Results 21 to 30 of about 1,416,187 (309)
MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks
European Physical Journal C: Particles and Fields, 2021 In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector ...
Joosep Pata +4 more
doaj +1 more source
Missing information search with deep learning for mass estimation
Physical Review Research, 2023 We introduce DeeLeMa, a deep learning-based network for the analysis of energy and momentum in high-energy particle collisions. This novel approach is specifically designed to address the challenge of analyzing collision events with multiple invisible ...
Kayoung Ban +4 more
doaj +1 more source
Investigating the origin of strangeness enhancement in small systems through multi-differential analyses [PDF]
EPJ Web of Conferences, 2022 The main goal of the ALICE experiment is to study the physics of strongly interacting matter, including the properties of the quark-gluon plasma (QGP). The relative production of strange hadrons with respect to non-strange hadrons in heavy-ion collisions
Ercolessi Francesca
doaj +1 more source
Detection of zero anisotropy at 5.2 AU during the November 1998 solar particle event: Ulysses Anisotropy Telescopes observations [PDF]
Annales Geophysicae, 2000 For the first time during the mission, the Anisotropy Telescopes instrument on board the Ulysses spacecraft measured constant zero anisotropy of protons in the 1.3-2.2 MeV energy range, for a period lasting more than three days.
S. Dalla +5 more
doaj +1 more source
Journal of High Energy Physics, 2023 Missing kinematic information of known invisible particles, such as neutrinos, limit several high-energy physics analysis. The undetected particle carries away momentum and energy information, preventing the total reconstruction of such an event.
Thomas M. G. Kraetzschmar +6 more
doaj +1 more source
Event shapes and jets in $e^{+}e^{-}$ and pp collisions [PDF]
, 2021 In high energy particle collisions the shape of the event, i.e. the relative distribution of particles in momentum space, is often used to try to select events with certain topologies. It is claimed that an event shape observable like transverse sphericity is able to discriminate between jet-like events and events that are dominated by soft production ...
arxiv +1 more source
Event Shape Selection Method in Search of the Chiral Magnetic Effect in Heavy-ion Collisions [PDF]
Physics Letters B, Volume 848, January 2024, 138367, 2023 The search for the chiral magnetic effect (CME) in heavy-ion collisions has been impeded by the significant background arising from the anisotropic particle emission pattern, particularly elliptic flow. To alleviate this background, the event shape selection (ESS) technique categorizes collision events according to their shapes and projects the CME ...
arxiv +1 more source
Event-by-Event Investigation of the Two-Particle Source Function in Heavy-Ion Collisions with EPOS
Entropy, 2022 Exploring the shape of the pair-source function for particles such as pions or kaons has been an important goal of heavy-ion physics, and substantial effort has been made in order to understand the underlying physics behind the experimental observations ...
Dániel Kincses +2 more
doaj +1 more source
International Particle Physics Masterclasses with LHC data
EPJ Web of Conferences, 2014 The International Particle Physics Masterclasses is an educational activity developed by the International Particle Physics Outreach Group with the aim to bring the excitement of cutting-edge particle-physics research into the classroom.
Foka Panagiota
doaj +1 more source
Energy flow networks: deep sets for particle jets
Journal of High Energy Physics, 2019 A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning efforts to learn ...
Patrick T. Komiske +2 more
doaj +1 more source