  • Real data: 4.8k events (initially 18k)
  • Simulated data: 6k events (initially 10k).
>
>
  • The topo-cluster based method uses topological clusters measured in the calorimeter. Clusters are derived from calorimeter cells by adding the energy in neighbouring cells with a dynamical topological cluster algorithm. All the cells in the neighbourhood of the cluster are included, if they have an energy larger than a predefined threshold (neighbour threshold). The procedure is repeated until no cells in the neighbourhood of the cluster are found. As a last step all cells surrounding the cluster are merged to the cluster. The default configuration uses a seed threshold of 4 standard deviations and a neighbour threshold of 2 standard deviations
 
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Tracks reconstructed by MOORE and CTB trackers in the MS and ID respectively were used for this analysis. No global ID-MS alignment corrections were applied in the reconstruction.
>
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We look at different missing ET variables:
  •   EXmiss = - ΣE.sinθ.cosφ
  •   EYmiss = - ΣE.sinθ.sinφ
  •   ΣET = ΣE.sinθ
  •   ETmiss = - √((EXmiss)2+(EYmiss)2)
 
Changed:
<
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Next, the track parameters at perigee are compared between MS and ID standalone reconstructions. Except the momentum, MS track parameters are extrapolated to the beam axis (taking into account the energy loss in the calorimeters) in order to allow for comparisons. Next, the track parameters at perigee are compared between MS and ID standalone reconstructions. In order to compare Theta0 and z0 parameters, ID tracks are required to have Silicon hits.
>
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Since we use random triggers, there is no real energy deposited in the calorimeters. So the only contribution to missing ET is electronic noise. That is why we expect EXmiss, EYmiss and ΣET to follow Gaussian distributions centered on 0.

Here, only high noisy channels are masked during the missing ET reconstruction using the standard list of cells as given in the database.

All data comes from Christmas reprocessing, using the official AOD/ESD containers

MissingET variables with random triggers

  • data: 50292 random events from run 91639
    • red markers : METbase, cell based algorithm
    • blue markers : METtopo, topocluster based alogorithm

The cell based algorithm is a simple one that is used to assess the basic calorimeter performances. It will be used for early data.

The topological clustering algorithm provides a better noise suppression and therefore a better ETmiss resolution. This algorithm is close to the default ETmiss reconstruction algorithm that will be used for the analysis of collision data since it provides more refined results.

Both algorithms demand a very good noise description in the calorimeter.

The Gaussian noise model parametrises cell energy, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has an standard deviation which is equal to the respective σnoise value derived from the CaloNoiseToolDB.

 
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An internal note with some more technical details on the reconstruction is available, for ATLAS members only, at ATL-COM-PHYS-2009-074.
 
Changed:
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Correlation between Muon Spectrometer (MS) and Inner Detector (ID) track parameters [Run 91890]: Phi0 correlation.
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>
Text, Text, Text

Good Stability

 
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The good correlation observed between parameters measured in the MS and ID shows that both sub-detectors are well synchronized.
>
>
eps version

EXmiss<br />

Text, Text, Text

Good Stability

eps version

 
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>

EYmiss<br />

Text, Text, Text

Good Stability

 eps version
Deleted:
<
<

 
Changed:
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Phi0.
>
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sumET<br />
 
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Correlation between Muon Spectrometer (MS) and Inner Detector (ID) track parameters [Run 91890]: Theta0 correlation.
>
>
Text, Text, Text
 
Changed:
<
<
The good correlation observed between parameters measured in the MS and ID shows that both sub-detectors are well synchronized.
>
>
Good Stability
  eps version
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Theta0.
 
Added:
>
>
MET_data<br />
 
Changed:
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Difference between Muon Spectrometer (MS) and Inner Detector (ID) standalone reconstruction [Data/MC comparisons]:
>
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Evolution of MissingET variables with time

 
Changed:
<
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Phi0 parameter.
>
>
Text, Text, Text
 
Changed:
<
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The differences between the track parameters measured in the MS and the ID gives an idea about how well both sub-detectors are mechanically aligned. The agreement data/MC is fairly good.
>
>
Good Stability
 
Changed:
<
<
eps version
>
>
eps version
 
Changed:
<
<

>
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EXmiss_mean<br />
 
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Diff_phi0.
>
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Text, Text, Text

Good Stability

eps version

EXmiss_width<br />
 
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Difference between Muon Spectrometer (MS) and Inner Detector (ID) standalone reconstruction [Data/MC comparisons]:
 
Changed:
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<
Theta0 parameter.
>
>
Text, Text, Text
 
Changed:
<
<
The differences between the track parameters measured in the MS and the ID gives an idea about how well both sub-detectors are mechanically aligned. The agreement data/MC is fairly good.
>
>
Good Stability
 
Changed:
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<
eps version
>
>
eps version
 
Changed:
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Diff_theta0.
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EYmiss_mean<br />
 
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Difference between Muon Spectrometer (MS) and Inner Detector (ID) standalone reconstruction [Data/MC comparisons]:
 
Changed:
<
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d0 parameter.
>
>
Text, Text, Text

Good Stability

 
Changed:
<
<
The differences between the track parameters measured in the MS and the ID (QoverP distribution available for the Inner Detector approved plots page) gives an idea about how well both sub-detectors are mechanically aligned. The agreement data/MC is fairly good.
>
>
eps version
 
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<
eps version
 
Changed:
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Diff_d0.
>
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EYmiss_width<br />
 
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Difference between Muon Spectrometer (MS) and Inner Detector (ID) standalone reconstruction [Data/MC comparisons]:
 
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z0 parameter.
>
>
Text, Text, Text
 
Changed:
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The differences between the track parameters measured in the MS and the ID gives an idea about how well both sub-detectors are mechanically aligned. The agreement data/MC is fairly good, within 4mm.
>
>
Good Stability
 
Changed:
<
<
eps version
>
>
eps version
 
Changed:
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Diff_z0.
>
>

sumET_mean<br />

Text, Text, Text

Good Stability

eps version

sumET_width<br />
 
Deleted:
<
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By taking the difference in the momentum measured in the ID and MS, one can study the energy loss in the calorimeters. The plot shows this difference between ID and bottom MS tracks. A mean value of about 3 GeV is observed for both data and MC.
 
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eps version
 
Deleted:
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Diff_MomentumBottom.
 
Changed:
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<
>
>
 


Line: 176 to 195
 
Added:
>
>
  • EXmiss_METtopo_mean_time.gif:
    EXmiss_METtopo_mean_time.gif

  • EYmiss_METtopo_mean_time.gif:
    EYmiss_METtopo_mean_time.gif

  • EXmiss_METtopo_width_time.gif:
    EXmiss_METtopo_width_time.gif

  • EYmiss_METtopo_width_time.gif:
    EYmiss_METtopo_width_time.gif

  • sumET_METtopo_mean_time.gif:
    sumET_METtopo_mean_time.gif

  • sumET_METtopo_width_time.gif:
    sumET_METtopo_width_time.gif

  • Px_data.gif:
    Px_data.gif

  • Py_data.gif:
    Py_data.gif

 
META FILEATTACHMENT attachment="Image_1.png" attr="" comment="" date="1238143422" name="Image_1.png" path="Image 1.png" size="585207" stream="Image 1.png" user="Main.DavidRousseau" version="1"
Added:
>
>
META FILEATTACHMENT attachment="EXmiss_METtopo_mean_time.gif" attr="" comment="" date="1238150487" name="EXmiss_METtopo_mean_time.gif" path="EXmiss_METtopo_mean_time.gif" size="9038" stream="EXmiss_METtopo_mean_time.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EYmiss_METtopo_mean_time.gif" attr="" comment="" date="1238150784" name="EYmiss_METtopo_mean_time.gif" path="EYmiss_METtopo_mean_time.gif" size="9073" stream="EYmiss_METtopo_mean_time.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EXmiss_METtopo_width_time.gif" attr="" comment="" date="1238150817" name="EXmiss_METtopo_width_time.gif" path="EXmiss_METtopo_width_time.gif" size="9124" stream="EXmiss_METtopo_width_time.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="EYmiss_METtopo_width_time.gif" attr="" comment="" date="1238150838" name="EYmiss_METtopo_width_time.gif" path="EYmiss_METtopo_width_time.gif" size="9046" stream="EYmiss_METtopo_width_time.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="sumET_METtopo_mean_time.gif" attr="" comment="" date="1238150872" name="sumET_METtopo_mean_time.gif" path="sumET_METtopo_mean_time.gif" size="8991" stream="sumET_METtopo_mean_time.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="sumET_METtopo_width_time.gif" attr="" comment="" date="1238150914" name="sumET_METtopo_width_time.gif" path="sumET_METtopo_width_time.gif" size="9107" stream="sumET_METtopo_width_time.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="Px_data.gif" attr="" comment="" date="1238152060" name="Px_data.gif" path="Px_data.gif" size="10685" stream="Px_data.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="Py_data.gif" attr="" comment="" date="1238152094" name="Py_data.gif" path="Py_data.gif" size="10891" stream="Py_data.gif" user="Main.DimitrisVarouchas" version="1"
META FILEATTACHMENT attachment="SumEt_data.gif" attr="" comment="" date="1238152141" name="SumEt_data.gif" path="SumEt_data.gif" size="10445" stream="SumEt_data.gif" user="Main.DimitrisVarouchas" version="1"

Revision 12009-03-27 - DavidRousseau

Line: 1 to 1
Added:
>
>
META TOPICPARENT name="EtMissDocuments"
<!-- This is the default ATLAS template. 
Please modify it in the sections indicated to create your topic! In particular, notice that at the bottom there are some sections that must be filled for publicly accessible pages.
If you have any comments/complaints about this template, then please email : Stephen Haywood (Computing Documentation Coordinator)
S.Haywood at rl.ac.uk (or failing that, edward.moyse at cern.ch)
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<!-- By default the title is the WikiWord used to create this topic !-->
<!-- if you want to modify it to something more meaningful, just replace ApprovedNoisePlotsJetEtMiss below with i.e "My Topic"!-->
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ApprovedNoisePlotsJetEtMiss

<!--optional-->

PRELIMINARY PRELIMINARY PLEASE IGNORE

The combined muon performance plots below are approved to be shown by ATLAS speakers at conferences and similar events.

Please do not add figures on your own. Contact the the muon combined performance coordinators (Domizia.Orestano@cernNOSPAMPLEASE.ch, Carlo.Schiavi@cernNOSPAMPLEASE.ch) in case of questions and/or suggestions.

Plots from first reprocessing of cosmics 2008 data

The plots show, on cosmic data from autumn 2008, the comparison between quantities reconstructed for inner detector tracks and for matching muon spectrometer ones. The observed differences are in agreement with the expected multiple scattering and energy loss in the calorimeters and are well reproduced by the Monte Carlo simulation. No correction has been yet applied for the relative alignment of the two subdetectors, which appears to be already quite good.

The plots were obtained using 18k data events from ATLAS combined cosmic run 91890, taken with both solenoid and toroid fields on. These events, produced by the December 2008 re-processing at Tier1's, were pre-selected requiring a reconstructed track in the Inner Detector (ID). Simulated events use the latest samples available at the time, made with release 14.4.0. One of the main improvements with respect to previous simulations was the use of the Xe-gas mixture for the transition radiation tracker (TRT) and an updated detector description geometry. The relevant parts of the reconstruction chain were re-run with release 14.5.0.5, with a special fix for an incorrect drift function also used for the simulated data. In addition, one RPC sector (6) was masked in simulation, corresponding to the real detector configuration in the run analysed.

The following event selection was applied in order to emulate the stream and trigger configuration used:

  • At least one track reconstructed in the ID.
  • At least one combined ID-MS reconstructed track with 3 or more RPC Phi hits.

After all these cuts, the number of events in the analysis is:

  • Real data: 4.8k events (initially 18k)
  • Simulated data: 6k events (initially 10k).

Tracks reconstructed by MOORE and CTB trackers in the MS and ID respectively were used for this analysis. No global ID-MS alignment corrections were applied in the reconstruction.

Next, the track parameters at perigee are compared between MS and ID standalone reconstructions. Except the momentum, MS track parameters are extrapolated to the beam axis (taking into account the energy loss in the calorimeters) in order to allow for comparisons. Next, the track parameters at perigee are compared between MS and ID standalone reconstructions. In order to compare Theta0 and z0 parameters, ID tracks are required to have Silicon hits.

An internal note with some more technical details on the reconstruction is available, for ATLAS members only, at ATL-COM-PHYS-2009-074.

Correlation between Muon Spectrometer (MS) and Inner Detector (ID) track parameters [Run 91890]: Phi0 correlation.

The good correlation observed between parameters measured in the MS and ID shows that both sub-detectors are well synchronized.

eps version

Phi0.

Correlation between Muon Spectrometer (MS) and Inner Detector (ID) track parameters [Run 91890]: Theta0 correlation.

The good correlation observed between parameters measured in the MS and ID shows that both sub-detectors are well synchronized.

eps version

Theta0.
Difference between Muon Spectrometer (MS) and Inner Detector (ID) standalone reconstruction [Data/MC comparisons]:

Phi0 parameter.

The differences between the track parameters measured in the MS and the ID gives an idea about how well both sub-detectors are mechanically aligned. The agreement data/MC is fairly good.

eps version


Diff_phi0.
Difference between Muon Spectrometer (MS) and Inner Detector (ID) standalone reconstruction [Data/MC comparisons]:

Theta0 parameter.

The differences between the track parameters measured in the MS and the ID gives an idea about how well both sub-detectors are mechanically aligned. The agreement data/MC is fairly good.

eps version


Diff_theta0.
Difference between Muon Spectrometer (MS) and Inner Detector (ID) standalone reconstruction [Data/MC comparisons]:

d0 parameter.

The differences between the track parameters measured in the MS and the ID (QoverP distribution available for the Inner Detector approved plots page) gives an idea about how well both sub-detectors are mechanically aligned. The agreement data/MC is fairly good.

eps version


Diff_d0.
Difference between Muon Spectrometer (MS) and Inner Detector (ID) standalone reconstruction [Data/MC comparisons]:

z0 parameter.

The differences between the track parameters measured in the MS and the ID gives an idea about how well both sub-detectors are mechanically aligned. The agreement data/MC is fairly good, within 4mm.

eps version


Diff_z0.
By taking the difference in the momentum measured in the ID and MS, one can study the energy loss in the calorimeters. The plot shows this difference between ID and bottom MS tracks. A mean value of about 3 GeV is observed for both data and MC.

eps version


Diff_MomentumBottom.


<!--***********************************************************-->
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<!--For significant updates to the topic, consider adding your 'signature' (beneath this editing box)-->
Major updates:
-- DomiziaOrestano - 09 Mar 2009

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META FILEATTACHMENT attachment="Image_1.png" attr="" comment="" date="1238143422" name="Image_1.png" path="Image 1.png" size="585207" stream="Image 1.png" user="Main.DavidRousseau" version="1"
 
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