1 citations · 3 across the 10 of their papers we have counts for
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Parnassus for the CLD Detector: A Generative Machine-Learning Surrogate for Detector Simulation and Reconstruction at the FCC-ee
Umar Sohail Qureshi, Benjamin Nachman, Caterina Vernieri
Detector simulation and event reconstruction will be computationally expensive for future collider programs and are currently critical bottlenecks for accurate feasibility…
Dffusion: Capturing the Beam-Beam Physics of Collisions with Diffusion Models
Antonio Chahine, Mariarosaria D'Alfonso, Jan Eysermans +10
Beam-induced backgrounds at high-luminosity colliders, such as the FCC-ee, are dominated by incoherent pair creation (IPC), and require computationally expensive simulatio…
Reweighting Adversarial Networks for Unbinned Unfolding
Umar Sohail Qureshi, Krish Desai, Jesse Thaler +1
Differential cross sections are the currency of scientific exchange in particle and nuclear physics. Recently, machine learning methods have enabled unbinned and high-dimensional c…
Higgs Physics with the XFEL Compton Collider Concept at GeV
Umar Sohail Qureshi, Tim Barklow, Ariel Schwartzman
We investigate single Higgs production in GeV collisions at the X-Ray Free Electron (XFEL) Compton Collider (XCC) concept and present an analysis targeting the…
Probing the Higgs Self-Coupling with an XFEL Compton Collider at GeV
Santiago Ampudia Castelazo, Umar Sohail Qureshi, Tim Barklow +1
We present a study probing the Higgs self-coupling with the X-ray free-electron laser Compton Collider (XCC) concept. The analysis is performed considering the $γγ\to HH \to b…
PanopTag: Simultaneously Tagging All Jets in a Particle Collision Event
Umar Sohail Qureshi, Brendon Bullard, Ariel Schwartzman
Jet tagging, identifying the origin of jets produced in particle collisions, is a critical classification task in high-energy physics. Despite the revolutionary impact of deep lear…