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From the 1 of 173 linked papers with an AI index.

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20242026
most citedDetermination of the spin and parity of all-charm tetraquarks

20 citations · 67 across the 42 of their papers we have counts for

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Showing 2025Show all

76 papers · 1 filter

hep-ex20252 cited

Search for a new scalar resonance decaying to a Higgs boson and another new scalar particle in the final state with two bottom quarks and two photons in proton-proton collisions at = 13 TeV

CMS Collaboration

A search is presented for a new scalar resonance, X, decaying to a standard model Higgs boson and another new scalar particle, Y, in the final state where the Higgs boson decays to…

hep-ex20251 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-ex202520 cited

Determination of the spin and parity of all-charm tetraquarks

CMS Collaboration

The traditional quark model accounts for the existence of baryons, such as protons and neutrons, which consist of three quarks, as well as mesons, composed of a quark-antiquark pai…

hep-ex2025

Search for new physics in jet multiplicity patterns of multilepton events at = 13 TeV

CMS Collaboration

A first search for beyond the standard model physics in jet multiplicity patterns of multilepton events is presented, using a data sample corresponding to an integrated luminosity…

astro-ph.HE20251 cited

Seasonal Variations of the Atmospheric Muon Neutrino Spectrum measured with IceCube

R. Abbasi, M. Ackermann, J. Adams +425

This study presents an energy-dependent analysis of seasonal variations in the atmospheric muon neutrino spectrum, using 11.3 years of data from the IceCube Neutrino Observatory. B…

hep-ex20251 cited

Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC

CMS Collaboration

We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towa…