UQ4ML | COMETA Workshop on Uncertainty Quantification for Machine Learning

Session

HEP - Theory

UQ4ML/20250916/hep-th

Sep 16, 2025, 9:30 AM

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

Conveners

HEP - Theory: Keynote

• Marco Letizia

HEP - Theory: Talks

• Marco Letizia

41. Machine learning for optimal BSM sensitivity

Giuliano Panico (University of Florence and INFN Florence)

9/16/25, 9:30 AM

HEP - Theory

Keynote

I will review the parametrized classifiers for optimizing the sensitivity to EFT operators and some the machine-learning approaches for general anomaly detection. Particular attention will be devoted to validation procedures and ways to treat uncertainties.

31. Determination and validation of modeling and theory uncertaintie

Stefano Forte (Università degli Studi e INFN Milano (IT))

9/16/25, 11:00 AM

HEP - Theory

Short-talk

I discuss how uncertainties related to machine learning modeling of a regression problem, as well as those related to missing theoretical information, can be estimated and subsequently validated. Even though these uncertainties are intrinsically Bayesian, given that there is only one underlying true theory and true model, they can be determined both in a Bayesian and frequentist framework. I...

32. Parton Distributions from Neural Networks: Analytical Results

Amedeo Chiefa

9/16/25, 11:30 AM

HEP - Theory

Short-talk

Parton Distribution Functions (PDFs) play a crucial role in describing experimental data at hadron colliders and provide insight into proton structure. As the LHC enters an era of high-precision measurements, a robust PDF determination with a reliable uncertainty quantification has become increasingly important to match the experimental precision. The NNPDF collaboration has pioneered the use...

33. Energy Flow Networks for Jet Quenching Studies

João A. Gonçalves (LIP - IST)

9/16/25, 12:00 PM

HEP - Theory

Short-talk

The phenomena of Jet Quenching, a key signature of the Quark-Gluon Plasma (QGP) formed in Heavy-Ion (HI) collisions, provides a window of insight into the properties of the primordial liquid. In this study, we evaluate the discriminating power of Energy Flow Networks (EFNs), enhanced with substructure observables, in distinguishing between jets stemming from proton-proton (pp) and jets...