25 citations · 27 across the 2 of their papers we have counts for
6 papers
SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics
Atul Kumar Sinha, Daniele Paliotta, Bálint Máté +4
Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors. Detailed simulation frameworks such as the gold standard Gea…
Funnels: Exact maximum likelihood with dimensionality reduction
Samuel Klein, John A. Raine, Sebastian Pina-Otey +2
Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model. We use the SurVAE framework to construct dimension reducing s…
Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors
Saúl Alonso-Monsalve, Dana Douqa, César Jesús-Valls +5
Deep learning tools are being used extensively in high energy physics and are becoming central in the reconstruction of neutrino interactions in particle detectors. In this work, w…
Exhaustive Neural Importance Sampling applied to Monte Carlo event generation
Sebastian Pina-Otey, Federico Sánchez, Thorsten Lux +1
The generation of accurate neutrino-nucleus cross-section models needed for neutrino oscillation experiments require simultaneously the description of many degrees of freedom and p…
Efficient sampling generation from explicit densities via Normalizing Flows
Sebastian Pina-Otey, Thorsten Lux, Federico Sánchez +1
For many applications, such as computing the expected value of different magnitudes, sampling from a known probability density function, the target density, is crucial but challeng…
Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows
Sebastian Pina-Otey, Federico Sánchez, Vicens Gaitan +1
In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression. We discuss the application of Neural…