UQ4ML | COMETA Workshop on Uncertainty Quantification for Machine Learning

Session

HEP - Experiment

UQ4ML/20250916/hep-ex

Sep 16, 2025, 2:00 PM

Amphithéâtre Claude Bloch (IPhT) (CEA Paris-Saclay)

Conveners

HEP - Experiment: Keynote

• Shamik Ghosh (Centre National de la Recherche Scientifique (FR))

HEP - Experiment: Talks

• Shamik Ghosh (Centre National de la Recherche Scientifique (FR))

47. Uncertainty quantification for deep learning in astroparticle physics

Christian Glaser (Uppsala University)

9/16/25, 2:00 PM

HEP - Experiment

Keynote

In this contribution I will review the use cases of uncertainty quantification with deep learning in high-energy astroparticle physics. Among other things, I will present the combination of neural networks with conditional normalizing flows to predict the Posterior for all quantities of interest. This Ansatz can be further expanded with the snowstorm method developed by the IceCube...

40. Interdisciplinary Digital Twin Engine InterTwin for calorimeter simulation

Vera Maiboroda (CNRS, IJCLab)

9/16/25, 3:30 PM

HEP - Experiment

Short-talk

The interTwin project develops an open-source Digital Twin Engine to integrate application-specific Digital Twins (DTs) across scientific domains. Its framework for the development of DTs supports interoperability, performance, portability and accuracy. As part of this initiative, we implemented the CaloINN normalizing-flow model for calorimeter simulations within the interTwin framework....

45. Scaling laws for amplitude surrogates

Joaquin Iturriza Ramirez (Centre National de la Recherche Scientifique (FR))

9/16/25, 4:00 PM

Deep Learning and Uncertainty Quantification

Short-talk

Fast and precise evaluations of scattering amplitudes even in the case of precision calculations is essential for event generation tools at the HL-LHC. We explore the scaling behavior of the achievable precision of neural networks in this regression problem for multiple architectures, including a Lorentz symmetry aware multilayer perceptron and the L-GATr architecture. L-GATr is equivariant...

37. Fair Universe HiggsML Uncertainty Challenge

Ragansu Chakkappai (IJCLab-Orsay)

9/16/25, 4:30 PM

HEP - Experiment

Short-talk

The Fair Universe project organised the HiggsML Uncertainty Challenge, which took place from 12th September 2024, to 14th March 2025. This groundbreaking competition in high-energy physics (HEP) and machine learning was the first to strongly emphasis on uncertainties, focusing on mastering both the uncertainties in the input training data and providing credible confidence intervals in the...