35 citations · 72 across the 11 of their papers we have counts for
12 papers
Learning to Validate Generative Models: a Goodness-of-Fit Approach
Pietro Cappelli, Gaia Grosso, Marco Letizia +2
Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous…
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)…
Multiple testing for signal-agnostic searches of new physics with machine learning
Gaia Grosso, Marco Letizia
In this work, we address the question of how to enhance signal-agnostic searches by leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on ma…
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…
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…
Learning new physics efficiently with nonparametric methods
Marco Letizia, Gianvito Losapio, Marco Rando +5
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, n…
Learning New Physics from an Imperfect Machine
Raffaele Tito d'Agnolo, Gaia Grosso, Maurizio Pierini +2
We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds di…
Learning Multivariate New Physics
Raffaele Tito D'Agnolo, Gaia Grosso, Maurizio Pierini +2
We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets. Our data analysis strategy does not rely on any…