IN2P3/IRFU Machine Learning workshop 2026

Session

AI for Physics Analysis, Inference and Discovery

17 nov. 2026, 10:55

Amphitheatre (LPSC)

Présidents de session

AI for Physics Analysis, Inference and Discovery: Part 1

• Thomas Vuillaume (LAPP, Univ. Savoie Mont-Blanc, CNRS)

AI for Physics Analysis, Inference and Discovery: Part 2

• Thomas Vuillaume (LAPP, Univ. Savoie Mont-Blanc, CNRS)

AI for Physics Analysis, Inference and Discovery: Part 3

• Thomas Vuillaume (LAPP, Univ. Savoie Mont-Blanc, CNRS)

Description

Machine learning methods supporting scientific measurements, interpretation, and discoveries.

Examples include:
- Statistical inference
- Parameter estimation
- New physics searches
- Scientific time-series analysis
- Event classification
- Scientific interpretation

219. Accelerating Weather Forecasts with Artificial Intelligence

Dr Mikaël Jacquemont (Bull SAS)

17/11/2026 10:55

AI for Physics Analysis, Inference and Discovery

Invited talk

Until very recently, weather forecasting almost only relied on solving physical equations using numerical solvers called numerical weather prediction (NWP) models. Modelling the atmosphere faster and at higher precision was made possible thanks to access to more computing power through the evolution of High-Performance Computing (HPC) clusters.
The rise of Artificial Intelligence (AI), more...

185. emulating accurately and consistently the cosmological power spectrum and the halo mass function

johan comparat

17/11/2026 11:25

AI for Physics Analysis, Inference and Discovery

Standard talks

Cosmological forecasts and likelihood analyses increasingly need two ingredients at once: the linear matter power spectrum, and the abundance of dark matter haloes. Computing them exactly is far too slow for parameter inference, and computing them from unrelated approximations quietly introduces inconsistencies between the two.

In this presentation, I will introduce two emulators built to...

192. Speeding-up the search for anomalies in ZTF time series with interpretable machine learning

Emmanuel Gangler (LPC)

17/11/2026 11:45

AI for Physics Analysis, Inference and Discovery

Standard talks

Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the astronomer is left to independently analyze the data in order to understand why a given event is tagged as an anomaly. Interpretable AI on the other hand provides clues to the analyst, often in the...

227. AI/ML for large galaxy survey analyses

Agnès Ferté (LPNHE)

17/11/2026 12:05

Standard talks

Large galaxy surveys such as the Dark Energy Survey (DES) or the new LSST from the Vera C. Rubin Observatory offer several cosmological probes to test dark energy and gravity. The combination of galaxy clustering and weak lensing has proven to be a powerful probe, especially in combination with complementary probes such as galaxy clusters, supernovae and the baryonic acoustic oscillation peak....

213. Revisiting Nuclear Cross Sections for Cosmic Rays with Gaussian Processes

Anthony Correia (LPSC,IN2P3/CNRS/UGA)

17/11/2026 12:25

AI for Physics Analysis, Inference and Discovery

Lightning talks

Inelastic and production cross sections are crucial inputs for predicting nuclear cosmic-ray fluxes in the Galaxy. Deuterium (²H) and helium-3 (³He) are both expected to be produced solely by interactions of heavier nuclei with the interstellar gas, yet recent AMS-02 and Voyager data are in tension with this picture. In this context, it is natural to revisit the relevant nuclear cross...

191. Physics-informed impedance-model-agnostic reconstruction of point-charge wakefields from finite bunch electromagnetic simulations

Keon Hee Kim (CNRS/GANIL)

17/11/2026 12:30

AI for Physics Analysis, Inference and Discovery

Lightning talks

Electromagnetic wakefield simulations generally provide wake potentials excited by finite length bunches, whereas beam dynamics studies require the underlying point-charge wake and broadband impedance. Direct access to the THz impedance regime in full 3D electromagnetic simulations typically requires extremely short drive bunches and very fine numerical resolution. The resulting simulations...

215. Hybrid Latent-Space Inference for Galaxy Deblending

Anna Preto

17/11/2026 14:50

AI for Physics Analysis, Inference and Discovery

Standard talks

In the absence of systematic errors, weak lensing is among the most constraining probes of dark energy. Blending is a major source of systematic uncertainty for weak-lensing analyses, affecting both galaxy shape measurements and photometric redshifts. At LSST depth, blending is estimated to affect about 62% of detectable galaxies. Deblending therefore requires assigning the observed flux among...

201. Accurate and efficient simulation-based inference for massive black-hole binaries with LISA

Alice Spadaro (L2I Toulouse, CNRS/IN2P3, Université de Toulouse)

17/11/2026 15:10

AI for Physics Analysis, Inference and Discovery

Standard talks

The Laser Interferometer Space Antenna (LISA) is a future space-based gravitational-wave observatory designed to detect low-frequency gravitational waves, including those produced by massive black-hole binaries. I will present a machine-learning framework based on normalizing flows for rapid Bayesian parameter estimation of these sources. The method is trained on simulated LISA signals and can...

205. MODULARITY: A Modular Architecture for Distributed Autonomous Scientific Discovery

Hayg Guler (IJCLAB)

17/11/2026 15:30

AI for Physics Analysis, Inference and Discovery

Standard talks

Modern scientific facilities increasingly combine complex instrumentation, simulations, heterogeneous diagnostics, and machine-learning-based control. We propose MODULARITY, a domain-agnostic architecture for distributed closed-loop experimentation, where complex systems are decomposed into interoperable autonomous modules. Each module combines observations, control variables, constraints,...

212. Machine Learning-Based Event Categorisation for Higgs-to-Dimuon Analyses

Somanko Saha

17/11/2026 15:50

AI for Physics Analysis, Inference and Discovery

Lightning talks

The search for the Higgs boson decay to a pair of muons provides a direct probe of the Higgs coupling to second-generation fermions and represents an important test of the Standard Model. Due to the small branching fraction of this decay and the large backgrounds from Drell–Yan and other processes, achieving high sensitivity requires efficient exploitation of the kinematic and event-level...

218. Self Organised Randoms for correcting systematic effects in Euclid photometric galaxy clustering.

Dr Jérôme Odier (LPSC Grenoble)

17/11/2026 16:30

AI for Physics Analysis, Inference and Discovery

Standard talks

The Euclid satellite is designed to make accurate measurements of weak lensing and galaxy clustering for the purpose of probing structure formation over half of the age of the Universe. However, spatial variations in the Euclid survey and instrumental properties, as well as on the foreground sky emission, can affect photometric galaxy clustering and bias cosmological results. To correct for...

195. LIS² (Large Image Split Segmentation): A Modular Research Software Framework for Deep Learning on Large-Scale Scientific Images

Dr Dominique Benielli (Aix Marseille Univ, CNRS, LIS, Marseille, France)

17/11/2026 16:50

Event/Object Reconstruction and Identification

Standard talks

The automatic detection of interstellar filaments in large-scale astrophysical surveys is a challenging segmentation problem for deep learning methods. Observations cover large portions of the Galactic plane and exhibit a wide range of densities and contrasts, spatially varying noise, and a heterogeneous distribution of structures. In addition, available annotations are necessarily incomplete,...

221. Cosmology using topological data analysis with SBI

Cyrille Doux (LPSC)

17/11/2026 17:10

AI for Physics Analysis, Inference and Discovery

Standard talks

In this talk I will present a cosmological analysis of weak lensing data from the Dark Energy Survey using a topological descriptors, persistent homology, within a simulation-based inference framework. This analysis captures cosmological information from the field beyond Gaussian, complementing standard two-point correlation functions.

214. Lorentz-Equivariant Transformers for ttX multi-lepton final state at the LHC

JINU RAJ

17/11/2026 17:30

AI for Physics Analysis, Inference and Discovery

Lightning talks

Rare top-quark processes, ttW,ttH,tH,ttZ,tttt, are traditionally measured in separate analyses, despite substantial overlap among their multilepton final states. However, this strongly limits the overall physics understanding, as the correlations between the various measurements are large and poorly known. We propose a new approach to tackle these limitations by simultaneously reconstructing...