35 citations · 72 across the 11 of their papers we have counts for
6 papers · 1 filter
Robust resonant anomaly detection with NPLM
Gaia Grosso, Debajyoti Sengupta, Tobias Golling +1
In this study, we investigate the application of the New Physics Learning Machine (NPLM) algorithm as an alternative to the standard CWoLa method with Boosted Decision Trees (BDTs)…
Anomaly-aware summary statistic from data batches
Gaia Grosso
Signal-agnostic data exploration based on machine learning could unveil very subtle statistical deviations of collider data from the expected Standard Model of particle physics. Th…
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…