411 citations · 480 across the 15 of their papers we have counts for
9 papers · 1 filter
Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA
Lorenzo Borella, Alberto Coppi, Jacopo Pazzini +4
Tensor Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning…
Triggerless data acquisition pipeline for Machine Learning based statistical anomaly detection
Gaia Grosso, Nicolò Lai, Matteo Migliorini +4
This work describes an online processing pipeline designed to identify anomalies in a continuous stream of data collected without external triggers from a particle detector. The pr…
Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test
Gaia Grosso, Nicolò Lai, Marco Letizia +5
We here propose a machine learning approach for monitoring particle detectors in real-time. The goal is to assess the compatibility of incoming experimental data with a reference d…
The Analytical Method algorithm for trigger primitives generation at the LHC Drift Tubes detector
G. Abbiendi, J. Alcaraz Maestre, A. Álvarez Fernández +143
The Compact Muon Solenoid (CMS) experiment prepares its Phase-2 upgrade for the high-luminosity era of the LHC operation (HL-LHC). Due to the increase of occupancy, trigger latency…
A fast and flexible machine learning approach to data quality monitoring
Gaia Grosso, Nicolò Lai, Marco Letizia +4
We present a machine learning based approach for real-time monitoring of particle detectors. The proposed strategy evaluates the compatibility between incoming batches of experimen…
Muon trigger with fast Neural Networks on FPGA, a demonstrator
Matteo Migliorini, Jacopo Pazzini, Andrea Triossi +2
The online reconstruction of muon tracks in High Energy Physics experiments is a highly demanding task, typically performed with programmable logic boards, such as FPGAs. Complex a…