49 citations
- Institute for High Energy PhysicsES3 papers
- University of GenevaCH3 papers
- Barcelona Institute of Science and TechnologyES1 paper
- ETH ZurichCH1 paper
- Eurac ResearchIT1 paper
- European Organization for Nuclear ResearchCH1 paper
- Institució Catalana de Recerca i Estudis AvançatsES1 paper
- Institute for Particle Physics and AstrophysicsCH1 paper
- Instituto de Astrofísica de CanariasES1 paper
- Universidad Carlos III de MadridES1 paper
- Universidad de La LagunaES1 paper
- Universitat Politècnica de CatalunyaES1 paper
6 papers
Scientific CMOS sensors in Astronomy: IMX455 and IMX411
Miguel R. Alarcon, Javier Licandro, Miquel Serra-Ricart +3
Scientific complementary metal-oxide-semiconductor (CMOS) detectors have developed quickly in recent years thanks to their low cost and high availability. They also have some advan…
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…
Optimal regularity of stable solutions to nonlinear equations involving the -Laplacian
Xavier Cabre, Pietro Miraglio, Manel Sanchon
We consider the equation in a smooth bounded domain of , where is the -Laplace operator. Explicit examples of unbounded stable energy solution…
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…
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…
Fundamentals of the Holomorphic Embedding Load-Flow Method
Antonio Trias
The Holomorphic Embedding Load-Flow Method (HELM) was recently introduced as a novel technique to constructively solve the power-flow equations in power grids, based on advanced co…