arXiv subjects

Cristiano Fanelli

Publications and source records attributed to Cristiano Fanelli.

At least 19 recordsLinked to original sources

arXiv2026

Cluster Ages to Reconstruct the Milky Way Assembly (CARMA). V. The chronological merger tree of the Milky Way

We present a new age determination of 24 globular clusters (GCs) dynamically associated with the main accretion events experienced by the Milky Way (MW), as part of the Cluster Ages to Reconstruct the Milky Way Assembly (CARMA) project's effort to trace the Galaxy's assembly history. We used deep and homogeneous archival Hubble Space Telescope data, and applied the CARMA isochrone-fitting code to derive homogeneous estimates of age, metallicity, reddening, and distance modulus for systems dynamically associated with Gaia-Sausage-Enceladus (GSE), the Sagittarius dwarf galaxy (Sag), the Helmi streams (H99), and the Sequoia galaxy (Seq). These 24 new determinations are supplemented by 11 previously studied GSE clusters to construct the complete age-metallicity relation (AMR) of the GSE system. We find that each progenitor system describes a well-defined AMR, with a distinct slope and extent reflecting its individual star-formation efficiency and chemical enrichment history. By fitting analytical AMR models within a Markov chain Monte Carlo framework, we quantify the stellar mass and accretion time for each progenitor galaxy. This results in the first detailed merger tree obtained from strictly homogeneous chronological information, according to which the Low-energy-Kraken-Heracles (LKH) system is the first merger experienced by the MW that brought GCs in, followed by Sequoia, H99, GSE, and finally Sgr. The most significant events in terms of stellar mass are LKH, GSE, and Sgr, which together contribute a total of approximately 2.5 x 10^9 solar masses. This corresponds to more than 95% of the stellar mass accreted by the MW from mergers massive enough to host GCs.

astro-ph.GA↗

arXiv2026

Evidence of a massive accretion event 1.8 billion years before the Gaia-Sausage-Enceladus merger

The merger history of the Galaxy has been traced back firmly to redshift 2 (10 Billion years ago). While evidence for the existence of at least one more significant merger before this time has been presented, its interpretation is yet to be fully established. Here we show that the population of globular clusters around the Galaxy depicts three distinct age-metallicity sequences, one associated with the progenitor of the Milky Way, one with the merger with Gaia-Enceladus 10 billion years ago, and a third intermediate sequence associated to at least one merger which we estimate took place about 1.8 billion years before Gaia-Enceladus. This discovery has been possible thanks to exquisite Hubble Space Telescope data and sophisticated analysis that enables very precise relative age determination of globular clusters. The newly identified sequence reveals that this merger took place with an object of stellar mass similar to that of Gaia-Enceladus $(\simeq5\times10^8 M_{\odot})$, and which deposited most of its mass in the inner 6 kpc of the Milky Way. The identification of a third merger event in the inner Galaxy puts to rest earlier debates, and honoring previous works we name the progenitor system Low-energy-Kraken-Heracles, or LKH for short.

astro-ph.GA↗

arXiv2026

Symbolic Extraction of Non-Perturbative Transverse-Momentum-Dependent Distributions from Drell-Yan Data

We present an analytical parametrization of the non-perturbative transverse-momentum-dependent (TMD) parton distribution function of unpolarized quarks, extracted from Drell-Yan data using a combination of neural-network fitting and symbolic regression. A factorized neural network is trained directly against experimental cross-section data from fixed-target, Tevatron, RHIC, and LHC experiments at next-to-next-to-next-to-leading logarithmic accuracy, and symbolic regression is subsequently applied to each network component to discover compact analytical expressions. The final formula is selected from a Pareto front in the space of expression complexity and experimental $χ^2$, yielding a closed-form non-perturbative function with 9 free numerical constants that achieves $χ^2/\mathrm{ndf}=1.040$ over 482 data points. A non-trivial $x$-$b_T$ cross term is retained even under a sparsity prior that biases it toward zero, indicating a mild but genuine correlation between the longitudinal momentum fraction and the transverse momentum. This work demonstrates that symbolic regression is a viable tool for bridging flexible machine-learning fits and interpretable analytical TMD parametrizations, and opens a systematic path toward data-driven discovery of specific features of non-perturbative QCD.

hep-ph↗

arXiv2026

Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing

The Production and Distributed Analysis (PanDA) system, originally developed for the ATLAS experiment at the CERN Large Hadron Collider (LHC), has evolved into a robust platform for orchestrating large-scale workflows across distributed computing resources. Coupled with its intelligent Distributed Dispatch and Scheduling (iDDS) component, PanDA supports AI/ML-driven workflows through a scalable and flexible workflow engine. We present an AI-assisted framework for detector design optimization that integrates multi-objective Bayesian optimization with the PanDA--iDDS workflow engine to coordinate iterative simulations across heterogeneous resources. The framework addresses the challenge of exploring high-dimensional parameter spaces inherent in modern detector design. We demonstrate the framework using benchmark problems and realistic studies of the ePIC and dRICH detectors for the Electron-Ion Collider (EIC). Results show improved automation, scalability, and efficiency in multi-objective optimization. This work establishes a flexible and extensible paradigm for AI-driven detector design and other computationally intensive scientific applications.

cs.DC↗

arXiv2026

GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation

Modern particles physics experiments have demonstrated an increasing need for fast, high-fidelity detector simulation as detector components have improved and subsequent computational requirements approach the limits of available resources. Recently, deep generative models have emerged as a promising alternative to traditional Monte-Carlo methods, with recent works drawing inspiration from large language models (LLMs) and self-supervised next-token prediction methods. In this work, we present an application of a GPT-style autoregressive transformer as a fast surrogate model for the calorimeter inside the CLAS12 experiment at the Thomas Jefferson National Accelerator Facility. The model is conditioned on incident momentum and generates realistic detector hits autoregressively across all nine calorimeter layers as sequences of strip, ADC, and TDC tokens. We demonstrate that the model faithfully reproduces hit multiplicity, spatial distributions, energy deposits, and the energy-momentum response of the electromagnetic calorimeter. The generator achieves inference rates exceeding 700 events per second on a single GPU, providing a substantial speedup over traditional Geant4-based simulations while maintaining physics fidelity essential for high-luminosity experimental programs.

physics.ins-det↗

arXiv2026

Application of a Mixture of Experts-based Foundation Model to the GlueX DIRC Detector

We present a Mixture-of-Experts-based foundation model applied to the GlueX DIRC detector at Jefferson Lab, demonstrating its utility as a unified framework for fast simulation, particle identification, and hit-level noise filtering of Cherenkov photons. By leveraging a single shared transformer backbone across all tasks, the approach eliminates the fragmentation of task-specific pipelines while maintaining competitive-and in several cases superior-performance relative to established methods. The model operates directly on low-level detector inputs, performing hit-by-hit autoregressive generation over split spatial and temporal vocabularies with continuous kinematic conditioning, and supports class-conditional generation of pions and kaons through its Mixture-of-Experts architecture. We benchmark against the standard geometrical reconstruction and prior deep learning methods across the full kinematic phase space of the GlueX DIRC, demonstrating that the foundation model framework transfers effectively to this detector without architectural modification. This work positions the foundation model as a practical and scalable alternative to the suite of task-specific models currently proposed for GlueX DIRC analysis.

physics.data-an↗

arXiv2026

Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuning

Modern particle physics experiments face an increasing demand for high-fidelity detector simulation as luminosities rise and computational requirements approach the limits of available resources. Deep generative models have emerged as promising surrogates for traditional Monte Carlo simulation, with recent advances drawing inspiration from large language models (LLM) and next-token prediction paradigms. In this work, we introduce a generalizable foundation model for calorimetry built on next-token transformer backbones, designed to support modular adaptation across materials, particle species, and detector configurations. Our approach combines Mixture-of-Experts pre-training with parameter-efficient fine-tuning strategies to enable controlled, additive model expansion without catastrophic forgetting. A pre-trained backbone is trained to generate electromagnetic showers across multiple absorber materials, while new materials are incorporated through the addition and tuning of lightweight expert modules. Extensions to new particle types are achieved via parameter-efficient fine-tuning and modular vocabularies, preserving the integrity of the base model. This design enables efficient, incremental knowledge integration as new simulation datasets become available, a critical requirement in realistic detector-development workflows. In addition, we demonstrate that next-token calorimeter models are computationally competitive with standard generative approaches under established LLM optimization procedures. These results establish next-token architectures as a viable path toward extensible, physics-aware foundation models for calorimetry and future high-energy physics experiments.

physics.ins-det↗

arXiv2026

Physics-Aware, Shannon-Optimal Compression via Arithmetic Coding for Distributional Fidelity

Assessing whether two datasets are distributionally consistent is central to modern scientific analysis, particularly as generative artificial intelligence produces synthetic data whose fidelity must be validated against real observations in increasingly high-dimensional settings. Existing approaches are typically relative: they determine whether one dataset is more consistent with a reference than another, but do not provide a physically grounded absolute standard for fidelity. We propose an information-theoretic approach in which lossless compression via arithmetic coding provides an operational measure of dataset fidelity under a physics-informed probabilistic representation. Datasets sharing the same underlying physical correlations admit comparable optimal descriptions, while discrepancies-arising from miscalibration, mismodeling, or bias-manifest as an irreducible excess in codelength relative to the Shannon-optimal limit defined by the physics itself. This excess codelength defines an absolute fidelity metric, quantified directly in bits. Unlike conventional measures, which lack an intrinsic scale, zero excess provides a well-defined and physically meaningful target corresponding to consistency with the underlying distribution. We show that this metric is global, interpretable, additive across components, and asymptotically optimal, with differences in codelength corresponding to differences in expected negative log-likelihood under a common reference model. As a byproduct, our approach achieves improved compression relative to standard general-purpose algorithms such as gzip. These results establish arithmetic coding not merely as a compression tool, but as a measurement instrument for absolute, physics-grounded assessment of distributional fidelity.

cs.IT↗

arXiv2026

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS.

cs.AI↗

arXiv2026

Long Range Outlook for Short-Range Correlations

Short range correlated (SRC) N N pairs are pairs of nucleons with high relative momentum (prel > kF where kF ~ 250 MeV/c is the Fermi momentum in medium to heavy nuclei) and lower center of mass momentum. The motivation for studying SRC pairs ranges from a desire to achieve a more comprehensive understanding of the many-body nuclear wave-function at high-resolution to searching for explicit QCD-dynamics effects within the nuclear medium, not to mention connections to many other open problems in nuclear physics. Exploring short-range correlations was one of the physics motivations for building CEBAF (now Jefferson Lab). Scientists used the high luminosity and high energy of this cutting-edge machine to find kinematics that cleanly showed the signals of short-range correlations. This paved the way in the last two decades for tremendous progress understanding these correlations. This paper reviews recent progress and highlights outstanding questions and areas that need further study.

nucl-ex↗

arXiv2025

Towards Foundation Models for Experimental Readout Systems Combining Discrete and Continuous Data

We present a (proto) Foundation Model for Nuclear Physics, capable of operating on low-level detector inputs from Imaging Cherenkov Detectors at the future Electron Ion Collider. Building upon established next-token prediction approaches, we aim to address potential challenges such as resolution loss from existing tokenization schemes and limited support for conditional generation. We propose four key innovations: (i) separate vocabularies for discrete and continuous variates, combined via Causal Multi-Head Cross-Attention (CMHCA), (ii) continuous kinematic conditioning through prepended context embeddings, (iii) scalable and simple, high-resolution continuous variate tokenization without joint vocabulary inflation, and (iv) class conditional generation through a Mixture of Experts. Our model enables fast, high-fidelity generation of pixel and time sequences for Cherenkov photons, validated through closure tests in the High Performance DIRC. We also show our model generalizes to reconstruction tasks such as pion/kaon identification, and noise filtering, in which we show its ability to leverage fine-tuning under specific objectives.

cs.LG↗

arXiv2025

Generative Models for Fast Simulation of Cherenkov Detectors at the Electron-Ion Collider

The integration of Deep Learning (DL) into experimental nuclear and particle physics has driven significant progress in simulation and reconstruction workflows. However, traditional simulation frameworks such as Geant4 remain computationally intensive, especially for Cherenkov detectors, where simulating optical photon transport through complex geometries and reflective surfaces introduces a major bottleneck. To address this, we present an open, standalone fast simulation tool for Detection of Internally Reflected Cherenkov Light (DIRC) detectors, with a focus on the High-Performance DIRC (hpDIRC) at the future Electron-Ion Collider (EIC). Our framework incorporates a suite of generative models tailored to accelerate particle identification (PID) tasks by offering a scalable, GPU-accelerated alternative to full Geant4-based simulations. Designed with accessibility in mind, our simulation package enables both DL researchers and physicists to efficiently generate high-fidelity large-scale datasets on demand, without relying on complex traditional simulation stacks. This flexibility supports the development and benchmarking of novel DL-driven PID methods. Moreover, this fast simulation pipeline represents a critical step toward enabling EIC-wide PID strategies that depend on virtually unlimited simulated samples, spanning the full acceptance of the hpDIRC.

physics.ins-det↗

arXiv2025

Stellar rotation in the intermediate-age massive cluster NGC 1783: clues on the nature of UV-dim stars

Over the past decade, stellar rotation has emerged as a key factor in shaping the morphology of color-magnitude diagrams of young and intermediate-age star clusters. In this study, we use MUSE integral-field spectroscopy to investigate the stellar rotation of ~2300 stars in the 1.5 Gyr old cluster NGC 1783 in the Large Magellanic Cloud. The effective temperature, surface gravity, radial velocity, and projected rotational velocity ($v\mathrm{sin}i$) of the entire sample were obtained within a Bayesian framework to derive robust estimates of these parameters along with their associated errors. The analysis shows that stars along the extended main sequence turn-off (eMSTO) cover a wide range of rotational velocities, from values consistent with no/slow rotation up to $v\mathrm{sin}i$ ~ 250 km/s. The distribution of stellar rotation velocities appears to play a crucial role in explaining the broadening of the eMSTO in this cluster, and a correlation is observed between $v\mathrm{sin}i$ and the color of the eMSTO stars, with $v\mathrm{sin}i$ increasing as the color becomes redder. Among the eMSTO stars, we investigate the peculiar population of stars strongly dimmed in the UV (so-called UV-dim stars), recently discovered in NGC 1783. UV-dim stars show clear photometric evidence of self-extinction and mild spectroscopic signatures typically observed in shell stars, thus suggesting that they have likely a decretion disc observed nearly equator-on. Interestingly, the study also shows that a significant fraction of UV-dim stars are slow rotators. We discuss potential implications these results may have on our understanding of the formation and evolution of UV-dim stars and we propose that the rotational properties of the UV-dim stars should vary with cluster age.

astro-ph.GA↗

arXiv2025

Unmasking Social Bots: How Confident Are We?

Social bots remain a major vector for spreading disinformation on social media and a menace to the public. Despite the progress made in developing multiple sophisticated social bot detection algorithms and tools, bot detection remains a challenging, unsolved problem that is fraught with uncertainty due to the heterogeneity of bot behaviors, training data, and detection algorithms. Detection models often disagree on whether to label the same account as bot or human-controlled. However, they do not provide any measure of uncertainty to indicate how much we should trust their results. We propose to address both bot detection and the quantification of uncertainty at the account level - a novel feature of this research. This dual focus is crucial as it allows us to leverage additional information related to the quantified uncertainty of each prediction, thereby enhancing decision-making and improving the reliability of bot classifications. Specifically, our approach facilitates targeted interventions for bots when predictions are made with high confidence and suggests caution (e.g., gathering more data) when predictions are uncertain.

cs.SI↗

arXiv2025

The bulge globular cluster Terzan 6 as seen from multi-conjugate adaptive optics and HST

This work consists of the first detailed photometric study of Terzan 6, one of the least known globular clusters in the Galactic bulge. Through the analysis of high angular resolution and multi-wavelength data obtained from adaptive optics corrected and space observations, we built deep, optical and near-infrared color-magnitude diagrams reaching $\approx 4$ magnitudes below the main-sequence turnoff. Taking advantage of 4 different epochs of observations, we measured precise relative proper motions for a large sample of stars, from which cluster members have been solidly distinguished from Galactic field interlopers. A non-canonical reddening law (with $R_V=2.85$) and high-resolution differential reddening map, with color excess variations up to $δE(B-V) \approx 0.8 $ mag, have been derived in the direction of the system. According to these findings, new values of the extinction and distance modulus have been obtained: respectively, $E(B-V)=2.36\pm0.05$ and $(m-M)_0=14.46 \pm 0.10$ (corresponding to $d=7.8 \pm 0.3$ kpc). We also provide the first determinations of the cluster center and projected density profile from resolved star counts. The center is offset by more than $7$ arcsec to the east from the literature value, and the structural parameters obtained from the King model fitting to the density profile indicate that Terzan 6 is in an advanced stage of its dynamical evolution. We also determined the absolute age of the system, finding $t=13\pm 1 $ Gyr, in agreement with the old ages found for the globular clusters in the Galactic bulge. From the re-determination of the absolute magnitude of the red giant branch bump and the recent estimate of the cluster global metallicity, we find that Terzan 6 nicely matches the tight relation between these two parameters drawn by the Galactic globular cluster population.

astro-ph.GA↗

arXiv2024

Uncertainty Quantification with Bayesian Higher Order ReLU KANs

We introduce the first method of uncertainty quantification in the domain of Kolmogorov-Arnold Networks, specifically focusing on (Higher Order) ReLUKANs to enhance computational efficiency given the computational demands of Bayesian methods. The method we propose is general in nature, providing access to both epistemic and aleatoric uncertainties. It is also capable of generalization to other various basis functions. We validate our method through a series of closure tests, including simple one-dimensional functions and application to the domain of (Stochastic) Partial Differential Equations. Referring to the latter, we demonstrate the method's ability to correctly identify functional dependencies introduced through the inclusion of a stochastic term. The code supporting this work can be found at https://github.com/wmdataphys/Bayesian-HR-KAN

cs.LG↗

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↗