119 citations · 883 across the 34 of their papers we have counts for
35 papers
Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN
Patryk Będkowski, Jan Dubiński, Kamil Deja +1
Simulating detector responses is a crucial part of understanding the inner-workings of particle collisions in the Large Hadron Collider at CERN. The current reliance on statistical…
Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN
Mikołaj Kita, Jan Dubiński, Przemysław Rokita +1
In High Energy Physics simulations play a crucial role in unraveling the complexities of particle collision experiments within CERN's Large Hadron Collider. Machine learning simula…
Particle physics DL-simulation with control over generated data properties
Karol Rogoziński, Jan Dubiński, Przemysław Rokita +1
The research of innovative methods aimed at reducing costs and shortening the time needed for simulation, going beyond conventional approaches based on Monte Carlo methods, has bee…
Particle identification with machine learning from incomplete data in the ALICE experiment
Maja Karwowska, Łukasz Graczykowski, Kamil Deja +2
The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle iden…
Machine-learning-based particle identification with missing data
Miłosz Kasak, Kamil Deja, Maja Karwowska +3
In this work, we introduce a novel method for Particle Identification (PID) within the scope of the ALICE experiment at the Large Hadron Collider at CERN. Identifying products of u…
Looking through the past: better knowledge retention for generative replay in continual learning
Valeriya Khan, Sebastian Cygert, Kamil Deja +2
In this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Current generative rehearsal methods are usually benchmarke…