35 citations · 72 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Goodness of fit by Neyman-Pearson testing
Gaia Grosso, Marco Letizia, Maurizio Pierini +1
The Neyman-Pearson strategy for hypothesis testing can be employed for goodness of fit if the alternative hypothesis is selected from data by exploring a rich parametrised family o…
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…