Conveners
🔀 Datasets & Ethics
- David Rousseau (IJCLab-Orsay)
There are currently many Cherenkov neutrino telescopes being deployed and designed across the world. These detectors are exploring new optical sensors and geometric configurations to maximize their physics goals. Alongside detector R&D, machine learning (ML) has become established as a promising avenue for reconstructions in these detectors; however, there has not been a consistent comparison...
Anomaly detection in multivariate time series is crucial to ensure the quality of data coming from a physics experiment. Accurately identifying the moments when unexpected errors or defects occur is essential but challenging, as the types of anomalies are unknown beforehand and reliably labeled data is scarce. Additionally, the multi-dimensional nature of time series data adds to the problem’s...
The storage, transmission and processing of data is a major challenge across many fields of physics and industry. Traditional generic data compression techniques are lossless, but are limited in performance and require additional computation.
BALER [1,2] is an open-source autoencoder-based framework for the development of tailored lossy data compression models suitable for data from...
Learning from heterogeneous data is one of the major challenges for AI in the coming years, particularly for what are called 'foundation models'. In fundamental physics, the heterogeneity of data can come from the instruments used to acquire them (the subsystems of large detectors in particle physics or the crossing of modalities in multi-messenger, multi-instrument systems in astrophysics,...
This work presents an open framework for generating synthetic transactional datasets, addressing the twin challenges of data scarcity and privacy in fraud research. Conducted as an industry secondment at IBM France Lab Saclay within the SMARTHEP Network—a European project fostering collaboration between High Energy Physics and Industry—our approach leverages Generative AI Agents to simulate...
High-purity germanium (HPGe) detectors play a critical role in nuclear physics experiments, including searches for neutrinoless double-beta decay. Traditional pulse shape discrimination (PSD) methods help distinguish signal from background events in such detectors. However, the performance of these traditional PSD methods declines at lower energies (500 ≲ keV). This low-energy regime is...
