Conveners
Parallel 20: future facilities
- Jonghan Park (University of Tsukuba (JP))
-
Alexander Milov (Weizmann Institute of Science (IL))24/09/2024, 14:006. Future experimental facilities and new techniquesOral presentation
A new apparatus, NA60+, is proposed for measuring muon pairs in the center-of-mass region from 5 to 17 GeV at CERN SPS in a variety of collisional systems from Pb+Pb and down to p+Be. The physics scope of the new detector will cover topics from the measurement of thermal radiation coming from the hot and dense medium to chiral symmetry restoration, strangeness, and charm production.
The...
Go to contribution page -
Gunther Roland (Massachusetts Inst. of Technology (US))24/09/2024, 14:206. Future experimental facilities and new techniquesOral presentation
The intriguing phenomena emerging in the high-density quantum chromodynamics (QCD) matter are being widely studied in the heavy ion program at the LHC and will be understood more deeply during the high-luminosity LHC (HL-LHC) era. The CMS experiment is under the Phase 2 upgrade towards the HL-LHC era. Among others, a new timing detector is proposed with its timing resolution for minimum...
Go to contribution page -
Samuel Belin (Universidade de Santiago de Compostela (ES))24/09/2024, 14:406. Future experimental facilities and new techniquesOral presentation
Owing to its spectrometer acceptance, complementary to the other LHC ex-
Go to contribution page
periments, and to its excellent tracking and particle identification, LHCb has
been performing since the LHC Run2 a unique heavy-ion programme. By ex-
ploiting instead the injection of gases in the LHC accelerator beam-pipe, LHCb
has been simultaneously acquiring data in fixed-target mode. The sum of the
two... -
Alexandre Falcão (University of Bergen)24/09/2024, 15:006. Future experimental facilities and new techniquesOral presentation
To compare collider experiments, measured data must be corrected for detector distortions through a process known as unfolding. As measurements become more sophisticated, the need for higher-dimensional unfolding increases, but traditional techniques have limitations. To address this, machine learning-based unfolding methods were recently introduced. In this work, we introduce OmniFoldHI, an...
Go to contribution page