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20212025
most citedDark sector searches with the CMS experiment

33 citations · 171 across the 25 of their papers we have counts for

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24 papers · 1 filter

hep-ex2025★ 1 cited

Identification of tau leptons using a convolutional neural network with domain adaptation

CMS Collaboration

A tau lepton identification algorithm, DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic de…

hep-ex2025★ 1 cited

Search for low-mass hidden-valley dark showers with non-prompt muon pairs in proton-proton collisions at = 13 TeV

CMS Collaboration

A search for signatures of a dark analog to quantum chromodynamics is performed. The analysis targets long-lived dark mesons that decay into standard-model particles, with a high b…

hep-ex2025

Inclusive and differential measurements of the cross section and the / cross section ratio in proton-proton collisions at = 13 TeV

CMS Collaboration

Inclusive and differential cross section measurements of top quark pair () production in association with a photon () are performed as a function of lepton, p…

hep-ex2025★ 1 cited

Search for dijet resonances with data scouting in proton-proton collisions at = 13 TeV

CMS Collaboration

A search is presented for narrow resonances, with a mass between 0.6 and 1.8 TeV, decaying to pairs of jets, in proton-proton collisions at = 13 TeV. The search is perfo…

hep-ex2025

Search for dark matter production in association with bottom quarks and a lepton pair in proton-proton collisions at = 13 TeV

CMS Collaboration

A search is performed for dark matter produced in association with bottom quarks and a pair of electrons or muons in data collected with the CMS detector at the LHC, corresponding…

hep-ex2025★ 1 cited

Wasserstein normalized autoencoder for anomaly detection

CMS Collaboration

A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between…