27 citations · 29 across the 9 of their papers we have counts for
10 papers
RINO: Renormalization Group Distillation with No Labels
Zichun Hao, Raghav Kansal, Abhijith Gandrakota +6
Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfaithful to the underlying physics…
Measurement of the top quark pair production cross section in PbPb collisions at = 5.36 TeV
CMS Collaboration
The inclusive cross section for top quark pair () production in lead-lead (PbPb) collisions is reported for the first time at a center-of-mass energy per nucleon…
Evidence of ZZ production and observation of in proton-proton collisions at = 13 TeV
CMS Collaboration
Evidence of the production of two Z bosons and a photon in proton-proton collisions is reported for the first time in CMS. The analysis uses data collected by the CMS experiment be…
Search for Higgs boson pair production in the decay channel with two leptons in the final state using proton-proton collision data at = 13.6 TeV
CMS Collaboration
A search for Higgs boson pair production is presented, targeting final states where one Higgs boson decays to a pair of bottom quarks and the other Higgs boson decays to two W boso…
Search for Higgs boson production at high transverse momentum in the WW decay channel in proton-proton collisions at = 13 TeV
CMS Collaboration
A search for Higgs boson (H) production at high transverse momentum () in the WW decay channel is presented. The analysis uses proton-proton collisions at …
System-size dependence of charged-particle suppression in ultrarelativistic nucleus-nucleus collisions
CMS Collaboration
High-energy partons lose energy while propagating through the hot, strongly interacting medium produced in ultrarelativistic nucleus-nucleus collisions, leading to a suppression of…
Observation of the jet diffusion wake using dijets in heavy ion collisions
CMS Collaboration
Energetic quarks and gluons traversing a hot and dense quark-gluon plasma deposit energy and momentum into the medium before hadronizing to collimated sprays of particles, known as…
An Evaluation of Representation Learning Methods in Particle Physics Foundation Models
Michael Chen, Raghav Kansal, Abhijith Gandrakota +3
We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-clou…
RINO: Renormalization Group Invariance with No Labels
Zichun Hao, Raghav Kansal, Abhijith Gandrakota +4
A common challenge with supervised machine learning (ML) in high energy physics (HEP) is the reliance on simulations for labeled data, which can often mismodel the underlying colli…
Lorentz group equivariant autoencoders
Zichun Hao, Raghav Kansal, Javier Duarte +1
There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection…