IN2P3/IRFU Machine Learning workshop 2026

Session

Event/Object Reconstruction and Identification

18 nov. 2026, 09:00

Amphitheatre (LPSC)

Description

Machine learning methods for reconstructing particles, events, and physical observables from detector data.

Examples include:
- Particle and event reconstruction
- Tracking and clustering
- Jet tagging and particle identification
- Multi-detector information fusion
- Reconstruction in high-granularity detectors

210. GammaLearn: deep-learning event reconstruction for the CTAO - Results and Lessons learned between 2017 and 2026

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

18/11/2026 09:00

Event/Object Reconstruction and Identification

Standard talks

Since 2017, GammaLearn has developed deep-learning event reconstruction for Imaging Atmospheric Cherenkov Telescopes. Our main target is LST-1, the first Large-Sized Telescope of CTAO. One multi-task convolutional network, γ-PhysNet, estimates the particle type, energy and arrival direction of each event from the calibrated camera image and pixel timing.

From indexed convolution on...

199. Machine Learning for event reconstruction with uncertainty quantification in the ClearMind ultra-fast detector

Dr Théo Kaprélian (Université Paris-Saclay, CEA, Service de Génie Logiciel pour la Simulation, Gif-surYvette, 91191, Franc)

18/11/2026 09:20

Event/Object Reconstruction and Identification

Standard talks

The ClearMind collaboration [1] aims to develop a gamma detector with a coincidence resolving time below 100 ps. The detector combines a large monolithic crystal with a photocathode deposited directly on its surface and coupled to a microchannel plate photomultiplier tube (MCP-PMT). The opposite face is instrumented with silicon photomultipliers (SiPMs) to improve depth of interaction (DOI)...

207. Transformers for reconstruction of electromagnetic showers in calorimeters

Yuliia Maidannyk (CEA-IRFU)

18/11/2026 09:40

Event/Object Reconstruction and Identification

Standard talks

The reconstruction of electrons and photons in the CMS Electromagnetic Calorimeter (ECAL) currently relies on a geometrical clustering algorithm called PFClustering. While it is efficient for isolated particles, it has a limited ability to resolve close-by showers and mitigate detector noise, which reduces the sensitivity of physics analyses and will worsen with detector ageing. We present...

182. Upgrade II LHCb calorimeter real-time reconstruction with GNN

Aniol Lobo Salvia (CNRS - LAPP)

18/11/2026 10:00

Event/Object Reconstruction and Identification

Standard talks

Upgrade II constitutes a major leap forward at LHCb towards more and better data, driven by new hardware and software that will enable an ambitious increase of the current instant luminosity, thus, the experiment's physics reach. In particular, the calorimeter will be replaced by the new branded PicoCal, featuring better granularity and timing capabilities.
A significant challenge will be to...

204. SPINE for DUNE

Nived PUTHUMANA MELEPPATTU (IJCLab)

18/11/2026 10:20

Event/Object Reconstruction and Identification

Standard talks

SPINE (Scalable Particle Imaging with Neural Embeddings) is a transformative, ML-native approach to reconstruction and is rapidly becoming a common reconstruction pipeline across LArTPC experiments. Building on its strong performance in ICARUS, SBND and ND-LAr, we are extending SPINE to the DUNE Far Detector and its two large-scale prototypes at CERN. This is not a direct port of existing...

211. A machine learning approach to stellar stream detection in photometric surveys

Matthieu Pelissier (LPSC/UGA)

18/11/2026 10:40

Event/Object Reconstruction and Identification

Lightning talks

Stellar streams are faint remnants of disrupted dwarf galaxies or globular clusters, orbiting within our Galaxy's halo. These low-surface-brightness structures can be detected in photometric surveys such as DES and LSST, but detection has so far relied on visual inspection. We aim to automate this process using a machine learning approach, trained on simulated streams injected with the...