arXiv subjects

Cristiano Fanelli

Publications and source records attributed to Cristiano Fanelli.

36 records · Page 2Linked to original sources

arXiv2024

Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Models

Imaging Cherenkov detectors are crucial for particle identification (PID) in nuclear and particle physics experiments. Fast reconstruction algorithms are essential for near real-time alignment, calibration, data quality control, and efficient analysis. At the future Electron-Ion Collider (EIC), the ePIC detector will feature a dual Ring Imaging Cherenkov (dual-RICH) detector in the hadron direction, a Detector of Internally Reflected Cherenkov (DIRC) in the barrel, and a proximity focus RICH in the electron direction. This paper focuses on the DIRC detector, which presents complex hit patterns and is also used for PID of pions and kaons in the GlueX experiment at JLab. We present Deep(er)RICH, an extension of the seminal DeepRICH work, offering improved and faster PID compared to traditional methods and, for the first time, fast and accurate simulation. This advancement addresses a major bottleneck in Cherenkov detector simulations involving photon tracking through complex optical elements. Our results leverage advancements in Vision Transformers, specifically hierarchical Swin Transformer and normalizing flows. These methods enable direct learning from real data and the reconstruction of complex topologies. We conclude by discussing the implications and future extensions of this work, which can offer capabilities for PID for multiple cutting-edge experiments like the future EIC.

physics.ins-det↗

arXiv2024

Towards a RAG-based Summarization Agent for the Electron-Ion Collider

The complexity and sheer volume of information encompassing documents, papers, data, and other resources from large-scale experiments demand significant time and effort to navigate, making the task of accessing and utilizing these varied forms of information daunting, particularly for new collaborators and early-career scientists. To tackle this issue, a Retrieval Augmented Generation (RAG)--based Summarization AI for EIC (RAGS4EIC) is under development. This AI-Agent not only condenses information but also effectively references relevant responses, offering substantial advantages for collaborators. Our project involves a two-step approach: first, querying a comprehensive vector database containing all pertinent experiment information; second, utilizing a Large Language Model (LLM) to generate concise summaries enriched with citations based on user queries and retrieved data. We describe the evaluation methods that use RAG assessments (RAGAs) scoring mechanisms to assess the effectiveness of responses. Furthermore, we describe the concept of prompt template-based instruction-tuning which provides flexibility and accuracy in summarization. Importantly, the implementation relies on LangChain, which serves as the foundation of our entire workflow. This integration ensures efficiency and scalability, facilitating smooth deployment and accessibility for various user groups within the Electron Ion Collider (EIC) community. This innovative AI-driven framework not only simplifies the understanding of vast datasets but also encourages collaborative participation, thereby empowering researchers. As a demonstration, a web application has been developed to explain each stage of the RAG Agent development in detail.

cs.CL↗

arXiv2024

The ESO-VLT MIKiS survey reloaded: the internal kinematics of the core of M75

We present the results of a study aimed at characterizing the kinematics of the inner regions of the halo globular cluster M75 (NGC 6864) based on data acquired as part of the ESO-VLT Multi-Instrument Kinematic Survey (MIKiS) of Galactic globular clusters. Our analysis includes the first determination of the line-of-sight velocity dispersion profile in the core region of M75. By using MUSE/NFM observations, we obtained a sample of $\sim 1900$ radial velocity measurements from individual stars located within $16''$ (corresponding to about $r < 3 r_c$ where $r_c$ is the estimated core radius of the system) from the cluster center. After an appropriate selection of the most accurate velocity measures, we determined the innermost portion of the velocity dispersion profile, finding that it is characterized by a constant behavior and a central velocity dispersion of $σ_0\sim 9$ km s$^{-1}$. The simultaneous King model fitting to the projected velocity dispersion and density profiles allowed us to check and update previous determinations of the main structural parameters of the system. We also detected a mild hint of rotation in the central $\sim 7''$ from the center, with an amplitude of just $\sim 1.0$ km s$^{-1}$ and a position angle of the rotation axis of PA$_0 = 174°$. Intriguingly, the position angle is consistent with that previously quoted for the suspected rotation signal in the outer region of the cluster. Taking advantage of the high quality of the photometric catalog used for the analysis of the MUSE spectra, we also provide updated estimates of the cluster distance, age, and reddening.

astro-ph.GA↗

arXiv2024

Physics Event Classification Using Large Language Models

The 2023 AI4EIC hackathon was the culmination of the third annual AI4EIC workshop at The Catholic University of America. This workshop brought together researchers from physics, data science and computer science to discuss the latest developments in Artificial Intelligence (AI) and Machine Learning (ML) for the Electron Ion Collider (EIC), including applications for detectors, accelerators, and experimental control. The hackathon, held on the final day of the workshop, involved using a chatbot powered by a Large Language Model, ChatGPT-3.5, to train a binary classifier neutrons and photons in simulated data from the \textsc{GlueX} Barrel Calorimeter. In total, six teams of up to four participants from all over the world took part in this intense educational and research event. This article highlights the hackathon challenge, the resources and methodology used, and the results and insights gained from analyzing physics data using the most cutting-edge tools in AI/ML.

physics.data-an↗

arXiv2023

ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering

We introduce a physics-informed Bayesian Neural Network (BNN) with flow approximated posteriors using multiplicative normalizing flows (MNF) for detailed uncertainty quantification (UQ) at the physics event-level. Our method is capable of identifying both heteroskedastic aleatoric and epistemic uncertainties, providing granular physical insights. Applied to Deep Inelastic Scattering (DIS) events, our model effectively extracts the kinematic variables $x$, $Q^2$, and $y$, matching the performance of recent deep learning regression techniques but with the critical enhancement of event-level UQ. This detailed description of the underlying uncertainty proves invaluable for decision-making, especially in tasks like event filtering. It also allows for the reduction of true inaccuracies without directly accessing the ground truth. A thorough DIS simulation using the H1 detector at HERA indicates possible applications for the future EIC. Additionally, this paves the way for related tasks such as data quality monitoring and anomaly detection. Remarkably, our approach effectively processes large samples at high rates.

cs.LG↗

arXiv2023

First evidence of multi-iron sub-populations in the Bulge Fossil Fragment candidate Liller 1

In the context of a project aimed at characterizing the properties of the so-called Bulge Fossil Fragments (the fossil remnants of the bulge formation epoch), here we present the first determination of the metallicity distribution of Liller 1. For a sample of 64 individual member stars we used ESO- MUSE spectra to measure the equivalent width of the CaII triplet and then derive the iron abundance. To test the validity of the adopted calibration in the metal-rich regime, the procedure was first applied to three reference bulge globular clusters (NGC 6569, NGC 6440, and NGC 6528). In all the three cases, we found single-component iron distributions, with abundance values fully in agreement with those reported in the literature. The application of the same methodology to Liller 1 yielded, instead, a clear bimodal iron distribution, with a sub-solar component at $\text{[Fe/H]}= -0.48\,$dex ($σ= 0.22$) and a super-solar component at $\text{[Fe/H]}= +0.26\,$dex ($σ= 0.17$). The latter is found to be significantly more centrally concentrated than the metal-poor population, as expected in a self-enrichment scenario and in agreement with what found in another bulge system, Terzan 5. The obtained metallicity distribution is astonishingly similar to that predicted by the reconstructed star formation history of Liller 1, which is characterized by three main bursts and a low, but constant, activity of star formation over the entire lifetime. These findings provide further support to the possibility that, similar to Terzan 5, also Liller 1 is a Bulge Fossil Fragment.

astro-ph.GA↗

arXiv2022

Machine Learning in Nuclear Physics

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.

nucl-th↗

arXiv2022

Snowmass white paper: Need for amplitude analysis in the discovery of new hadrons

We highlight the need for the development of comprehensive amplitude analysis methods to further our understanding of hadron spectroscopy. Reaction amplitudes constrained by first principles of $S$-matrix theory and by QCD phenomenology are needed to extract robust interpretations of the data from experiments and from lattice calculations.

hep-ph↗

arXiv2022

Design of Detectors at the Electron Ion Collider with Artificial Intelligence

Artificial Intelligence (AI) for design is a relatively new but active area of research across many disciplines. Surprisingly when it comes to designing detectors with AI this is an area at its infancy. The Electron Ion Collider is the ultimate machine to study the strong force. The EIC is a large-scale experiment with an integrated detector that extends for about $\pm$35 meters to include the central, far-forward, and far-backward regions. The design of the central detector is made by multiple sub-detectors, each in principle characterized by a multidimensional design space and multiple design criteria also called objectives. Simulations with Geant4 are typically compute intensive, and the optimization of the detector design may include non-differentiable terms as well as noisy objectives. In this context, AI can offer state of the art solutions to solve complex combinatorial problems in an efficient way. In particular, one of the proto-collaborations, ECCE, has explored during the detector proposal the possibility of using multi-objective optimization to design the tracking system of the EIC detector. This document provides an overview of these techniques and recent progress made during the EIC detector proposal. Future high energy nuclear physics experiments can leverage AI-based strategies to design more efficient detectors by optimizing their performance driven by physics criteria and minimizing costs for their realization.

physics.ins-det↗

arXiv2022

Streaming readout for next generation electron scattering experiment

Current and future experiments at the high intensity frontier are expected to produce an enormous amount of data that needs to be collected and stored for offline analysis. Thanks to the continuous progress in computing and networking technology, it is now possible to replace the standard `triggered' data acquisition systems with a new, simplified and outperforming scheme. `Streaming readout' (SRO) DAQ aims to replace the hardware-based trigger with a much more powerful and flexible software-based one, that considers the whole detector information for efficient real-time data tagging and selection. Considering the crucial role of DAQ in an experiment, validation with on-field tests is required to demonstrate SRO performance. In this paper we report results of the on-beam validation of the Jefferson Lab SRO framework. We exposed different detectors (PbWO-based electromagnetic calorimeters and a plastic scintillator hodoscope) to the Hall-D electron-positron secondary beam and to the Hall-B production electron beam, with increasingly complex experimental conditions. By comparing the data collected with the SRO system against the traditional DAQ, we demonstrate that the SRO performs as expected. Furthermore, we provide evidence of its superiority in implementing sophisticated AI-supported algorithms for real-time data analysis and reconstruction.

physics.ins-det↗

arXiv2021

Streaming Readout of the CLAS12 Forward Tagger Using TriDAS and JANA2

An effort is underway to develop streaming readout data acquisition system for the CLAS12 detector in Jefferson Lab's experimental Hall-B. Successful beam tests were performed in the spring and summer of 2020 using a 10GeV electron beam from Jefferson Lab's CEBAF accelerator. The prototype system combined elements of the TriDAS and CODA data acquisition systems with the JANA2 analysis/reconstruction framework. This successfully merged components that included an FPGA stream source, a distributed hit processing system, and software plugins that allowed offline analysis written in C++ to be used for online event filtering. Details of the system design and performance are presented.

physics.ins-det↗

arXiv2020

Machine Learning for Imaging Cherenkov Detectors

Imaging Cherenkov detectors are largely used in modern nuclear and particle physics experiments where cutting-edge solutions are needed to face always more growing computing demands. This is a fertile ground for AI-based approaches and at present we are witnessing the onset of new highly efficient and fast applications. This paper focuses on novel directions with applications to Cherenkov detectors. In particular, recent advances on detector design and calibration, as well as particle identification are presented.

physics.ins-det↗

arXiv2020

Photoproduction of axion-like particles

We explore the sensitivity of photon-beam experiments to axion-like particles (ALPs) with QCD-scale masses whose dominant coupling to the Standard Model is either to photons or gluons. We introduce a novel data-driven method that eliminates the need for knowledge of nuclear form factors or the photon-beam flux when considering coherent Primakoff production off a nuclear target, and show that data collected by the PrimEx experiment could substantially improve the sensitivity to ALPs with $0.03 \lesssim m_a \lesssim 0.3$ GeV. Furthermore, we explore the potential sensitivity of running the GlueX experiment with a nuclear target and its planned PrimEx-like calorimeter. For the case where the dominant coupling is to gluons, we study photoproduction for the first time, and predict the future sensitivity of the GlueX experiment using its nominal proton target. Finally, we set world-leading limits for both the ALP-gluon coupling and the ALP-photon coupling based on public mass plots.

hep-ph↗

arXiv2019

DeepRICH: Learning Deeply Cherenkov Detectors

Imaging Cherenkov detectors are largely used for particle identification (PID) in nuclear and particle physics experiments, where developing fast reconstruction algorithms is becoming of paramount importance to allow for near real time calibration and data quality control, as well as to speed up offline analysis of large amount of data. In this paper we present DeepRICH, a novel deep learning algorithm for fast reconstruction which can be applied to different imaging Cherenkov detectors. The core of our architecture is a generative model which leverages on a custom Variational Auto-encoder (VAE) combined to Maximum Mean Discrepancy (MMD), with a Convolutional Neural Network (CNN) extracting features from the space of the latent variables for classification. A thorough comparison with the simulation/reconstruction package FastDIRC is discussed in the text. DeepRICH has the advantage to bypass low-level details needed to build a likelihood, allowing for a sensitive improvement in computation time at potentially the same reconstruction performance of other established reconstruction algorithms. In the conclusions, we address the implications and potentialities of this work, discussing possible future extensions and generalization.

physics.data-an↗

arXiv2019

Double Polarization Observables in Pentaquark Photoproduction

We investigate the properties of the hidden charm pentaquark-like resonances first observed by LHCb in 2015, by measuring the polarization transfer KLL between the incident photon and the outgoing proton in the exclusive photoproduction of J/psi near threshold. We present a first estimate of the sensitivity of this observable to the pentaquark photocouplings and hadronic branching ratios, and extend our predictions to the case of initial state helicity correlation ALL, using a polarized target. These results serve as a benchmark for the SBS experiment at Jefferson Lab, which proposes to measure for the first time the helicity correlations ALL and KLL in J/psi exclusive photoproduction, in order to determine the pentaquark photocouplings and branching ratios.

hep-ph↗

arXiv2016

The GlueX DIRC Project

The GlueX experiment was designed to search for and study the pattern of gluonic excitations in the meson spectrum produced through photoproduction reactions at a new tagged photon beam facility in Hall D at Jefferson Laboratory. The particle identification capabilities of the GlueX experiment will be enhanced by constructing a DIRC (Detection of Internally Reflected Cherenkov light) detector, utilizing components of the decommissioned BaBar DIRC. The DIRC will allow systematic studies of kaon final states that are essential for inferring the quark flavor content of both hybrid and conventional mesons. The design for the GlueX DIRC is presented, including the new expansion volumes that are currently under development.

physics.ins-det↗

arXiv2016

Photoproduction of leptophobic bosons

We propose a search for photoproduction of leptophobic bosons that couple to quarks at the GlueX experiment at Jefferson Lab. We study in detail a new gauge boson that couples to baryon number $B$, and estimate that $γp \to p B$ will provide the best sensitivity for $B$ masses above 0.5 GeV. This search will also provide sensitivity to other proposed dark-sector states that couple to quarks. Finally, our results motivate a similar search for $B$ boson electroproduction at the CLAS experiment.

hep-ph↗

arXiv2015

Time dependent friction in a free gas

We consider a body immersed in a perfect gas, moving under the action of a constant force E along the x axis . We assume the gas to be described by the mean-field approximation and interacting elastically with the body, we study the friction exerted by the gas on the body fixed at constant velocities. The dynamic in this setting was studied in previous papers for object with simple shape, showing new features in the dynamic but not in the friction term. The case of more general shape of the body was left out for further difficulties, we believe indeed that there are actually non trivial issues to be faced for these more general cases. To show this and in the in the spirit of getting a more realistic perspective in the study of friction problems, in this paper we focused our attention on the friction term itself, studying its behavior on a body with a more general kind of concavity and fixed at constant velocities. We derive the expression of the friction term for constant velocities, we show how it is time dependent and we give its exact estimate in time. Finally we use this result to show the absence of a stationary velocity in the actual dynamic of such a body.

math-ph↗