2023-11-27
17:26 |
CMS tracking performance in 2023
/CMS Collaboration
This note reports on the results of the comparisons between data and Monte Carlo simulated events for the tracking-related reconstructed observables. The analyzed data were collected during 13.6 TeV proton-proton collisions in 2023. [...]
CMS-DP-2023-090; CERN-CMS-DP-2023-090.-
Geneva : CERN, 2023 - 29 p.
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2023-11-20
14:30 |
Efficiency of the ParticleNet b-tagging algorithm used in the CMS High-Level Trigger in 2022 and 2023
/CMS Collaboration
The ParticleNet b-tagging algorithm has been adapted for and integrated into the CMS High-Level Trigger (HLT) since the beginning of the Run-3 data-taking period, and used in a series of new triggers targeting HH$\rightarrow$4b signal events. This note shows the measurement of the ParticleNet b-tag efficiency, in 2022 data and simulation as well as in 2023 data, as a function of the offline b-tag score. [...]
CMS-DP-2023-089; CERN-CMS-DP-2023-089.-
Geneva : CERN, 2023 - 7 p.
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2023-11-20
14:30 |
CMS generator studies towards negative weight reduction and acceleration usage
/CMS Collaboration
This note presents several new developments on event generators for negative
weight reduction and generator acceleration usage. Two new algorithms,
including a histogram based resampler[1] and a neural-network (NN)[2] based
resampler are introduced and tested with CMS Monte Carlo (MC) samples.
Effective reduction of negatively weighted events has been demonstrated, with
physics performance preserved. [...]
CMS-DP-2023-088; CERN-CMS-DP-2023-088.-
Geneva : CERN, 2023 - 12 p.
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2023-11-20
14:30 |
RadSim Results for CMS Phase-2 v.6.0.0.2 Radiation Simulation
/CMS Collaboration
Monte Carlo estimation of the fluence or absorbed dose at CMS from simulation run v.6.0.0.2. This run uses CERN FLUKA code v4-1.1 to simulate proton-proton collisions at an energy of 7 TeV per beam. [...]
CMS-DP-2023-087; CERN-CMS-DP-2023-087.-
Geneva : CERN, 2023 - 32 p.
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2023-11-13
11:17 |
Level-1 Trigger Calorimeter Image Convolutional Anomaly Detection Algorithm
/CMS Collaboration
This performance note shows the implementation of the Calorimeter Image Convolutional Anomaly Detection Algorithm (CICADA), a Level-1 triggering algorithm designed to use fast, unsupervised machine learning techniques to trigger on anomalous events. Some of the score and rate performance of the algorithm as emulated on 2023 data are shown..
CMS-DP-2023-086; CERN-CMS-DP-2023-086.-
Geneva : CERN, 2023 - 8 p.
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2023-11-06
17:17 |
Generating parton-level events from reconstructed events with conditional normalizing flows
In High Energy Physics, generating physically meaningful parton configurations from a collision reconstructed within a detector is a critical step for many complex analysis tasks such as the Matrix Element Method computation and Bayesian inference on parameters of interest. Regressing parton level quantities, such as the transverse momentum of single particles, is a common task, but extracting the full event description at the generator level poses significant challenges. [...]
CMS-DP-2023-085; CERN-CMS-DP-2023-085.-
Geneva : CERN, 2023 - 25 p.
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2023-11-06
17:17 |
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2023-11-06
17:17 |
CMS Silicon Strip Tracker Maps
/CMS Collaboration
Tracker maps with silicon temperature and leaking current in the CMS strip tracker are presented..
CMS-DP-2023-083; CERN-CMS-DP-2023-083.-
Geneva : CERN, 2023 - 4 p.
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2023-11-06
17:17 |
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2023-11-06
17:17 |
Low-$p_T$ Electron ID scale factors from CMS in proton-proton collisions at $\sqrt{s}$ =13 TeV using $J/\psi$ events
/CMS Collaboration
Low-$p_T$ electrons (5 $< p_T <$ 15 GeV) appear in several final states in CMS (Compact Muon Solenoid) analyses. Their large interaction cross section with the tracker material and the presence of the strong CMS magnetic field makes the efficiency measurement for these low-$p_T$ electrons challenging, leading to larger uncertainties than at higher $p_T$.
A new approach is devised to measure the identification (ID) efficiency for low-$p_T$ electrons reconstructed using the CMS particle flow algorithm. [...]
CMS-DP-2023-081; CERN-CMS-DP-2023-081.-
Geneva : CERN, 2023 - 18 p.
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