activity
20142026
most citedDark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum

411 citations · 480 across the 15 of their papers we have counts for

collaborators

17 papers

physics.atom-ph2026

A cryogenic apparatus with a rotatable substrate for matrix isolation spectroscopy of BaF in neon crystals down to 3.2 K

Alessandro Lippi, Diego Bisero, Caterina Braggio +7

We present a new experimental apparatus for matrix isolation spectroscopy capable of growing cryogenic crystal matrices doped with barium monofluoride (BaF), a candidate for measur…

cs.LG2026

Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAs

Alberto Coppi, Ema Puljak, Lorenzo Borella +6

We present a systematic study of Tensor Network (TN) models $\unicode{x2013}$ Matrix Product States (MPS) and Tree Tensor Networks (TTN) $\unicode{x2013}$ for real-time jet tagging…

physics.atom-ph2025

Neutral Barium in Solid Neon: Optical Spectroscopy and First Excited State Lifetime

Alessandro Lippi, Giovanni Carugno, Roberto Calabrese +5

Matrix isolation spectroscopy enables probing atomic properties in controlled cryogenic environments. We present a spectroscopic study on neutral barium atoms embedded in a neon cr…

hep-ex2024★ 1 cited

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…

hep-ex2023

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…

hep-ex2023

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…