D. Athanasakos

ORCID: 0000-0003-1244-1607
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About
Contact & Profiles
Research Areas
  • Particle physics theoretical and experimental studies
  • Particle Detector Development and Performance
  • High-Energy Particle Collisions Research
  • Dark Matter and Cosmic Phenomena
  • Quantum Chromodynamics and Particle Interactions

Stony Brook University
2023-2024

Abstract A multi-TeV muon collider offers a spectacular opportunity in the direct exploration of energy frontier. Offering combination unprecedented collisions comparatively clean leptonic environment, high has unique potential to provide both precision measurements and highest reach one machine that cannot be paralleled by any currently available technology. The topic generated lot excitement Snowmass meetings continues attract large number supporters, including many from early career...

10.1088/1748-0221/19/02/t02015 article EN cc-by Journal of Instrumentation 2024-02-01

A bstract Machine learning-based jet classifiers are able to achieve impressive tagging performance in a variety of applications high-energy and nuclear physics. However, it remains unclear many cases which aspects jets give rise this discriminating power, whether observables that tractable perturbative QCD such as those obeying infrared-collinear (IRC) safety serve sufficient inputs. In article, we introduce new classifier, Jet Flow Networks (JFNs), an effort address the question IRC unsafe...

10.1007/jhep07(2024)257 article EN cc-by Journal of High Energy Physics 2024-07-26

A multi-TeV muon collider offers a spectacular opportunity in the direct exploration of energy frontier. Offering combination unprecedented collisions comparatively clean leptonic environment, high has unique potential to provide both precision measurements and highest reach one machine that cannot be paralleled by any currently available technology. The topic generated lot excitement Snowmass meetings continues attract large number supporters, including many from early career community. In...

10.48550/arxiv.2209.01318 preprint EN other-oa arXiv (Cornell University) 2022-01-01

Machine learning-based jet classifiers are able to achieve impressive tagging performance in a variety of applications high energy and nuclear physics. However, it remains unclear many cases which aspects jets give rise this discriminating power, whether observables that tractable perturbative QCD such as those obeying infrared-collinear (IRC) safety serve sufficient inputs. In article, we introduce new classifier, Jet Flow Networks (JFNs), an effort address the question IRC unsafe...

10.48550/arxiv.2305.08979 preprint EN cc-by arXiv (Cornell University) 2023-01-01
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