AtlasPublicTopicHeader.png

ApprovedPlotsTileSignalReconstruction

Introduction

This page lists the public plots illustrating signal reconstruction.

Signal Reconstruction

Energy Reconstruction

This plot shows the distribution of the EBA E4 (1.4< |η| <1.6) Tile calorimeter cells energy (in MeV) estimated by the Wiener Filter and Optimal Filter algorithms using proton-proton collision data from a special run with high number of inelastic collisions per beam crossing (<μ>) with peak <μ>=90 at 13 TeV collected in October 2018. A total of 64 modules in phi are used while known pathological channels were excluded.
Contact: Dayane Oliveira Goncalves
Reference: link to CDS .
Date: 06 March 2019
logdist_WFxOF2.png

his plot shows the EBA E4 (1.4< |η| <1.6) Tile calorimeter cells energy (in MeV) distribution, in logarithmic scale, estimated by the Wiener Filter and Optimal Filter algorithms using proton-proton collision data from a special run with high number of inelastic collisions per beam crossing (<μ>) with peak <μ>=90 at 13 TeV collected in October 2018. A total of 64 modules in phi are used while known pathological channels were excluded.
Contact: Dayane Oliveira Goncalves
Reference: link to CDS .
Date: 06 March 2019
dist_WFxOF2.png

This plot represents the correlation between the EBA E4 (1.4< |η| <1.6) Tile calorimeter cells energy (in MeV) estimated by the Wiener Filter and the one estimated by the Optimal Filter algorithm using proton-proton collision data from a special run with high number of inelastic collisions per beam crossing (<μ>) with peak <μ>=90 at 13 TeV collected in October 2018. A total of 64 modules in phi are used while known pathological channels were excluded.
Contact: Dayane Oliveira Goncalves
Reference: link to CDS .
Date: 06 March 2019
Correlation_wienerFilter_OF2.png

Cell energy distribution reconstructed by the Constrained Optimal Filter (COF) and the Optimal Filtering (OF2) algorithms using 2012 pp collision data at √s = 8 TeV, 25 ns bunch spacing (dT) and maximum average number of interactions per crossing (<μ>) of 11.3 for the JetTauEtmiss stream in run 216399 (around 25 millions entries). The COF method is resilient to Out of time signals, therefore, it presents better energy resolution with respect to OF2. Additionally, its design is luminosity independent and requires only the information of the pulse shape and pedestal value to compute the 7 amplitudes associated to the 7 samples of the read-out. In this plot only the central sample reconstruction is shown.
Contact: Bernardo Peralva
Reference: link to CDS .
Date: 09 March 2015
OF2vsCOF_Ehist_25nsMu11.png
eps version of the figure
Correlation of cell energy reconstructed by the Constrained Optimal Filter (COF) and theOptimal Filtering (OF2) algorithms using 2012 pp collision data at √s = 8 TeV, 25 ns bunch spacing (dT) and maximum average number of interactions per crossing (<μ>) of 11.3 for the JetTauEtmiss stream in run 216399 (around 25 millions entries). Under these conditions, the energy reconstructed by COF is resilient to Out of time (OOT) signals. A small bias due to high amplitude Out of time signals located outside the 7 samples is observed (Out of time signal at 100 ns or further).
Contact: Bernardo Peralva
Reference: link to CDS .
Date: 09 March 2015
OF2vsCOF_Ecorr_25nsMu11.png
pdf version of the figure
Correlation of quality factor computed by the Constrained Optimal Filter (COF) and the Optimal Filtering (OF2) algorithms using 2012 pp collision data at √s = 8 TeV, 25 ns bunch spacing (dT) and maximum average number of interactions per crossing (<μ>) of 11.3 for the JetTauEtmiss stream in run 216399 (around 25 millions entries). The QF computed by COF performs better detecting signals out of time.
Contact: Bernardo Peralva
Reference: link to CDS .
Date: 09 March 2015
OF2vsCOF_QFcorr_25nsMu11_v2.png
[[%ATT ACHURL%/OF2vsCOF_QFcorr_25nsMu11_v2.eps][eps version of the figure ]]
Cell energy distribution reconstructed by the Matched Filter (MF) and the Optimal Filtering (OF2) algorithms using 2010 pp collision data at √s = 7 TeV, 150 ns bunch spacing (dT) and peak average number of interactions per crossing (<μ>) of 3.31 for the JetTauEtmiss stream in run 167776. Under these conditions the reconstruction is not affected by Out-Of-Time signals. However the OF2 has a wider spread as compared to the MF around the noise region (200 MeV). As energy increases we see a better agreement between methods.
Contact: Bernardo Peralva
Reference: ATLAS-PLOT-TILECAL-2013-010 .
Date: 24 September 2013
MFOF2col2010.png
eps version of the figure
Correlation of cell energy reconstructed by the Matched Filter (MF) and the Optimal Filtering (OF2) algorithms using 2010 pp collision data at √s = 7 TeV, 150 ns bunch spacing (dT) and peak average number of interactions per crossing (<μ>) of 3.31 for the JetTauEtmiss stream in run 167776. Under these conditions the reconstruction is not affected by Out-Of-Time signals. As energy increases the response of the two algorithms are strongly correlated.
Contact: Bernardo Peralva
Reference: ATLAS-PLOT-TILECAL-2013-010 .
Date: 24 September 2013
corrCol10.png
eps version of the figure
Cell energy distribution reconstructed by the Matched Filter (MF) and the Optimal Filtering (OF2) algorithms using 2012 pp collision data at √s = 8 TeV, 25 ns bunch spacing (dT) and peak average number of interactions per crossing (<μ>) of 11.3 for the JetTauEtmiss stream in run 216399. The OF2 shows larger negative tails due to the contribution of Out-Of-Time signals.
Contact: Bernardo Peralva
Reference: ATLAS-PLOT-TILECAL-2013-010 .
Date: 24 September 2013
MFOF2col2012.png
eps version of the figure
Cell energy distribution reconstructed by the Matched Filter (MF) and the Optimal Filtering (OF2) algorithms using 2012 pp collision data at √s = 8 TeV, 25 ns bunch spacing (dT) and peak average number of interactions per crossing (<μ>) of 11.3 for the JetTauEtmiss stream in run 216399. The increase of the spread of the distribution obtained with the MF algorithm is smaller than the one obtained with the OF2 algorithm with respect to the 2010 with 150 ns bunch spacing results.
Contact: Bernardo Peralva
Reference: ATLAS-PLOT-TILECAL-2013-010 .
Date: 24 September 2013
MFOF2col2012_2.png
eps version of the figure
Correlation of cell energy reconstructed by the Matched Filter (MF) and the Optimal Filtering (OF2) algorithms using 2012 pp collision data at √s = 8 TeV, 25 ns bunch spacing (dT) and peak average number of interactions per crossing (<μ>) of 11.3 for the JetTauEtmiss stream in run 216399. The contribution of Out-Of-Time (OOT) signals in the different Bunch Crossing (BC) are disentangled in this comparison. The OF2 systematically reconstructs smaller energies than the MF in the presence of OOT signals.
Contact: Bernardo Peralva
Reference: ATLAS-PLOT-TILECAL-2013-010 .
Date: 24 September 2013
corrCol12.png
eps version of the figure
Performance of the ROD/DSP Optimal Filtering Non Iterative reconstruction with collision data (900 GeV). Collisions events as well as out of time events as cosmics and single beam events populate the plot, in order to evaluate the DSP reconstruction performance on a wide time window. Nevertheless, the 90% of the pulses are in the time range [-5,5]ns. Relative difference between the online and the offline cell Energy reconstruction, for a whole partition in TileCal, as a function of the cell time showing the bias in the energy reconstruction due to the cell phase variations. The bias can be corrected applying a second order correction using the phase of the pulse. In the time range [-10,10] ns the average difference between the offline and online reconstruction is within 1%.
Contact: Alberto Valero
Reference: ATLAS-PLOT-TILECAL-2010-003 .
Date: 07 May 2010
900GeV_Ediff_vs_tdsp.png
eps version of the figure
Performance of the ROD/DSP Optimal Filtering non iterative reconstruction with 2011 collision data at ps=7 TeV (run number 182284, JetEtMiss Stream). In time and out of time collision events populate the plot in order to evaluate the DSP reconstruction performance on a wide time window. Most of the pulses are in the time range [-5,5] ns. The red solid circles show the relative difference between the online (EDSP ) and the offline (EOFLI) cell energy reconstruction, for a whole partition in TileCal, as a function of the cell time (TDSP ). The bias due to the phase variation can be reduced applying a correction using the time of the pulse. The difference between the online (EDSP ) and the offline (EOFLI) cell energy reconstruction after the correction is shown with blu solid square markers. The vertical error bars for both blu and red markers correspond to the RMS of the distributions. In the time range [-10,10] ns the average difference between the offline and online reconstruction, after the correction, is smaller than 1%. This correction is applied for pulses with amplitude larger than 160 MeV.
Contact: ???
Reference: ???
Date: ???
collisions.182284_7TeV.ParabolicProfile_FinalUpdate_140512.png
eps version of the figure
Difference between the signal amplitude calculated on Collision data (run number 156682) with the Non Iterative Optimal Filtering Algorithm online, using the Digital Signal Processor (EDSP), and offline (EOFL NI). The signal amplitudes are measured in MeV and the data shown correspond to the HG range. The maximum expected difference due to fixed point arithmetic is, in this case, proportional to the calibration constant ADC -> MeV and therefore changes from channel to channel. The red dashed lines indicate the maximum expected precision for standard functioning channels and contain 99% of the channels. The blue lines indicate the expected precision for the highest calibration constant.
Contact: Alberto Valero
Reference: ATLAS-PLOT-TILECAL-2010-006
Date: 28 Jul 2010
Ediff_HG_phy.png
eps version of the figure
Difference between the signal amplitude calculated on Collision data (run number 156682) with the Non Iterative Optimal Filtering Algorithm online, using the Digital Signal Processor (EDSP), and offline (EOFL NI). The signal amplitudes are measured in MeV and the data shown correspond to the LG range. The signal registered in this range are almost completely due to signals generated from the Minimum Bias Scintillators. The maximum expected difference due to fixed point arithmetic is, in this case, proportional to the calibration constant ADC -> MeV and therefore changes from channel to channel.
Contact: Alberto Valero
Reference: ATLAS-PLOT-TILECAL-2010-006
Date: 28 Jul 2010
Ediff_LG_phy.png
eps version of the figure
Ratio between the signal amplitude calculated on Collision data (run number 156682) with the Non Iterative Optimal Filtering Algorithm online (EDSP) and the fit Algorithm (EFIT). The signal amplitudes are measured in MeV and the data shown correspond to the HG range. Only pulses with EFIT>300 MeV are considered. In order to disentangle the precision due to timing from the other effects only pulses in the phase range [-1,1] ns are considered. The asymmetry with respect to 1 is due to the parabolic deviation produced by pulses from -1 ns to 1 ns. This deviation can be corrected offline applying a second order correction using the phase of the pulse.The difference between the amplitude reconstructed with the two algorithms, for well synchronized pulses, is less than 0.3%.
Contact: Alberto Valero
Reference: ATLAS-PLOT-TILECAL-2010-006
Date: 28 Jul 2010
EdspEfit_HG.png
eps version of the figure
Ratio between the signal amplitude calculated on Collision data (run number 156682) with the Non-Iterative Optimal Filtering Algorithm online (EDSP) and the fit Algorithm (EFIT). The signal amplitudes are measured in MeV and the data shown correspond to the LG range. In order to disentangle the precision due to timing from the other effects only pulses in the phase range [-1,1] ns are considered. The asymmetry with respect to 1 is due to the parabolic deviation produced by pulses from -1 ns to 1 ns. This deviation can be corrected offline applying a second order correction using the phase of the pulse. The difference between the amplitude reconstructed with the two algorithms, for well synchronized pulses, is less than 0.4%.
Contact: Alberto Valero
Reference: ATLAS-PLOT-TILECAL-2010-006
Date: 28 Jul 2010
EdspEfit_LG.png
eps version of the figure
Ratio between the signal amplitude calculated on Collision data (run number 156682) with the Non Iterative Optimal Filtering Algorithm online (EDSP) and the fit Algorithm (EFIT). Only pulses in the phase range [-1,1] ns are considered.
Contact: Alberto Valero
Reference: ATLAS-PLOT-TILECAL-2010-006
Date: 28 Jul 2010
EdspEfit_BothGains.png
eps version of the figure
The plots show the time as a function of the energy reconstructed at the channel level in the Tile Calorimeter using two different methods applied to 2011 collision data. A run with train of bunches crossing every 50 ns is used. A cut of 200 MeV is used to reduce the contribution of the electronic noise. The offline Optimal Filtering iterative algorithm is shown on the top plot. The iterative method (used for cosmic rays and during commissioning for detector timing studies) is able to reconstruct signals generated by a different bunch crossings. (peaks at +- 50 ns) The Optimal Filtering non-iterative algorithm as implemented in the DSP (bottom) use a well defined signal phase for each channel and is not very sensitive to the presence of signals from other BC (out of time Minimum Bias pileup noise).
Contact: Alberto Valero
Reference: ATLAS-PLOT-TILECAL-2011-006
Date: 30 May 2011


eps version of the figure
eps version of the figure
Quality factor as a function of reconstructed amplitude in Data 2011. The data consists of runs: 177531, 177539, 177540, 177593, 177682 from March 2011 (<μ>=3.3). This data was recorded while the LHC was running with only 2 bunches per beam separared by at least 2.5 μs. The x-axis shows the amplitude in ADC counts before cell-dependent calibration constants are applied. The calibration factor is approximately 12 MeV per ADC count. See also Approved Tile Calorimeter Plots. (Link to CDS record, plots) (Link to CDS record, poster) (Link to CDS record, proceedings)
Contact: Christophe Clement, Pawel Klimek
Reference: ATL-COM-TILECAL-2011-038 Beware this public plot has been registered in the wrong catalogue
Date: 11 Oct 2011
QFvsAopt_Data_hi.png
eps version of the figure
Comparison of quality factor distribution in Data 2011 and in TileCal pulse simulator with non-ideal pulse shape for pulses with amplitudes above 200 ADC counts. The data consists of runs: 177531, 177539, 177540, 177593, 177682 from March 2011 (<μ>=3.3). This data was recorded while the LHC was running with only 2 bunches per beam separated by at least 2.5 μs. The x-axis shows the amplitude in ADC counts before cell-dependent calibration constants are applied. The calibration factor is approximately 12 MeV per ADC count. Plot on top has y axis in linear scale while plot on bottom has y axis in logarithmic scale. See also Approved Tile Calorimeter Plots. (Link to CDS record, plots) (Link to CDS record, poster) (Link to CDS record, proceedings)
Contact: Christophe Clement, Pawel Klimek
Reference: ATL-COM-TILECAL-2011-038 Beware this public plot has been registered in the wrong catalogue
Date: 11 Oct 2011
QF_Data_hi.png
eps version of the figure QF_Data_hi_LogY.png
eps version of the figure
Distribution of reconstructed amplitude [ADC counts] in Data 2011 ZeroBias stream, in absence of out-of-time pile-up. The data consists of runs: 177531, 177539, 177540, 177593, 177682 from March 2011 (<μ>=3.3). This data was recorded while the LHC was running with only 2 bunches per beam separared by at least 2.5 μs. The x-axis shows the amplitude in ADC counts before cell-dependent calibration constants are applied. The calibration factor is approximately 12 MeV per ADC count. See also Approved Tile Calorimeter Plots. (Link to CDS record, plots) (Link to CDS record, poster) (Link to CDS record, proceedings)
Contact: Christophe Clement, Pawel Klimek
Reference: ATL-COM-TILECAL-2011-038 Beware this public plot has been registered in the wrong catalogue
Date: 11 Oct 2011
Aopt_Data_ZeroBias.png
eps version of the figure
Comparison between the non-iterative (EOFLNI) and iterative (EOFLI) offline Optimal Filtering reconstruction for high pT muons in 2010 collision data at ps=7 TeV. To validate the performances of the two methods for very low signal, data caracterized by low pile-up, as the 2010 ones, are used and a clean sample of muons is selected requiring: a muon pT >20 GeV, a minimum cell track path length of 100 mm and an angular distance between the muon track extrapolated at the TileCal layer and the center of the cell ? <0.048 and ? <0.048. The plot shows the cell energy difference measured with the two methods as a function of the EOFLI . The most probable energy measured in the TileCal cells for the selected muons ranges from 400 MeV to about 1 GeV depending on the cell size. For energy deposits larger than 200 MeV the difference between the two method is smaller than 50 MeV for the majority of events, and the mean of the distribution is smaller than 10 MeV. The increasing of the spread in the low energy region is due to the bias explained in previous figure.
Contact: =Mr X
Reference: Unknown
Date: XX-YY-ZZZZ
EneDiffProf2_Ene_TOT_comp_fin0048detadphi_140512.png
eps version of the figure

Time Reconstruction

Correlation between time reconstructed online (DSP) and Offline with iterative algorithm (OFL I) for collisions data (7 TeV) for Tile Calorimeter channels with energy reconstructed offline with iterations above 300 MeV. The plot shows the [-30,30] ns range. It should be noted that 95% of the pulses are within the [-5,5] ns range. (Link to CDS record). Final_Tofli_vs_Tdsp_zoom.png
eps version of the figure
Absolute difference between time reconstructed online (DSP) and offline without iterations (OFL NI) as a function of time reconstructed offline for collisions data (7 TeV) for Tile Calorimeter channels with energy reconstructed by the DSP above 50 MeV. Since the same non iterative algorithm is used online and offline the expected difference is due to fixed point arithmetic and Look-up-Table (LUT) used in the DSP. The LUT effect is enhanced for larger phases and it is compatible with an approved plot for pseudo-data. (Link to CDS record). Final_Diff_ToflniTdsp_vs_Toflni.png
eps version of the figure
Absolute difference between time reconstructed online (DSP) and offline without iterations (OFL NI) as a function of energy reconstructed offline for collisions data (7 TeV) for Tile Calorimeter channels with energy reconstructed by the DSP above 50 MeV. Since the same non iterative algorithm is used online and offline the expected difference is due to fixed point arithmetic and Look-up-Table (LUT) used in the DSP. (Link to CDS record). Final_Diff_ToflniTdsp_vs_Eoflni.png
eps version of the figure
Correlation between time reconstructed online (DSP) and offline without iterations (OFL NI) for collisions data (7 TeV) for Tile Calorimeter channels with energy reconstructed by the DSP above 50 MeV. Since the same non-iterative algorithm is used online and offline the expected difference is due to fixed point arithmetic and Look-up-Table (LUT) used in the DSP. (Link to CDS record). Final_Toflni_vs_Tdsp.png
eps version of the figure
Histogram of the difference between time reconstructed online (DSP) and offline without iterations (OFL NI) for collisions data (7 TeV) for Tile Calorimeter channels with energy reconstructed by the DSP above 50 MeV. Since the same non-iterative algorithm is used online and offline the expected difference is due to fixed point arithmetic and Look-up-Table (LUT) used in the DSP. (Link to CDS record). Final_Diff_Toflni_Tdsp.png
eps version of the figure
Distribution of the time reconstructed at the channel level (2011 collision data with BCs of 50 ns) using different reconstruction algorithms. The offline OF iterative algorithm (OFL I) can reconstructs signals from events of MB interactions in the previous and following BCs. The non-iterative online (DSP) and offline (OFL NI) algorithms have a more limited time range around the BC of interest. For signals out of range the reconstructed time saturates to the maximum possible value:+/- 64 ns for DSP and +/- 75 ns for Offline. Only channels with an energy reconstructed with the offline OF iterative method above 300 MeV are considered for all the three methods. Moreover, negative energy pulses are not considered in the offline non-iterative method in order to eliminate any bias due to the different treatment of negative amplitudes. (Link to CDS record). Final_Diff_Toflni_Tdsp.png
eps version of the figure


Major updates:
-- PawelKlimek - 2016-08-23

Responsible: PawelKlimek
Subject: public

Topic attachments
I Attachment History Action Size Date Who Comment
Unknown file formateps 900GeV_Ediff_vs_tdsp.eps r1 manage 120.4 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng 900GeV_Ediff_vs_tdsp.png r1 manage 25.0 K 2016-08-23 - 15:55 PawelKlimek  
Unknown file formateps Aopt_Data_ZeroBias.eps r1 manage 13.3 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng Aopt_Data_ZeroBias.png r1 manage 99.3 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng Correlation_wienerFilter_OF2.png r1 manage 302.7 K 2019-03-06 - 14:03 DayaneOliveiraGoncalves  
Unknown file formateps Ediff_HG_phy.eps r1 manage 1955.2 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng Ediff_HG_phy.png r1 manage 26.8 K 2016-08-23 - 15:55 PawelKlimek  
Unknown file formateps Ediff_LG_phy.eps r1 manage 1955.2 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng Ediff_LG_phy.png r1 manage 68.0 K 2016-08-23 - 15:55 PawelKlimek  
Unknown file formateps EdspEfit_BothGains.eps r1 manage 1955.2 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng EdspEfit_BothGains.png r1 manage 28.4 K 2016-08-23 - 15:55 PawelKlimek  
Unknown file formateps EdspEfit_HG.eps r1 manage 1955.2 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng EdspEfit_HG.png r1 manage 28.0 K 2016-08-23 - 15:55 PawelKlimek  
Unknown file formateps EdspEfit_LG.eps r1 manage 1955.2 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng EdspEfit_LG.png r1 manage 37.9 K 2016-08-23 - 15:55 PawelKlimek  
Unknown file formateps EdspVStdsp_pileup.eps r1 manage 53.1 K 2016-08-23 - 16:06 PawelKlimek  
PNGpng EdspVStdsp_pileup.png r1 manage 21.7 K 2016-08-23 - 16:06 PawelKlimek  
PNGpng EneDiffProf2_Ene_TOT_comp_fin0048detadphi_140512.png r1 manage 20.7 K 2016-08-23 - 15:55 PawelKlimek  
Unknown file formateps EofliVStofli_pileup.eps r1 manage 58.8 K 2016-08-23 - 15:57 PawelKlimek  
PNGpng EofliVStofli_pileup.png r1 manage 22.5 K 2016-08-23 - 15:57 PawelKlimek  
Unknown file formateps Final_Diff_ToflniTdsp_vs_Eoflni.eps r1 manage 113.0 K 2016-08-23 - 15:57 PawelKlimek  
PNGpng Final_Diff_ToflniTdsp_vs_Eoflni.png r1 manage 30.6 K 2016-08-23 - 15:57 PawelKlimek  
Unknown file formateps Final_Diff_ToflniTdsp_vs_Toflni.eps r1 manage 144.6 K 2016-08-23 - 15:57 PawelKlimek  
PNGpng Final_Diff_ToflniTdsp_vs_Toflni.png r1 manage 36.5 K 2016-08-23 - 15:57 PawelKlimek  
Unknown file formateps Final_Diff_Toflni_Tdsp.eps r1 manage 122.9 K 2016-08-23 - 15:57 PawelKlimek  
PNGpng Final_Diff_Toflni_Tdsp.png r1 manage 28.3 K 2016-08-23 - 15:57 PawelKlimek  
Unknown file formateps Final_Tofli_vs_Tdsp_zoom.eps r1 manage 133.0 K 2016-08-23 - 15:57 PawelKlimek  
PNGpng Final_Tofli_vs_Tdsp_zoom.png r1 manage 31.9 K 2016-08-23 - 15:57 PawelKlimek  
Unknown file formateps Final_Toflni_vs_Tdsp.eps r1 manage 128.1 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng Final_Toflni_vs_Tdsp.png r1 manage 32.4 K 2016-08-23 - 15:58 PawelKlimek  
Unknown file formateps MFOF2col2010.eps r1 manage 16.1 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng MFOF2col2010.png r1 manage 17.1 K 2016-08-23 - 15:58 PawelKlimek  
Unknown file formateps MFOF2col2012.eps r1 manage 17.6 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng MFOF2col2012.png r1 manage 16.9 K 2016-08-23 - 15:58 PawelKlimek  
Unknown file formateps MFOF2col2012_2.eps r1 manage 15.2 K 2016-08-23 - 16:02 PawelKlimek  
PNGpng MFOF2col2012_2.png r1 manage 16.6 K 2016-08-23 - 16:02 PawelKlimek  
PDFpdf OF2vsCOF_Ecorr_25nsMu11.pdf r1 manage 2066.9 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng OF2vsCOF_Ecorr_25nsMu11.png r1 manage 30.6 K 2016-08-23 - 15:58 PawelKlimek  
Unknown file formateps OF2vsCOF_Ehist_25nsMu11.eps r1 manage 14.8 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng OF2vsCOF_Ehist_25nsMu11.png r1 manage 16.1 K 2016-08-23 - 15:58 PawelKlimek  
Unknown file formateps OF2vsCOF_QFcorr_25nsMu11_v2.eps r1 manage 5017.4 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng OF2vsCOF_QFcorr_25nsMu11_v2.png r1 manage 47.3 K 2016-08-23 - 15:58 PawelKlimek  
Unknown file formateps QF_Data_hi.eps r1 manage 11.9 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng QF_Data_hi.png r1 manage 106.7 K 2016-08-23 - 15:58 PawelKlimek  
Unknown file formateps QF_Data_hi_LogY.eps r1 manage 12.1 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng QF_Data_hi_LogY.png r1 manage 101.4 K 2016-08-23 - 15:58 PawelKlimek  
Unknown file formateps QFvsAopt_Data_hi.eps r1 manage 2156.0 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng QFvsAopt_Data_hi.png r1 manage 535.6 K 2016-08-23 - 15:58 PawelKlimek  
Unknown file formateps TimeReco_pileup.eps r1 manage 21.4 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng TimeReco_pileup.png r1 manage 33.3 K 2016-08-23 - 15:58 PawelKlimek  
PNGpng collisions.182284_7TeV.ParabolicProfile_FinalUpdate_140512.png r1 manage 17.8 K 2016-08-23 - 15:55 PawelKlimek  
Unknown file formateps corrCol10.eps r1 manage 71.2 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng corrCol10.png r1 manage 15.3 K 2016-08-23 - 15:55 PawelKlimek  
Unknown file formateps corrCol12.eps r1 manage 144.3 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng corrCol12.png r1 manage 20.9 K 2016-08-23 - 15:55 PawelKlimek  
PNGpng dist_WFxOF2.png r1 manage 238.7 K 2019-03-06 - 14:03 DayaneOliveiraGoncalves  
PDFpdf hist1_review3_2.pdf r1 manage 15.7 K 2019-03-06 - 13:46 DayaneOliveiraGoncalves  
PDFpdf hist2_review3.pdf r1 manage 16.0 K 2019-03-06 - 13:46 DayaneOliveiraGoncalves  
PDFpdf hist3_rev3.pdf r1 manage 47.0 K 2019-03-06 - 13:46 DayaneOliveiraGoncalves  
PNGpng logdist_WFxOF2.png r1 manage 196.7 K 2019-03-06 - 14:03 DayaneOliveiraGoncalves  
Edit | Attach | Watch | Print version | History: r3 < r2 < r1 | Backlinks | Raw View | WYSIWYG | More topic actions
Topic revision: r3 - 2019-03-06 - DayaneOliveiraGoncalves
 
    • Cern Search Icon Cern Search
    • TWiki Search Icon TWiki Search
    • Google Search Icon Google Search

    Atlas All webs login

This site is powered by the TWiki collaboration platform Powered by PerlCopyright & 2008-2019 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding TWiki? Send feedback