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pro vyhledávání: '"Larkoski, A"'
Top quark polarization provides an important tool for studying its production mechanisms, spin correlations, top quark properties, and new physics searches. Unlike lighter quarks, the top quark's polarization remains intact until its decay, enabling
Externí odkaz:
http://arxiv.org/abs/2407.07147
Autor:
Larkoski, Andrew J.
These lectures were presented at the 2024 QCD Masterclass in Saint-Jacut-de-la-Mer, France. They introduce and review fundamental theorems and principles of machine learning within the context of collider particle physics, focused on application to j
Externí odkaz:
http://arxiv.org/abs/2407.04897
Precision studies for top quark physics are a cornerstone of the Large Hadron Collider program. Polarization, probed through decay kinematics, provides a unique tool to scrutinize the top quark across its various production modes and to explore poten
Externí odkaz:
http://arxiv.org/abs/2407.01663
How can one fully harness the power of physics encoded in relativistic $N$-body phase space? Topologically, phase space is isomorphic to the product space of a simplex and a hypersphere and can be equipped with explicit coordinates and a Riemannian m
Externí odkaz:
http://arxiv.org/abs/2405.16698
Autor:
Larkoski, Andrew J.
Recently, a factorization theorem was proposed for partonic flavor evolution as defined by the net flavor of the Winner-Take-All axis of a jet. We validate the factorization theorem through explicit calculation at two-loop order, and in the process e
Externí odkaz:
http://arxiv.org/abs/2402.06735
Autor:
Larkoski, Andrew J., Neill, Duff
A definition of partonic jet flavor that is both theoretically well-defined and experimentally robust would have profound implications for measurements and predictions especially for heavy flavor applications. Recently, a definition of jet flavor was
Externí odkaz:
http://arxiv.org/abs/2310.01486
Autor:
Larkoski, Andrew J.
Binary discrimination between well-defined signal and background datasets is a problem of fundamental importance in particle physics. With detailed event simulation and the advent of extensive deep learning tools, identification of the likelihood rat
Externí odkaz:
http://arxiv.org/abs/2309.14417
Publikováno v:
Journal of High Energy Physics, Vol 2024, Iss 9, Pp 1-38 (2024)
Abstract How can one fully harness the power of physics encoded in relativistic N-body phase space? Topologically, phase space is isomorphic to the product space of a simplex and a hypersphere and can be equipped with explicit coordinates and a Riema
Externí odkaz:
https://doaj.org/article/616ff4f380574496a6556d00ba7e2f88
Publikováno v:
Journal of High Energy Physics, Vol 2024, Iss 7, Pp 1-28 (2024)
Abstract Machine learning-based jet classifiers are able to achieve impressive tagging performance in a variety of applications in high-energy and nuclear physics. However, it remains unclear in many cases which aspects of jets give rise to this disc
Externí odkaz:
https://doaj.org/article/2607df17cf024a19b6bb11f5b3d08cc4
Publikováno v:
Phys. Rev. D 109, 054039 (2024)
The single-particle inclusive fragmentation function and the particle multiplicity are observables of fundamental importance in studying properties of quantum chromodynamics at colliders. It is well-known that at high energies, the multiplicity distr
Externí odkaz:
http://arxiv.org/abs/2305.13359