arXiv subjects

Jacopo Pazzini

Publications and source records attributed to Jacopo Pazzini.

11 recordsLinked to original sources

arXiv2026

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

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 in high-energy physics, with a focus on low-latency deployment on Field Programmable Gate Arrays (FPGAs). Motivated by the strict requirements of the HL-LHC Level-1 trigger system, we explore TNs as compact and interpretable alternatives to deep neural networks. Using low-level jet constituent features, our models achieve competitive performance compared to state-of-the-art deep learning classifiers. We investigate post-training quantization to enable hardware-efficient implementations without degrading classification performance or latency. The best-performing models are synthesized to estimate FPGA resource usage, latency, and memory occupancy, demonstrating sub-microsecond latency and supporting the feasibility of online deployment in real-time trigger systems. Overall, this study highlights the potential of TN-based models for fast and resource-efficient inference in low-latency environments.

cs.LG↗

arXiv2025

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

Matrix isolation spectroscopy enables probing atomic properties in controlled cryogenic environments. We present a spectroscopic study on neutral barium atoms embedded in a neon cryogenic crystal at 6.8 K, extending previous investigations performed in other noble gas hosts. The visible and near-infrared emission spectra were recorded under two different laser excitation schemes. First, 10-ns laser pulses at 355 nm were used to directly excite high-lying energy levels of barium, enabling the observation of fluorescence cascades. Second, a tunable continuous-wave laser operating between 700 nm and 900 nm allowed us to determine the matrix-induced shifts of barium energy levels relative to their vacuum values, as well as the inhomogeneous linewidths of the observed transitions and to perform lifetime measurements. Our results confirm multiple radiative pathways and matrix-induced relaxation channels affecting the 5d6s and 6s6p barium manifolds. Furthermore, we present the first lifetime measurement of the barium 5d6s 3D1 state in a neon crystal, yielding 0.39 \pm 0.02 s, with a predicted increase of about 10% at 2 K. The study of fluorescence and spectroscopic properties of barium isolated in neon represents an important step toward future searches for the electron electric dipole moment using barium monofluoride in neon matrices, where neutral barium atoms may act as unavoidable impurities and potential sources of background and systematic limitations.

physics.atom-ph↗

arXiv2024

Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA

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 (ML) tasks, yielding comparable results to standard supervised learning techniques. In this work, we study the use of Tree Tensor Networks (TTNs) in high-frequency real-time applications by exploiting the low-latency hardware of the Field-Programmable Gate Array (FPGA) technology. We present different implementations of TTN classifiers, capable of performing inference on classical ML datasets as well as on complex physics data. A preparatory analysis of bond dimensions and weight quantization is realized in the training phase, together with entanglement entropy and correlation measurements, that help setting the choice of the TTN architecture. The generated TTNs are then deployed on a hardware accelerator; using an FPGA integrated into a server, the inference of the TTN is completely offloaded. Eventually, a classifier for High Energy Physics (HEP) applications is implemented and executed fully pipelined with sub-microsecond latency.

hep-ex↗

arXiv2023

Triggerless data acquisition pipeline for Machine Learning based statistical anomaly detection

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 processing pipeline begins with a local reconstruction algorithm, employing neural networks on an FPGA as its first stage. Subsequent data preparation and anomaly detection stages are accelerated using GPGPUs. As a practical demonstration of anomaly detection, we have developed a data quality monitoring application using a cosmic muon detector. Its primary objective is to detect deviations from the expected operational conditions of the detector. This serves as a proof-of-concept for a system that can be adapted for use in large particle physics experiments, enabling anomaly detection on datasets with reduced bias.

hep-ex↗

arXiv2023

Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test

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 dataset, characterising the data behaviour under normal circumstances, via a likelihood-ratio hypothesis test. The model is based on a modern implementation of kernel methods, nonparametric algorithms that can learn any continuous function given enough data. The resulting approach is efficient and agnostic to the type of anomaly that may be present in the data. Our study demonstrates the effectiveness of this strategy on multivariate data from drift tube chamber muon detectors.

hep-ex↗

arXiv2023

A fast and flexible machine learning approach to data quality monitoring

We present a machine learning based approach for real-time monitoring of particle detectors. The proposed strategy evaluates the compatibility between incoming batches of experimental data and a reference sample representing the data behavior in normal conditions by implementing a likelihood-ratio hypothesis test. The core model is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. The resulting algorithm is fast, efficient and agnostic about the type of potential anomaly in the data. We show the performance of the model on multivariate data from a drift tube chambers muon detector.

hep-ex↗

arXiv2021

A horizontally scalable online processing system for trigger-less data acquisition

The majority of high energy physics experiments rely on data acquisition and hardware-based trigger systems performing a number of stringent selections before storing data for offline analysis. The online reconstruction and selection performed at the trigger level are bound to the synchronous nature of the data acquisition system, resulting in a trade-off between the amount of data collected and the complexity of the online reconstruction performed. Exotic physics processes, such as long-lived and slow-moving particles, are rarely targeted by online triggers as they require complex and nonstandard online reconstruction, usually incompatible with the time constraints of most data acquisition systems. The online trigger selection can thus impact as one of the main limiting factors to the experimental reach for exotic signatures. Alternative data acquisition solutions based on the continuous and asynchronous processing of the stream of data from the detectors are therefore foreseeable. Trigger-less data readout systems, paired with efficient streaming data processing solutions, can provide a viable alternative. In this document, an end-to-end implementation of a fully trigger-less data acquisition and online processing system is discussed. An easily scalable and deployable implementation of such an architecture is proposed, based on open-source distributed computing frameworks capable of performing asynchronous online processing of streaming data. The proposed schema can be suitable for deployment as a fully integrated system for small-scale experimental apparatus, or to complement the trigger-based data acquisition systems of larger experiments. A muon telescope setup consisting of a set of gaseous detectors is used as the experimental development testbed in this work, and a fully integrated online processing pipeline deployed on cloud computing resources is implemented and described.

physics.ins-det↗

arXiv2021

Muon trigger with fast Neural Networks on FPGA, a demonstrator

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 analytical algorithms are executed in a quasi-real-time environment to identify, select and reconstruct local tracks in often noise-rich environments. A novel approach to the generation of local triggers based on an hybrid combination of Artificial Neural Networks and analytical methods is proposed, targeting the muon reconstruction for drift tube detectors. The proposed algorithm exploits Neural Networks to solve otherwise computationally expensive analytical tasks for the unique identification of coherent signals and the removal of the geometrical ambiguities. The proposed approach is deployed on state-of-the-art FPGA and its performances are evaluated on simulation and on data collected from cosmic rays.

hep-ex↗

arXiv2019

Using Big Data Technologies for HEP Analysis

The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of information. The growing size of the datasets could potentially become a limiting factor in the capability to produce scientific results timely and efficiently. Recently, new technologies and new approaches have been developed in industry to answer to the necessity to retrieve information as quickly as possible to analyze PB and EB datasets. Providing the scientists with these modern computing tools will lead to rethinking the principles of data analysis in HEP, making the overall scientific process faster and smoother. In this paper, we are presenting the latest developments and the most recent results on the usage of Apache Spark for HEP analysis. The study aims at evaluating the efficiency of the application of the new tools both quantitatively, by measuring the performances, and qualitatively, focusing on the user experience. The first goal is achieved by developing a data reduction facility: working together with CERN Openlab and Intel, CMS replicates a real physics search using Spark-based technologies, with the ambition of reducing 1 PB of public data in 5 hours, collected by the CMS experiment, to 1 TB of data in a format suitable for physics analysis. The second goal is achieved by implementing multiple physics use-cases in Apache Spark using as input preprocessed datasets derived from official CMS data and simulation. By performing different end-analyses up to the publication plots on different hardware, feasibility, usability and portability are compared to the ones of a traditional ROOT-based workflow.

cs.DC↗

arXiv2015

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

This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches. A prioritized, compact set of benchmark models is proposed, accompanied by studies of the parameter space of these models and a repository of generator implementations. This report also addresses how to apply the Effective Field Theory formalism for collider searches and present the results of such interpretations.

hep-ex↗

arXiv2015

Measurement of CP Violation in $\mathrm{B}^{0}_{\mathrm{s}} \! \to \mathrm{J} / ψ\,ϕ$ decays with the CMS detector

The CP-violating weak phase $ϕ_\mathrm{s}$ and the decay width difference $ΔΓ_{\mathrm{s}}$ of $\mathrm{B}^{0}_{\mathrm{s}}$ mesons are measured by the CMS experiment at the LHC using a data sample of $\mathrm{B}^{0}_{\mathrm{s}} \! \to \mathrm{J} / ψ\,ϕ$ decays. The analysed dataset corresponds to an integrated luminosity of about $20~\mathrm{fb}^{-1}$ collected in $pp$ collisions at a centre-of-mass energy $\sqrt{s} = 8~\mathrm{TeV}$. A total of $49\,000$ reconstructed $\mathrm{B}^{0}_{\mathrm{s}}$ decays are used to extract the values $ϕ_\mathrm{s}$ and $ΔΓ_\mathrm{s}$ by performing a time-dependent and flavour-tagged angular analysis of the $μ^+μ^-\mathrm{K}^+\mathrm{K}^-$ final state.The weak phase is measured to be $ϕ_\mathrm{s} = -0.03 \pm 0.11~\mathrm{(stat.)} \pm 0.03~\mathrm{(syst.)}~\mathrm{rad}$, and the decay width difference between the $\mathrm{B}^{0}_{\mathrm{s}}$ mass eigenstates is $ΔΓ_\mathrm{s} = 0.096 \pm 0.014~\mathrm{(stat.)} \pm 0.007~\mathrm{(syst.)}~\mathrm{ps}^{-1}$.

hep-ex↗