Conveners
Generative
- There are no conveners in this block
Generative
- Frank-Dieter Gaede (Deutsches Elektronen-Synchrotron (DE))
The development of precise and computationally efficient simulations is a central challenge in modern physics. With the advent of deep learning, new methods are emerging from the field of generative models. Recent applications to the generation of calorimeter images showed promising results motivating the application in astroparticle physics. In this contribution, we introduce a...
The properties of hot and/or dense nuclear matter are studied in the laboratory via Heavy-Ion Collisions (HIC) experiments. Of particular interest are the intermediate energy heavy-ion collisions that create strongly interacting matter of moderate temperatures and high densities where interesting structures in the QCD phase diagram such as a first order phase transition from a gas of hadrons...
caloutils is a Python package built to simplify and streamline the handling, processing, and analysis of 4D point cloud data derived from calorimeter showers in high-energy physics experiments. The package includes tools to map between continuous point clouds and discrete calorimeter cells.
Furthermore, the library contains models for evaluating the performance of generative models of...
In this talk I will present a recent strategy to perform a goodness-of-fit test via two-sample testing, powered by machine learning. This approach allows to evaluate the discrepancy between a data sample of interest and a reference sample, in an unbiased and statistically sound fashion. The model leverages the ability of classifiers to estimate the density ratio of the data-generating...
I will summarize the results of the CaloChallenge, a HEP community challenge on generating calorimeter showers with deep generative models that took place in 2022/2023.
