8 papers
Uncovering Hidden Systematics in Neural Network Models for High Energy Physics
Lucie Flek, Philipp Alexander Jungs, Akbar Karimi +6
Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flexibility enables unprecedente…
Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications
Philip Bechtle, Lucie Flek, Philipp Alexander Jung +7
In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our understanding of fundamental phenomena.…
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…
Search for a new heavy resonance decaying to a top quark and a neutral scalar boson in proton-proton collisions at = 13 TeV
CMS Collaboration
A first search at the LHC for a new heavy resonance decaying to a top quark and a neutral scalar boson in the fully hadronic final state is presented, where the boson is id…
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…