CERN Accelerating science

CMS Detector Performance Summaries

新增:
2025-09-09
11:36
MEMFlow in Double Higgs search application /CMS Collaboration
The Matrix Element Method (MEM) offers optimal statistical power for hypothesis testing in particle physics, but its application is hindered by the computationally intensive multidimensional integrals required to model detector effects. We present a novel approach that addresses this challenge by employing Transformers and generative machine learning (ML) models. [...]
CMS-DP-2025-056; CERN-CMS-DP-2025-056.- Geneva : CERN, 2025 - 57 p. Fulltext: PDF;

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2025-09-09
11:36
Results on CUDA and HIP execution graphs and unified memory in CLUE /CMS Collaboration
This note outlines the impact of execution graphs and unified memory with CUDA and ROCm on CLUE..
CMS-DP-2025-055; CERN-CMS-DP-2025-055.- Geneva : CERN, 2025 - 100 p. Fulltext: PDF;

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2025-09-09
11:36
Validation of online GPU vs. CPU reconstruction in Pixel, ECAL, HCAL, and PF /CMS Collaboration
Since the beginning of Run 3, the CMS experiment has implemented a heterogeneous scheme for the High-Level Trigger (HLT) farm that integrates NVIDIA GPU accelerators for online event reconstruction. This note reports on the validation of GPU-based reconstruction against CPU for pixel, ECAL, and HCAL local reconstruction, as well as for pixel track and vertex reconstruction and the clustering stage of Particle Flow (PF) reconstruction. [...]
CMS-DP-2025-054; CERN-CMS-DP-2025-054.- Geneva : CERN, 2025 - 11 p. Fulltext: PDF;

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2025-09-04
09:55
From bins to flows: unbinned and multivariate scale factors /CMS Collaboration
Most CMS objects, taggers, and triggers are calibrated using likelihood fits. A widely used technique for measuring efficiencies, particularly for electrons and muons, is the Tag-and-Probe method. This approach exploits the clean signature of $Z\rightarrow l^{+}l^{-}$ decays: one lepton is required to pass stringent identification and trigger criteria (the tag), while the other (the probe) is used to study the efficiency of a given selection in both data and Monte Carlo (MC) simulation. [...]
CMS-DP-2025-053; CERN-CMS-DP-2025-053.- Geneva : CERN, 2025 - 25 p. Fulltext: PDF;

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2025-09-04
09:55
Calibration of the jet mass scale using boosted W bosons and top quarks for data taken in 2022 /CMS Collaboration
This note reports correction factors, to be applied to simulation, for soft drop jet mass for the identification of hadronically decaying top quarks or W bosons. These scale factors are calculated from and are valid for the proton--proton collision data set at $\sqrt{s} = 13.6$ TeV from the 2022 data-taking of the CMS experiment, corresponding to a total integrated luminosity of $34.65 fb ^{#-1}$..
CMS-DP-2025-052; CERN-CMS-DP-2025-052.- Geneva : CERN, 2025 - 37 p. Fulltext: PDF;

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2025-09-01
11:27
Performance of mkFit and LST algorithms in the CMS Phase-2 High Level Trigger Tracking /CMS Collaboration
This note presents results on the performance of mkFit and LST algorithms in the CMS Phase-2 High Level Trigger (HLT) Tracking
CMS-DP-2025-051; CERN-CMS-DP-2025-051.- Geneva : CERN, 2025 - 21 p. Fulltext: PDF;

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2025-09-01
11:27
Debiasing Ultrafast Anomaly Detection with Posterior Agreement /CMS Collaboration
The Level-1 Trigger system of the CMS experiment at CERN makes the final decision on which LHC collision data are stored to disk for later analysis. One algorithm used with this scope is an anomaly detection model based on an autoencoder architecture. [...]
CMS-DP-2025-050; CERN-CMS-DP-2025-050.- Geneva : CERN, 2025 - 40 p. Fulltext: PDF;

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2025-09-01
11:27
Performance Comparison of Lossless Compression Algorithms on CMS Data using ROOT TTree and RNTuple /CMS Collaboration
This note reports on the performance of lossless compression algorithms for RAW data storage in Run 3 and Phase 2 simDigis, comparing the new RNTuple data format with the TTree format..
CMS-DP-2025-049; CERN-CMS-DP-2025-049.- Geneva : CERN, 2025 - 24 p. Fulltext: PDF;

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2025-09-01
11:27
Extended ML Selections and Track Embeddings for Duplicate Removal in Line Segment Tracking (LST) /CMS Collaboration
This note describes recent improvements to the CMS Line Segment Tracking algorithm through the further integration of deep neural network (DNN) selections and a new ML-based duplicate removal strategy
CMS-DP-2025-048; CERN-CMS-DP-2025-048.- Geneva : CERN, 2025 - 43 p.

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2025-09-01
11:27
Performance of boosted tau lepton identification with DeepTau Framework (Boosted DeepTau) /CMS Collaboration
This note presents a dedicated identification algorithm targeting individual hadronic tau leptons ($\tau_\mathrm{h}$) within boosted ditau systems. Based on the DeepTau architecture used for resolved $\tau_\mathrm{h}$, the Boosted DeepTau algorithm achieves a factor of 2--4 improvement in the rejection of jets for individual $\tau_\mathrm{h}$ candidates with $p_\mathrm{T}$ $<$ 100 GeV, and an order of magnitude improvement at higher $p_\mathrm{T}$. [...]
CMS-DP-2025-047; CERN-CMS-DP-2025-047.- Geneva : CERN, 2025 - 15 p. Fulltext: PDF;

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