Conveners
New Detector and Reconstruction Methodologies, Machine Learning and Computing at HL-LHC: Block A (Europe / US East)
- Matthias Schott (CERN / University of Mainz)
- Chilufya Mwewa (Brookhaven National Laboratory (US))
- Alberto Belloni (University of Maryland (US))
New Detector and Reconstruction Methodologies, Machine Learning and Computing at HL-LHC: Block B (US West / Asia)
- There are no conveners in this block
The measurement of the trilinear Higgs self-coupling is among the primary goals of the high-luminosity LHC and is accessible in Higgs pair production. However, this process is also sensitive to other couplings. While most of them can be well-constrained by single-Higgs measurements, this does not hold true for the coupling between the Higgs and light quarks. We show that the Higgs pair...
We propose a new framework for automatic generation of simulated events for particle physics processes in high energy physics applications, for LHC and HL-LHC configurations, with automatic support for hardware accelerators, in particular, graphics processing units (GPU). The project focus on two specific problems: automate the generation of matrix elements and phase space terms for GPU and...
For a long time it was conjectured that the large azimuthal anisotropic flow observed in heavy-ion collisions implied early thermalization. After the discovery of azimuthal anisotropic flow in small systems, where it is unclear if there is time for the system to thermalize, it was realized that one can have hydrodynamization, a phenomena in which flow can build up out-of-equilibrium (chemical...
The fundamental theory of strong nuclear force, quantum chromodynamics, predicts the existence of an extreme state of matter dubbed quark--gluon plasma (QGP) under extreme values of temperature and/or baryon density, which are attainable in ultra-relativistic heavy-ion collisions at Large Hadron Collider. Properties of QGP can be inferred via its hydrodynamic response to the anisotropies in...
We propose a new framework for automatic generation of simulated events for particle physics processes in high energy physics applications, for LHC and HL-LHC configurations, with automatic support for hardware accelerators, in particular, graphics processing units (GPU). The project focus on two specific problems: automate the generation of matrix elements and phase space terms for GPU and...
The measurement of the trilinear Higgs self-coupling is among the primary goals of the high-luminosity LHC and is accessible in Higgs pair production. However, this process is also sensitive to other couplings. While most of them can be well-constrained by single-Higgs measurements, this does not hold true for the coupling between the Higgs and light quarks. We show that the Higgs pair...
For a long time it was conjectured that the large azimuthal anisotropic flow observed in heavy-ion collisions implied early thermalization. After the discovery of azimuthal anisotropic flow in small systems, where it is unclear if there is time for the system to thermalize, it was realized that one can have hydrodynamization, a phenomena in which flow can build up out-of-equilibrium (chemical...
The fundamental theory of strong nuclear force, quantum chromodynamics, predicts the existence of an extreme state of matter dubbed quark--gluon plasma (QGP) under extreme values of temperature and/or baryon density, which are attainable in ultra-relativistic heavy-ion collisions at Large Hadron Collider. Properties of QGP can be inferred via its hydrodynamic response to the anisotropies in...
Observation of the cosmic microwave background radiation (CMBR) by various satellites confirms the Big Bang evolution, inflation and provides important information regarding the early Universe and its evolution with excellent accuracy [1]. In little bangs, the produced fire-ball goes through a rapid evolution from partonic QGP phase to a hadronic phase, and finally freezes out. The physics of...
In recent years, deep learning techniques became highly popular in high energy physics experiments as they deliver very promising results not only in the context of signal classification, but also in the context of object reconstruction. The involved neural network architectures are typically rather complex and require therefore significant resources during the training phase. One possibility...
