Présidents de session
Deep Learning for Detector Signal Reconstruction and Calibration
- Karl HAUSCHILD (IJCLab)
- Thomas Vuillaume (LAPP, Univ. Savoie Mont-Blanc, CNRS)
Accurate energy calibration of calorimeters is essential for the physics goals of collider experiments, particularly at the CERN Large Hadron Collider. Conventional calibration strategies encounter growing limitations as calorimeter granularity increases. We propose a novel calibration method that simultaneously calibrates individual detector cells within a particle shower by targeting a...
Within the CTAO collaboration, GammaLearn is a project to develop deep learning solutions for the event reconstruction of Imaging Atmospheric Cherenkov Telescopes directly from the acquired images. Previous work demonstrated very good performances of the developed architecture network ganma-PhysNet on simulated and real data in constrained conditions. However, image acquisition covers...
Searching for new physics often requires testing many different signal hypotheses across an extensive parameter space, such as signal mass or width. Traditional approaches typically involves the training of one classifier per hypothesis, which quickly becomes impractical when scanning over a broad range of parameters. At higher masses, where event yields are low, limited training data leads to...
Inverse problems in astronomy are often computationally expensive, and Markov Chain Monte Carlo (MCMC) routines become impractical for massive, heterogeneous datasets or when only limited data are available. With the advent of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the astronomical community will receive millions of transient alerts daily, many of which evolve on...
Maximizing the scientific discovery rate in complex modern experiments demands advanced data analysis and real-time control. We present a suite of recently developed Artificial Intelligence (AI) and Machine Learning (ML) applications that can transform the precision and efficiency of experimental work at GANIL.
Our efforts are focused on two critical areas:
- Deep Neural Networks...
We present a hybrid machine-learning framework that combines high-accuracy numerical regression with symbolic regression to model and interpret nuclear charge radii. Using Light Gradient Boosting and Gaussian Process Regression with rigorous cross-validation, the method reproduces experimental trends across the nuclear chart and distills them into simple analytical expressions. These formulas...
The SVOM satellite mission, launched in June 2024 is dedicated to Gamma-Ray Burst (GRBs) studies. The ECLAIRs trigger onboard SVOM, which reorients the satellite for GRB follow-up observations, also provides a real-time Alert Sequence for each detected GRB, transmitted to ground over the SVOM VHF receiver network. One of the two trigger algorithms, the Image Trigger (IMT) transmits at the end...
