5 papers
Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders
Raphaël Bonnet-Guerrini, Johann Ioannou-Nikolaides, Inar Timiryasov +1
We present a first application of sparse-autoencoder-based mechanistic interpretability to particle physics. Studying a neutrino foundation model pretrained on IceCube data and fin…
Multiclass Classification without Labels via Posterior Simplex Geometry
Raphaël Bonnet-Guerrini, Johann Ioannou-Nikolaides, Troels Petersen +1
In many classification problems, reliable instance-level labels are unavailable. However, it is often possible to construct weakly enriched unlabeled samples: datasets selected by…
Interpreting Parton Distributions with Shapley Values
Raphaël Bonnet-Guerrini, Stefano Carrazza, Stefano Forte +3
We show that Shapley values can be used to trace how individual parton distributions (PDFs) shape the theory predictions for high-energy observables computed from them. This provid…
Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
Raphaël Bonnet-Guerrini, Bruno Sanchez, Dominique Fouchez +5
Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Reliable labels are costly, while communi…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…