3 citations · 3 across the 7 of their papers we have counts for
7 papers
Exploring ESSSB Near Water Cherenkov Detector Designs Through Graph Neural Network Flavour Identification
J. Aguilar, M. Anastasopoulos, D. Barčot +100
The ESSSB experiment aims to measure CP violation in the leptonic sector with high precision, necessitating robust reconstruction of neutrino events in the water Cherenkov (WC)…
Study of Supernova Neutrinos at ESSnuSB
ESSnuSB, :, J. Aguilar +97
In this paper, we have studied the sensitivity of the ESSnuSB far detector to supernova neutrinos. ESSnuSB is a proposed long-baseline neutrino experiment in Sweden, which will use…
Complementarity between atmospheric and super-beam neutrinos at ESSnuSB
ESSnuSB, :, J. Aguilar +92
The ESSnuSB experiment aims to measure the leptonic CP phase with an unprecedented resolution by probing neutrino oscillations at the second oscillation maximum. In the pr…
SMARTHEP: training PhD students in real-time analysis at the LHC and in industry
Johannes Albrecht, Laura Boggia, Leon Bozianu +18
In this invited Editorial for Software and Computing for Big Science, we describe the SMARTHEP Innovative Training Network funded via the Marie Skłodowska-Curie Actions between 202…
Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
Laura Boggia, Carlos Cocha, Fotis Giasemis +11
The field of high energy physics (HEP) has seen a marked increase in the use of machine learning (ML) techniques in recent years. The proliferation of applications has revolutionis…
Summary of the trigger systems of the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
Johannes Albrecht, Leon Bozianu, Lukas Calefice +11
In modern High Energy Physics (HEP) experiments, triggers perform the important task of selecting, in real time, the data to be recorded and saved for physics analyses. As a result…
GraphNeT: Graph neural networks for neutrino telescope event reconstruction
Andreas Søgaard, Rasmus F. Ørsøe, Leon Bozianu +6
GraphNeT is an open-source python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks at neutrino telescopes using gr…