This page lists the public plots illustrating signal reconstruction.
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Energy Reconstruction  
The figure provides a energy reconstruction comparison of three methods: OF2, COF and COFp (COF method using the pedestal estimator). The black histogram represents the OF2 performance on energy estimation. The red histogram shows the COF performance. The green energy reconstuction histogram illustrate the COFp performance. The readout is the EBC21.A15L. The data used is a zero bias data stream with approximately 90 collision per bunch crossing and 25$ns$ of bunch spacing (high pileup scenario) in run 364485. The selected LumiBlocks were from 720 to 738, where 88.7 $\leq {<}\mu {>} \leq$ 90.3. Contact: Juan Marin Reference: ATLCOMTILECAL2020038 Date: 18 August 2020 

Pedestal estimation performance on channel EBC21.A15L (red line). The calibrated value (black line) is 55.1 ADC counts. After a small number of events, the pedestal estimation achieves the calibrated value, providing a good pedestal information to COF equations. In this run, the pedestal value used in the COF method was the stored in DB, which is a miscalibrated value. The data used is a zero bias data stream with approximately 90 collision per bunch crossing and 25$ns$ of bunch spacing (high pileup scenario) in run 364485. The selected LumiBlocks were from 720 to 738, where 88.7 $\leq {<}\mu {>} \leq$ 90.3. Contact: Juan Marin Reference: ATLCOMTILECAL2020038 Date: 18 August 2020 

Contour map of the TileCal trigger towers built in the event number 13980821248. The plot shows the reconstructed energy performed by COF algorithm, considering the energy estimation in the outoftime pileup. In $\eta \times \phi = 1.5 \times 0.3$ the energy reconstructed by the COF method is approximately 6 GeV. The map presents a low background noise contribution, since the COF method is designed to handle with high pileup incidence. The data used are from zero bias stream with approximately 90 collisions per bunch crossing and 25 ns of bunch spacing (high pileup scenario) from run 364485. Contact: Juan Marin Reference: ATLCOMTILECAL2020038 Date: 18 August 2020 

Contour map of the TileCal trigger towers built in the event number 13980821248. The plot shows the reconstructed energy performed by OF2 algorithm. In $\eta \times \phi = 1.5 \times 0.3$ the energy reconstructed by the OF2 method is approximately 3 GeV. The negative values on the map is due to background energy estimate from a high pileup collision data. The data used are from zero bias stream with approximately 90 collisions per bunch crossing and 25ns of bunch spacing (high pileup scenario) from run 364485. Contact: Juan Marin Reference: ATLCOMTILECAL2020038 Date: 18 August 2020 

The plot shows pulses read from four channels of the TileCal of a single event. The red and green pulse are contaminated by the outoftime pileup effect from adjacent collisions. The yellow and blue pulses does not present any energy deposition and hold on the channel pedestal value. These channels belongs to the $\eta \times \phi = 1.5 \times 0.3$ region. The data used are from zero bias stream with approximately 90 collisions per bunch crossing and 25 ns of bunch spacing (high pileup scenario) from run 364485. Contact: Juan Marin Reference: ATLCOMTILECAL2020038 Date: 18 August 2020 

Heat map of the TileCal LBA partition, comparing the Optimal Filter (OF2) and the Constrained Optimal Filter (COF) performance, considering readout channels from all modules. The heatmap colors refer to standard deviation percentage differences among methods. A positive value indicates that in a specific pair module/channel the COF algorithm has a smaller standard deviation than the OF2 method and negative values, otherwise. The white points and straightlines represent channels that were in the bad channel list or are noninstrumented channels. The data used is a zero bias data stream with approximately 90 collision per bunch crossing and 25$ns$ of bunch spacing (high pileup scenario) in run 364485. The selected LumiBlocks were from 720 to 738, where 88.7 ≤ <μ> ≤ 90.3. Contact: Juan Marin Reference: ATLCOMTILECAL2020009 Date: 17 February 2020  
The figure shows an energy reconstruction comparison, using a zero bias data stream with approximately 90 collision per bunch crossing and 25ns of bunch spacing (high pileup scenario) in run 364485. The selected LumiBlocks were from 720 to 738, where 88.7 ≤ <μ> ≤ 90.3. The analysis considers only the A side of the TileCal Long Barrel. The two energy reconstruction methods compared are: the Optimal Filter 2 (OF2, the black line) and the Constrained Optimal Filter (COF, in yellow). For the second method, just the energy estimates for the central bunch crossing are displayed. The COF estimations produces smaller standard deviation, a smaller negative tail and a higher mean with respect to OF2. Contact: Juan Marin Reference: ATLCOMTILECAL2020009 Date: 17 February 2020  
Energy distribution of TileCal EBA E4 cells reconstructed by the Wiener Filter, the Constrained Optimal Filter (COF) and the Optimal Filter (OF2), using 2018 ATLAS ZeroBias data at √s = 13 TeV with 25 ns of bunch spacing in run 355544. A total of 64 modules in phi are used while known pathological channels were excluded. The selected LumiBlocks were from 542 to 561, where 29.7 ≤ <μ> ≤ 30.3. In such scenario of occupancy, the pileup in E4 cells becomes embedded in the background for the majority of the events. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure 
Energy distribution of TileCal EBA E4 cells reconstructed by the Wiener Filter, the Constrained Optimal Filter (COF) and the Optimal Filter (OF2), using 2018 ATLAS ZeroBias data at √s = 13 TeV with 25 ns of bunch spacing in run 355544. A total of 64 modules in phi are used while known pathological channels were excluded. The selected LumiBlocks were from 125 to 144, where 48.7 ≤ <μ> ≤ 51.3. In such scenario of occupancy, the pileup in E4 cells becomes embedded in the background for the majority of the events. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure 
Energy distribution of TileCal EBA E4 cells reconstructed by the Wiener Filter, the Constrained Optimal Filter (COF) and the Optimal Filter (OF2), using 2018 ATLAS ZeroBias data at √s = 13 TeV with 25 ns of bunch spacing in run 364485. A total of 64 modules in phi are used while known pathological channels were excluded. The selected LumiBlocks were from 719 to 734, where 89.5 ≤ <μ> ≤ 90.5. In such scenario of occupancy, the pileup in E4 cells becomes embedded in the background for the majority of the events. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure 
This plot shows the evolution of the RMS of the estimated energy distribution for different <μ> values, where low RMS values are desired. Improvements can be observed in terms of energy estimation for high luminosity scenario by using the Wiener Filter with respect to Optimal Filter (OF2) and Constrained Optimal Filter (COF) algorithms in the Tilecal E4 cells. A set of Wiener Filter weights designed for <μ> = 40 was used to reconstruct events with luminosity around <μ> = 30 and <μ> = 50 and another set of weights designed for <μ> = 90 was used to reconstruct events with luminosity around <μ> = 90. The dataset is a 2018 ATLAS ZeroBias stream at √s = 13 TeV with 25 ns of bunch spacing in runs 355544 and 364485. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure 
This plot shows the evolution of the mean of the estimated energy distribution for different <μ> values, where mean values close to zero are desired. Improvements can be observed in terms of energy estimation for high luminosity scenario by using the Wiener Filter with respect to Optimal Filter (OF2) and Constrained Optimal Filter (COF) algorithms in the Tilecal E4 cells. A set of Wiener Filter weights designed for <μ> = 40 was used to reconstruct events with luminosity around <μ> = 30 and <μ> = 50 and another set of weights designed for <μ> = 90 was used to reconstruct events with luminosity around <μ> = 90. The dataset is a 2018 ATLAS ZeroBias stream at √s = 13 TeV with 25 ns of bunch spacing in runs 355544 and 364485. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure 
This plot shows the percent relative difference of the standard deviation of the estimation error between Wiener Filter and the Constrained Optimal Filter (COF) reference, where warm colors represent higher STD values and cool colors represent smaller values. The most significant improvements by using the Wiener Filter with respect to the COF algorithm are visible for channels E3 (ch 0), E4 (ch 1) and MBTS (ch 4 and 12 in some modules) in the EBA partition. The dataset is a 2018 ATLAS ZeroBias stream at √s = 13 TeV with 25 ns of bunch spacing in run 355544. The selected LumiBlocks were from 304 to 370, where 38.0 ≤ <μ> ≤ 42.0. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure 
This plot shows the percent relative difference of the standard deviation of the estimation error between Wiener Filter and the Constrained Optimal Filter (COF) reference, where warm colors represent higher STD values and cool colors represent smaller values. The most significant improvements by using the Wiener Filter with respect to the COF algorithm are visible for channels E3 (ch 0), E4 (ch 1), E4' and MBTS (ch 4 and 12 in some modules) in the EBC partition. The dataset is a 2018 ATLAS ZeroBias stream at √s = 13 TeV with 25 ns of bunch spacing in run 355544. The selected LumiBlocks were from 304 to 370, where 38.0 ≤ <μ> ≤ 42.0. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure 
This plot shows the percent relative difference of the standard deviation of the estimation error between Wiener Filter and the Constrained Optimal Filter (COF) reference, where warm colors represent higher STD values and cool colors represent smaller values. The most significant improvements by using the Wiener Filter with respect to the COF algorithm are visible for channels E3 (ch 0), E4 (ch 1) and MBTS (ch 4 and 12 in some modules) in the EBA partition. The dataset is a 2018 ATLAS ZeroBias stream at √s = 13 TeV with 25 ns of bunch spacing in run 364485. The selected LumiBlocks were from 719 to 734, where 89.5 ≤ <μ> ≤ 90.5. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure 
This plot shows the percent relative difference of the standard deviation of the estimation error between Wiener Filter and the Constrained Optimal Filter (COF) reference, where warm colors represent higher STD values and cool colors represent smaller values. The most significant improvements by using the Wiener Filter with respect to the COF algorithm are visible for channels E3 (ch 0), E4 (ch 1), E4' and MBTS (ch 4 and 12 in some modules) in the EBC partition. The dataset is a 2018 ATLAS ZeroBias stream at √s = 13 TeV with 25 ns of bunch spacing in run 364485. The selected LumiBlocks were from 719 to 734, where 89.5 ≤ <μ> ≤ 90.5. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure 
This plot shows the mean difference of the estimation error among Wiener Filter, Constrained Optimal Filter (COF) and Optimal Filter (OF2) reference. Positive values represent greater mean values than OF2 and negative values represent lower values. The most significant improvements by using the Wiener Filter with respect to the COF and OF2 algorithms are visible for channels E3 (ch 0) and E4 (ch 1) in the module EBA01. The dataset is a 2018 ATLAS ZeroBias stream at √s = 13 TeV with 25 ns of bunch spacing in run 364485. The selected LumiBlocks were from 719 to 734, where 89.5 ≤ <μ> ≤ 90.5. Cross hatched areas comprise noninstrumented channels. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure 
This plot shows the percent relative error of standard deviation of the estimation error among Wiener Filter, Constrained Optimal Filter (COF) and Optimal Filter (OF2) reference. Positive values represent greater STD values than OF2 and negative values represent lower values. The most significant improvements by using the Wiener Filter with respect to the COF and OF2 algorithms are visible for channels E3 (ch 0) and E4 (ch 1) in the module EBA01. The dataset is a 2018 ATLAS ZeroBias stream at √s = 13 TeV with 25 ns of bunch spacing in run 364485. The selected LumiBlocks were from 719 to 734, where 89.5 ≤ <μ> ≤ 90.5. Cross hatched areas comprise noninstrumented channels. Contact: Guilherme Goncalves Reference: ATLCOMTILECAL2020006 Date: 30 January 2020  eps version of the figure

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 protonproton 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  
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 protonproton 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  
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 protonproton 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  
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 readout. In this plot only the central sample reconstruction is shown. Contact: Bernardo Peralva Reference: link to CDS . Date: 09 March 2015  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  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  %ATT ACHURL%/OF2vsCOF_QFcorr_25nsMu11_v2.eps 
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 OutOfTime 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: ATLASPLOTTILECAL2013010 . Date: 24 September 2013  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 OutOfTime signals. As energy increases the response of the two algorithms are strongly correlated. Contact: Bernardo Peralva Reference: ATLASPLOTTILECAL2013010 . Date: 24 September 2013  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 OutOfTime signals. Contact: Bernardo Peralva Reference: ATLASPLOTTILECAL2013010 . Date: 24 September 2013  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: ATLASPLOTTILECAL2013010 . Date: 24 September 2013  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 OutOfTime (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: ATLASPLOTTILECAL2013010 . Date: 24 September 2013  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: ATLASPLOTTILECAL2010003 . Date: 07 May 2010  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: ???  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: ATLASPLOTTILECAL2010006 Date: 28 Jul 2010  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: ATLASPLOTTILECAL2010006 Date: 28 Jul 2010  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: ATLASPLOTTILECAL2010006 Date: 28 Jul 2010  eps version of the figure 
Ratio between the signal amplitude calculated on Collision data (run number 156682) with the NonIterative 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: ATLASPLOTTILECAL2010006 Date: 28 Jul 2010  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: ATLASPLOTTILECAL2010006 Date: 28 Jul 2010  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 noniterative 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: ATLASPLOTTILECAL2011006 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 xaxis shows the amplitude in ADC counts before celldependent 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: ATLCOMTILECAL2011038 Beware this public plot has been registered in the wrong catalogue Date: 11 Oct 2011  eps version of the figure 
Comparison of quality factor distribution in Data 2011 and in TileCal pulse simulator with nonideal 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 xaxis shows the amplitude in ADC counts before celldependent 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: ATLCOMTILECAL2011038 Beware this public plot has been registered in the wrong catalogue Date: 11 Oct 2011  eps version of the figure eps version of the figure 
Distribution of reconstructed amplitude [ADC counts] in Data 2011 ZeroBias stream, in absence of outoftime pileup. 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 xaxis shows the amplitude in ADC counts before celldependent 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: ATLCOMTILECAL2011038 Beware this public plot has been registered in the wrong catalogue Date: 11 Oct 2011  eps version of the figure 
Comparison between the noniterative (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 pileup, 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: XXYYZZZZ  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).  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 LookupTable (LUT) used in the DSP. The LUT effect is enhanced for larger phases and it is compatible with an approved plot for pseudodata. (Link to CDS record).  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 LookupTable (LUT) used in the DSP. (Link to CDS record).  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 noniterative algorithm is used online and offline the expected difference is due to fixed point arithmetic and LookupTable (LUT) used in the DSP. (Link to CDS record).  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 noniterative algorithm is used online and offline the expected difference is due to fixed point arithmetic and LookupTable (LUT) used in the DSP. (Link to CDS record).  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 noniterative 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 noniterative method in order to eliminate any bias due to the different treatment of negative amplitudes. (Link to CDS record).  eps version of the figure 
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 PawelKlimek  20160823
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