ML4Jets2023

Session

Generative

9 Nov 2023, 10:00

Main Auditorium (DESY)

Conveners

Generative

• There are no conveners in this block

Generative

• Frank-Dieter Gaede (Deutsches Elektronen-Synchrotron (DE))

Presentation materials

Zoom link

104. Ultra-fast generation of Air Shower Images for Imaging Air Cherenkov Telescopes with Generative Adversarial Networks

Christian Elflein (Erlangen Centre for Astroparticle Physics)

09/11/2023, 10:00

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...

113. ParticleGrow: Event by event simulation of heavy-ion collisions via autoregressive point cloud generation

Manjunath Omana Kuttan (Frankfurt Institute for Advanced Studies)

09/11/2023, 10:15

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...

102. caloutils - Utilities and Metrics for Generative Models of Calorimeter Showers

Mr Moritz Scham (Deutsches Elektronen-Synchrotron (DE))

09/11/2023, 11:00

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...

67. Understanding generative networks via classifier weight distributions

Luigi Favaro

09/11/2023, 11:15

Well-trained classifiers and their complete weight distributions provide us with a well motivated and practicable method to test generative networks in particle physics. I will illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction level events. In all cases, the classifier weights make for a powerful test of the generative network, identify potential...

38. Level up your performance calculation of the fast shower simulation model

Anna Zaborowska (CERN)

09/11/2023, 11:30

Due to the large computing resources spent on the detailed (full) simulation of particle transport in the HEP experiments, many efforts have been undertaken to parametrise the detector response. In particular, particle showers developing in the calorimeters are typically the most time-consuming component of simulation, hence their parameterisation is of primary focus.

Fast shower simulation...

29. The New Physics Learning Machine: machine learning for goodness-of-fit via Neyman—Pearson testing

Dr Marco Letizia (MaLGa Center, Università di Genova and INFN)

09/11/2023, 11:45

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...

33. The Fast Calorimeter Simulation Challenge 2022

Dr Claudius Krause (Rutgers University)

09/11/2023, 12:00

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.