2026-05-26
11:43 |
Developments in Combine
/CMS Collaboration
This note describes recent developments in Combine, the CMS statistical analysis and combination tool, with a focus on the development of automatic differentiation support within the tool. In recent years, the RooFit team have added various new backends to RooFit, one of which is the codegen backend which enables AD via the
Clad tool. [...]
CMS-DP-2026-052; CERN-CMS-DP-2026-052.-
Geneva : CERN, 2026 - 16 p.
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2026-05-26
11:43 |
CMS Analysis Frameworks
/CMS Collaboration
One of the main challenges currently facing high energy particle physicists analyzing data from the Large Hadron Collider (LHC) at CERN is the unprecedented volume of both real data and simulated data that must be processed. This challenge is expected to intensify as the LHC enters its high luminosity phase, during which it is projected to deliver up to ten times more data than before. [...]
CMS-DP-2026-051; CERN-CMS-DP-2026-051.-
Geneva : CERN, 2026 - 20 p.
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2026-05-26
11:43 |
Differentiable Setup for a Top-Higgs Analysis
/CMS Collaboration
Autodifferentiation allows for the efficient computation of gradients in a complex computational graph. If the
analysis workflow, including the likelihood function, is implemented in a fully differentiable manner, the
parameters of an analysis can be optimized using the information of the statistical analysis.
In this work, we present how histogramming and the construction of the likelihood function can be
implemented in a differentiable way. [...]
CMS-DP-2026-050; CERN-CMS-DP-2026-050.-
Geneva : CERN, 2026 - 14 p.
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2026-05-26
11:43 |
Modeling the CMS High-Granularity Calorimeter with Generative AI
/CMS Collaboration
In the upcoming High-Luminosity LHC era, detector simulation will face computing resource constraints. At the same time the CMS detector will be upgraded with the new High Granularity Calorimeter (HGCal), which is more intensive to simulate. [...]
CMS-DP-2026-049; CERN-CMS-DP-2026-049.-
Geneva : CERN, 2026 - 26 p.
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2026-05-26
11:43 |
HLT Scouting performance in 2024 - e/gamma triggers and objects
/CMS Collaboration
This note presents an overview of the performance of electron and photon trigger candidates and algorithms processed through the scouting stream of CMS during 2024 proton-proton runs of the LHC..
CMS-DP-2026-048; CERN-CMS-DP-2026-048.-
Geneva : CERN, 2026 - 19 p.
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2026-05-26
11:43 |
Offloading CMS reconstruction modules to remote GPUs with RDMA
/CMS Collaboration
This study demonstrates the feasibility of a client-server distributed CMSSW, and benchmarks its current
implementation against the single-process CMSSW as it runs in production today. These benchmarks are run on two different HLT menus: the full HLT menu utilized online during proton-proton collisions in 2026 and a reduced HLT menu comprizing a subset of the full menu, running on every event all the GPU modules, and a small number of CPU modules required to prepare their input. [...]
CMS-DP-2026-047; CERN-CMS-DP-2026-047.-
Geneva : CERN, 2026 - 13 p.
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2026-05-26
11:43 |
Prospects For Anomaly Detection In the CMS High-Level Trigger
/CMS Collaboration
This study presents an offline feasibility study for a second-layer anomaly-detection algorithm at HLT. The anomaly detection goal is to detect events that deviate significantly from the dominant background. [...]
CMS-DP-2026-046; CERN-CMS-DP-2026-046.-
Geneva : CERN, 2026 - 18 p.
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2026-05-26
11:43 |
Enhancing the ME0 Level-1 Trigger in the CMS Experiment Using Machine Learning
/CMS Collaboration
The ME0 Gas Electron Multiplier (GEM) detector systems will be installed for the phase-2 upgrade of the Compact Muon Solenoid (CMS) experiment in the Large Hadron Collider (LHC). The ME0 detectors, located in each endcap of the muon system, are the only muon detectors that cover the range $2.4 < \lvert\eta\rvert < 2.8$. [...]
CMS-DP-2026-045; CERN-CMS-DP-2026-045.-
Geneva : CERN, 2026 - 8 p.
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2026-05-26
11:42 |
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2026-05-22
10:33 |
Soft Tau Reconstruction and R$\&$D on Accelerators in CMS Phase-2 Level-1 Trigger Scouting
/CMS Collaboration
Traditional data streams rely on a decision of the Level-1 trigger to accept only a fraction of the
events for further processing and selection by the High Level Trigger, followed by an offline
analysis of the selected events. Level-1 scouting introduces an alternative path, by analyzing
events only based on the Level-1 trigger reconstruction without a preselection. [...]
CMS-DP-2026-043; CERN-CMS-DP-2026-043.-
Geneva : CERN, 2026 - 61 p.
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