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Pileup mitigation at CMS in 13 TeV data
CMS collaboration - arxiv preprint arxiv:2003.00503, 2020 - arxiv.org
With increasing instantaneous luminosity at the LHC come additional reconstruction
challenges. At high luminosity, many collisions occur simultaneously within one proton …
challenges. At high luminosity, many collisions occur simultaneously within one proton …
[HTML][HTML] Review of top quark mass measurements in CMS
CMS collaboration - Physics Reports, 2025 - Elsevier
The top quark mass is one of the most intriguing parameters of the standard model (SM). Its
value indicates a Yukawa coupling close to unity, and the resulting strong ties to Higgs …
value indicates a Yukawa coupling close to unity, and the resulting strong ties to Higgs …
Higgs physics at the HL-LHC and HE-LHC
The discovery of the Higgs boson in 2012, by the ATLAS and CMS experiments, was a
success achieved with only a percent of the entire dataset foreseen for the LHC. It opened a …
success achieved with only a percent of the entire dataset foreseen for the LHC. It opened a …
Jet tagging via particle clouds
How to represent a jet is at the core of machine learning on jet physics. Inspired by the
notion of point clouds, we propose a new approach that considers a jet as an unordered set …
notion of point clouds, we propose a new approach that considers a jet as an unordered set …
[HTML][HTML] Machine learning for anomaly detection in particle physics
The detection of out-of-distribution data points is a common task in particle physics. It is used
for monitoring complex particle detectors or for identifying rare and unexpected events that …
for monitoring complex particle detectors or for identifying rare and unexpected events that …
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
CMS collaboration - arxiv preprint arxiv:2004.08262, 2020 - arxiv.org
Machine-learning (ML) techniques are explored to identify and classify hadronic decays of
highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also …
highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also …
Anomaly detection with density estimation
We leverage recent breakthroughs in neural density estimation to propose a new
unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By …
unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By …
Jet substructure at the Large Hadron Collider: a review of recent advances in theory and machine learning
Jet substructure has emerged to play a central role at the Large Hadron Collider (LHC),
where it has provided numerous innovative new ways to search for new physics and to …
where it has provided numerous innovative new ways to search for new physics and to …
Performance of pile-up mitigation techniques for jets in collisions at TeV using the ATLAS detector
Atlas Collaboration - arxiv preprint arxiv:1510.03823, 2015 - arxiv.org
The large rate of multiple simultaneous proton--proton interactions, or pile-up, generated by
the Large Hadron Collider in Run 1 required the development of many new techniques to …
the Large Hadron Collider in Run 1 required the development of many new techniques to …
Energy flow networks: deep sets for particle jets
A bstract A key question for machine learning approaches in particle physics is how to best
represent and learn from collider events. As an event is intrinsically a variable-length …
represent and learn from collider events. As an event is intrinsically a variable-length …