29 citations · 61 across the 6 of their papers we have counts for
8 papers
Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors
Roberto Moretti, Marco Rossi, Matteo Biassoni +6
The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by c…
Deep Learning Applications to Particle Physics: from Monte Carlo simulation acceleration to ProtoDUNE reconstruction
Marco Rossi
The thesis arises in the context of deep learning applications to particle physics. The dissertation follows two main parallel streams: the development of hardware-accelerated tool…
MadFlow: automating Monte Carlo simulation on GPU for particle physics processes
Stefano Carrazza, Juan Cruz-Martinez, Marco Rossi +1
We present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelera…
Towards the automation of Monte Carlo simulation on GPU for particle physics processes
Stefano Carrazza, Juan Cruz-Martinez, Marco Rossi +1
In this proceedings we present MadFlow, a new framework for the automation of Monte Carlo (MC) simulation on graphics processing units (GPU) for particle physics processes. In orde…
Deep Learning strategies for ProtoDUNE raw data denoising
Marco Rossi, Sofia Vallecorsa
In this work, we investigate different machine learning-based strategies for denoising raw simulation data from the ProtoDUNE experiment. The ProtoDUNE detector is hosted by CERN a…
PDFFlow: hardware accelerating parton density access
Marco Rossi, Stefano Carrazza, Juan M. Cruz-Martinez
We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calcula…