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| CMS-PRF-22-001 ; CERN-EP-2023-093 | ||
| Performance of the local reconstruction algorithms for the CMS hadron calorimeter with Run 2 data | ||
| CMS Collaboration | ||
| 17 June 2023 | ||
| JINST 18 (2023) P11017 | ||
| Abstract: A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015--2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared. | ||
| Links: e-print arXiv:2306.10355 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
A cross sectional view of the HB and HE in the $ r-z $ plane. The i.e.,ta coordinates are labeled, and the depth segmentation is coded in different colors. The number of depths of the HE was increased before the 2018 data-taking period. The location of the front-end electronics (labeled ``FEE'') is also shown. |
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Figure 2:
Average pulse shape for high-energy depositions for a channel in the HE. The red solid line is the pulse shape used in reconstruction algorithms with 1 ns granularity. The yellow filled histogram is constructed from the red shape by integrating over each 25 ns TS. The SOI corresponds to the TS from 75 to 100 ns. |
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Figure 3:
The average value of the pedestal, as a function of the integrated luminosity, since the start of 2018 data taking for the HB (left) and HE (right). Each horizontal line denotes the time range where the corresponding pedestal measurement is used in the energy reconstruction, and the error bars indicate the average standard deviation of the pedestal. |
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Figure 3-a:
The average value of the pedestal, as a function of the integrated luminosity, since the start of 2018 data taking for the HB. Each horizontal line denotes the time range where the corresponding pedestal measurement is used in the energy reconstruction, and the error bars indicate the average standard deviation of the pedestal. |
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Figure 3-b:
The average value of the pedestal, as a function of the integrated luminosity, since the start of 2018 data taking for the HE. Each horizontal line denotes the time range where the corresponding pedestal measurement is used in the energy reconstruction, and the error bars indicate the average standard deviation of the pedestal. |
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Figure 4:
An illustration of the M3 algorithm. Only three TSs from $ \text{SOI}{-1} $ to $ \text{SOI}{+1} $, indicated with dash lines, are used in the reconstruction. Pulse-shape templates for $ \text{SOI}{-1} $, SOI, and $ \text{SOI}{+1} $ are shown in blue, yellow, and pink, respectively. The gray shading shows the baseline. |
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Figure 5:
Representative fit results from MAHI for high-energy (left) and low-energy (right) pulses, in the HB (upper) and the HE (lower). The recorded pedestal-subtracted charge in the QIE (converted to units of energy) is given by the points, while the filled histograms represent the fitted values for the various pulse shapes. The sum of the fitted energy for each pulse, labeled by its position relative to the SOI, is presented in the legend. The combined uncertainty from the pedestal, photostatistics, and QIE quantization is shown by the hatched areas. |
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Figure 5-a:
Representative fit results from MAHI for high-energy pulses, in the HB. The recorded pedestal-subtracted charge in the QIE (converted to units of energy) is given by the points, while the filled histograms represent the fitted values for the various pulse shapes. The sum of the fitted energy for each pulse, labeled by its position relative to the SOI, is presented in the legend. The combined uncertainty from the pedestal, photostatistics, and QIE quantization is shown by the hatched areas. |
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Figure 5-b:
Representative fit results from MAHI for low-energy pulses, in the HB. The recorded pedestal-subtracted charge in the QIE (converted to units of energy) is given by the points, while the filled histograms represent the fitted values for the various pulse shapes. The sum of the fitted energy for each pulse, labeled by its position relative to the SOI, is presented in the legend. The combined uncertainty from the pedestal, photostatistics, and QIE quantization is shown by the hatched areas. |
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Figure 5-c:
Representative fit results from MAHI for high-energy pulses, in the HE. The recorded pedestal-subtracted charge in the QIE (converted to units of energy) is given by the points, while the filled histograms represent the fitted values for the various pulse shapes. The sum of the fitted energy for each pulse, labeled by its position relative to the SOI, is presented in the legend. The combined uncertainty from the pedestal, photostatistics, and QIE quantization is shown by the hatched areas. |
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Figure 5-d:
Representative fit results from MAHI for low-energy pulses, in the HE. The recorded pedestal-subtracted charge in the QIE (converted to units of energy) is given by the points, while the filled histograms represent the fitted values for the various pulse shapes. The sum of the fitted energy for each pulse, labeled by its position relative to the SOI, is presented in the legend. The combined uncertainty from the pedestal, photostatistics, and QIE quantization is shown by the hatched areas. |
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Figure 6:
The HCAL energy response of M0, M2, and MAHI, measured in an electron/photon-triggered data set using isolated tracks with 20 $ < p_\text{track} < $ 30 GeV and either $ |\eta_\text{track}| < $ 1.2 (left) or 1.5 $ < |\eta_\text{track}| < $ 2.3 (right). The vertical bars show the statistical uncertainty in the number of tracks in each bin. The measured energy resolution for each method in this sample is comparable. |
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Figure 6-a:
The HCAL energy response of M0, M2, and MAHI, measured in an electron/photon-triggered data set using isolated tracks with 20 $ < p_\text{track} < $ 30 GeV and $ |\eta_\text{track}| < $ 1.2. The vertical bars show the statistical uncertainty in the number of tracks in each bin. The measured energy resolution for each method in this sample is comparable. |
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Figure 6-b:
The HCAL energy response of M0, M2, and MAHI, measured in an electron/photon-triggered data set using isolated tracks with 20 $ < p_\text{track} < $ 30 GeV and 1.5 $ < |\eta_\text{track}| < $ 2.3. The vertical bars show the statistical uncertainty in the number of tracks in each bin. The measured energy resolution for each method in this sample is comparable. |
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Figure 7:
Ratios of responses to simulated charged pions between a sample with OOTPU included to a sample with no pileup, as functions of the generated pion energy. The left plot shows the response for pions in the HB ($ |\eta| < $ 1.3), whereas the middle and right plots show the response for pions in the HE (1.4 $ < |\eta| < $ 2.5 and 2.5 $ < |\eta| < $ 3.0). |
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Figure 7-a:
Ratios of responses to simulated charged pions between a sample with OOTPU included to a sample with no pileup, as functions of the generated pion energy. The plot shows the response for pions in the HB ($ |\eta| < $ 1.3). |
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Figure 7-b:
Ratios of responses to simulated charged pions between a sample with OOTPU included to a sample with no pileup, as functions of the generated pion energy. The plot shows the response for pions in the HE (1.4 $ < |\eta| < $ 2.5). |
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Figure 7-c:
Ratios of responses to simulated charged pions between a sample with OOTPU included to a sample with no pileup, as functions of the generated pion energy. The plot shows the response for pions in the HE (2.5 $ < |\eta| < $ 3.0). |
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Figure 8:
$ p_{\mathrm{T}}^\text{miss} $ resolutions as a function of the Z boson $ p_{\mathrm{T}} $, measured in a data set triggered by two muons. The left (right) plot is the resolution of the component parallel (perpendicular) to the Z boson's $ p_{\mathrm{T}} $. Error bars reflect statistical uncertainties. |
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Figure 8-a:
$ p_{\mathrm{T}}^\text{miss} $ resolutions as a function of the Z boson $ p_{\mathrm{T}} $, measured in a data set triggered by two muons. The plot is the resolution of the component parallel to the Z boson's $ p_{\mathrm{T}} $. Error bars reflect statistical uncertainties. |
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Figure 8-b:
$ p_{\mathrm{T}}^\text{miss} $ resolutions as a function of the Z boson $ p_{\mathrm{T}} $, measured in a data set triggered by two muons. The plot is the resolution of the component perpendicular to the Z boson's $ p_{\mathrm{T}} $. Error bars reflect statistical uncertainties. |
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Figure 9:
Relative difference of $ p_{\mathrm{T}}^\text{miss} $ in muon-triggered events between the online and offline reconstructions. Black: (online }$ p_{\mathrm{T}}^\text{miss} $(\text{M3) $ - \text{offline }p_{\mathrm{T}}^\text{miss} $ (M2) $) / \text{offline }p_{\mathrm{T}}^\text{miss} $ (M2). Red: (online }$ p_{\mathrm{T}}^\text{miss} $(\text{MAHI) $ - \text{offline }p_{\mathrm{T}}^\text{miss} $ (MAHI) $) / \text{offline }p_{\mathrm{T}}^\text{miss} $ (MAHI). The differences are fit to a Gaussian distribution and the fitted value of the standard deviation is shown. |
| Summary |
| Four local energy reconstruction algorithms for the CMS hadron calorimeter (HCAL) have been presented in this paper and their performance compared. When the bunch-crossing spacing is at least 50 ns, Method 0 performs well; however, a pulse-shape fitting algorithm must be used when the spacing is only 25 ns. The performance of Method 2 is strong under conditions of high out-of-time pileup, but its long reconstruction time makes it unusable in the high-level trigger. The use of a different algorithm, such as Method 3, to accommodate the time constraints of online reconstruction is possible, but the deleterious effects of mismatched algorithms make this solution undesirable. ``Minimization at HCAL, Iteratively'' is a pulse-shape fitting algorithm that readily suppresses out-of-time pileup and has good intrinsic energy resolution, and is also sufficiently fast to run in the high-level trigger. Hence, it was the preferred local energy reconstruction algorithm for HCAL by the end of Run 2. |
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