[HTML][HTML] Enriching the physics program of the CMS experiment via data scouting and data parking
CMS collaboration - Physics Reports, 2024 - Elsevier
Specialized data-taking and data-processing techniques were introduced by the CMS
experiment in Run 1 of the CERN LHC to enhance the sensitivity of searches for new …
experiment in Run 1 of the CERN LHC to enhance the sensitivity of searches for new …
Machine learning in the search for new fundamental physics
Compelling experimental evidence suggests the existence of new physics beyond the well-
established and tested standard model of particle physics. Various current and upcoming …
established and tested standard model of particle physics. Various current and upcoming …
[HTML][HTML] Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton–proton collisions at s= 13TeV at the CMS experiment
CMS collaboration - Physics Reports, 2024 - Elsevier
The LHC has provided an unprecedented amount of proton–proton collision data, bringing
forth exciting opportunities to address fundamental open questions in particle physics …
forth exciting opportunities to address fundamental open questions in particle physics …
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 …
Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at = 13 TeV
A Tumasyan, W Adam, JW Andrejkovic… - Journal of High Energy …, 2021 - Springer
A bstract A search is presented for new particles produced at the LHC in proton-proton
collisions at\(\sqrt {s}\)= 13 TeV, using events with energetic jets and large missing …
collisions at\(\sqrt {s}\)= 13 TeV, using events with energetic jets and large missing …
Graph neural networks in particle physics
Particle physics is a branch of science aiming at discovering the fundamental laws of matter
and forces. Graph neural networks are trainable functions which operate on graphs—sets of …
and forces. Graph neural networks are trainable functions which operate on graphs—sets of …
Identification of hadronic tau lepton decays using a deep neural network
CMS collaboration - arxiv preprint arxiv:2201.08458, 2022 - arxiv.org
A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons
($\tau_\mathrm {h} $) that originate from genuine tau leptons in the CMS detector against …
($\tau_\mathrm {h} $) that originate from genuine tau leptons in the CMS detector against …
[HTML][HTML] 50 Years of quantum chromodynamics: Introduction and Review
Quantum Chromodynamics, the theory of quarks and gluons, whose interactions can be
described by a local SU (3) gauge symmetry with charges called “color quantum numbers” …
described by a local SU (3) gauge symmetry with charges called “color quantum numbers” …
Search for supersymmetry in final states with two oppositely charged same-flavor leptons and missing transverse momentum in proton-proton collisions at = 13 …
AM Sirunyan, A Tumasyan, W Adam… - Journal of High Energy …, 2021 - Springer
A bstract A search for phenomena beyond the standard model in final states with two
oppositely charged same-flavor leptons and missing transverse momentum is presented …
oppositely charged same-flavor leptons and missing transverse momentum is presented …
An efficient Lorentz equivariant graph neural network for jet tagging
A bstract Deep learning methods have been increasingly adopted to study jets in particle
physics. Since symmetry-preserving behavior has been shown to be an important factor for …
physics. Since symmetry-preserving behavior has been shown to be an important factor for …