arXiv subjects

Francois Drielsma

Publications and source records attributed to Francois Drielsma.

4 recordsLinked to original sources

arXiv2026

Future of Artificial Intelligence for Science in Japan 2024 Community Report

This white paper summarizes scientific challenges and AI/ML research opportunities identified through the FAIRS Japan 2024 unconference process. The discussion focuses on three major physics domains: accelerator physics, cosmology and astrophysics, and neutrino physics. Although each domain has distinct scientific goals and experimental constraints, several common technical themes emerge: high-dimensional reconstruction, fast and accurate simulation, uncertainty propagation, simulation-to-data mismatch, anomaly detection, real-time decision-making, and shared infrastructure.

hep-ph↗

arXiv2021

Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors

Recent inroads in Computer Vision (CV) and Machine Learning (ML) have motivated a new approach to the analysis of particle imaging detector data. Unlike previous efforts which tackled isolated CV tasks, this paper introduces an end-to-end, ML-based data reconstruction chain for Liquid Argon Time Projection Chambers (LArTPCs), the state-of-the-art in precision imaging at the intensity frontier of neutrino physics. The chain is a multi-task network cascade which combines voxel-level feature extraction using Sparse Convolutional Neural Networks and particle superstructure formation using Graph Neural Networks. Each algorithm incorporates physics-informed inductive biases, while their collective hierarchy is used to enforce a causal structure. The output is a comprehensive description of an event that may be used for high-level physics inference. The chain is end-to-end optimizable, eliminating the need for time-intensive manual software adjustments. It is also the first implementation to handle the unprecedented pile-up of dozens of high energy neutrino interactions, expected in the 3D-imaging LArTPC of the Deep Underground Neutrino Experiment. The chain is trained as a whole and its performance is assessed at each step using an open simulated data set.

hep-ex↗

arXiv2020

Clustering of Electromagnetic Showers and Particle Interactions with Graph Neural Networks in Liquid Argon Time Projection Chambers Data

Liquid Argon Time Projection Chambers (LArTPCs) are a class of detectors that produce high resolution images of charged particles within their sensitive volume. In these images, the clustering of distinct particles into superstructures is of central importance to the current and future neutrino physics program. Electromagnetic (EM) activity typically exhibits spatially detached fragments of varying morphology and orientation that are challenging to efficiently assemble using traditional algorithms. Similarly, particles that are spatially removed from each other in the detector may originate from a common interaction. Graph Neural Networks (GNNs) were developed in recent years to find correlations between objects embedded in an arbitrary space. The Graph Particle Aggregator (GrapPA) first leverages GNNs to predict the adjacency matrix of EM shower fragments and to identify the origin of showers, i.e. primary fragments. On the PILArNet public LArTPC simulation dataset, the algorithm achieves achieves a shower clustering accuracy characterized by a mean adjusted Rand index (ARI) of 97.8 % and a primary identification accuracy of 99.8 %. It yields a relative shower energy resolution of $(4.1+1.4/\sqrt{E (\text{GeV})})\,\%$ and a shower direction resolution of $(2.1/\sqrt{E(\text{GeV})})^{\circ}$. The optimized algorithm is then applied to the related task of clustering particle instances into interactions and yields a mean ARI of 99.2 % for an interaction density of $\sim\mathcal{O}(1)\,m^{-3}$.

physics.ins-det↗

arXiv2017

Electron-Muon Ranger: hardware characterization

The Electron-Muon Ranger (EMR) is a fully-active tracking-calorimeter in charge of the electron background rejection downstream of the cooling channel at the international Muon Ionization Cooling Experiment. It consists of 2832 plastic scintillator bars segmented in 48 planes in an X-Y arrangement and uses particle range as its main variable to tag muons and discriminate electrons. An array of analyses were conducted to characterize the hardware of the EMR and determine whether the detector performs to specifications. The clear fibres coming from the bars were shown to transmit the desired amount of light, and only four dead channels were identified in the electronics. Two channels had indubitably been mismatched during assembly and the DAQ channel map was subsequently corrected. The level of crosstalk is within acceptable values for the type of multi-anode photomultiplier used with an average of $0.20\pm0.03\,\%$ probability of occurrence in adjacent channels and a mean amplitude equivalent to $4.5\pm0.1\,\%$ of the primary signal intensity. The efficiency of the signal acquisition, defined as the probability of recording a signal in a plane when a particle goes through it in beam conditions, reached $99.73\pm0.02\,\%$.

physics.ins-det↗