168 citations · 177 across the 7 of their papers we have counts for
7 papers
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
Report of the Topical Group on Electroweak Precision Physics and Constraining New Physics for Snowmass 2021
Alberto Belloni, Ayres Freitas, Junping Tian +65
The precise measurement of physics observables and the test of their consistency within the standard model (SM) are an invaluable approach, complemented by direct searches for new…
Anomalous production of massive gauge boson pairs at muon colliders
Brad Abbott, Aram Apyan, Bianca Azartash-Namin +11
The prospects of searches for anomalous production of hadronically decaying weak boson pairs at proposed high-energy muon colliders are reported. Muon-muon collision events are sim…
Physics potential for the HZZ decay at the CEPC
Ryuta Kiuchi, Yanxi Gu, Min Zhong +5
The precision of the yield measurement of the Higgs boson decaying into a pair of bosons process at the Circular Electron Positron Collider (CEPC) is evaluated. Including the r…
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
Xiangyang Ju, Daniel Murnane, Paolo Calafiura +21
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detecto…
Extending RECAST for Truth-Level Reinterpretations
Alex Schuy, Lukas Heinrich, Kyle Cranmer +1
RECAST is an analysis reinterpretation framework; since analyses are often sensitive to a range of models, RECAST can be used to constrain the plethora of theoretical models withou…