arXiv subjects

Caterina Doglioni

Publications and source records attributed to Caterina Doglioni.

At least 19 recordsLinked to original sources

arXiv2026

AgentRivet: an automated system for producing Rivet routines from journal publications

Particle physics collider experiments provide Rivet routines as part of the analysis preservation strategy for model-independent measurements. Rivet is a C++ toolkit that allow new theoretical models to be compared to the measurements, thus aiding the development and tuning of Monte Carlo event generators as well as searches for physics beyond the Standard Model. However, analysis coverage is known to be incomplete, with only 39% of measurements having documented and publicly available Rivet routines. In this article, we design and implement an automated workflow based on Large Language Models with the goal of providing the missing routines. This multi-step workflow, referred to as AgentRivet, extracts the physics analysis information from published papers and writes the missing Rivet routines, with intermediate code- and physics- reviews as part of an autonomous quality control. We report the results obtained using commercial Large Language Models, provided by OpenAI, Anthropic, and Google, for two recent measurements from the ATLAS and CMS experiments. We find that AgentRivet produces competent Rivet routines with few syntax errors. The physics fidelity of the routines is reasonable and follows the explanations given in the relevant publications. Nevertheless, physics-implementation issues do arise and are investigated using the artefacts produced by AgentRivet. The majority of physics implementation issues arise from subtle-but-ambiguous definitions in the given publication, although some models struggle to implement complex observables even when clear definitions are given.

hep-ex↗

arXiv2026

Green BOA: Determining the environmental break-even point for ML-based data compression

We summarise the outcome of two summer internship projects based at the University of Manchester, focused on the break-even point in terms of environmental sustainability for ML-based data compression algorithms. Using the example of a ML-based lossless compression algorithm, we compare estimates for the carbon-equivalent of the infrastructure needed for ML training and inference with the carbon-equivalent savings from reduced disk storage requirements, and discuss their break-even point.

cs.LG↗

arXiv2026

SMARTHEP: training PhD students in real-time analysis at the LHC and in industry

In this invited Editorial for Software and Computing for Big Science, we describe the SMARTHEP Innovative Training Network funded via the Marie Skłodowska-Curie Actions between 2021 and 2025. SMARTHEP trained 12 PhD students to advance machine learning and real-time analysis in high-energy physics experiments and industrial applications. We present the perspective of students, supervisors, and external observers of the network, concerning the work done within the network, the added value compared to ``typical'' PhD positions, and the emerging themes and directions from our experiences in the past four years.

physics.ed-ph↗

arXiv2025

Knowledge is Overrated: A zero-knowledge machine learning and cryptographic hashing-based framework for verifiable, low latency inference at the LHC

Low latency event-selection (trigger) algorithms are essential components of Large Hadron Collider (LHC) operation. Modern machine learning (ML) models have shown great offline performance as classifiers and could improve trigger performance, thereby improving downstream physics analyses. However, inference on such large models does not satisfy the $40\text{MHz}$ online latency constraint at the LHC. In this work, we propose \texttt{PHAZE}, a novel framework built on cryptographic techniques like hashing and zero-knowledge machine learning (zkML) to achieve low latency inference, via a certifiable, early-exit mechanism from an arbitrarily large baseline model. We lay the foundations for such a framework to achieve nanosecond-order latency and discuss its inherent advantages, such as built-in anomaly detection, within the scope of LHC triggers, as well as its potential to enable a dynamic low-level trigger in the future.

hep-ex↗

arXiv2025

BOA Constrictor: A Mamba-based lossless compressor for High Energy Physics data

The petabyte-scale data generated annually by High Energy Physics (HEP) experiments like those at the Large Hadron Collider present a significant data storage challenge. Whilst traditional algorithms like LZMA and ZLIB are widely used, they often fail to exploit the deep structure inherent in scientific data. We investigate the application of modern state space models (SSMs) to this problem, which have shown promise for capturing long-range dependencies in sequences. We present the Bytewise Online Autoregressive (BOA) Constrictor, a novel, streaming-capable lossless compressor built upon the Mamba architecture. BOA combines an autoregressive Mamba model for next-byte prediction with a parallelised streaming range coder. We evaluate our method on three distinct structured datasets in HEP, demonstrating state-of-the-art compression ratios, improving upon LZMA-9 across all datasets. These improvements range from 2.21$\times$ (vs. 1.69$\times$) on the ATLAS dataset to a substantial 44.14$\times$ (vs. 27.14$\times$) on the highly-structured CMS dataset, with a modest $\sim 4.5$MB model size. However, this gain in compression ratio comes with a trade-off in throughput; the Storage-Saving Rate ($σ_{SSR}$) of our prototype currently lags behind highly-optimised CPU-based algorithms like ZLIB. We conclude that while this Mamba-based approach is a highly promising proof-of-principle, significant future work on performance optimisation and hardware portability is required to develop it into a production-ready tool for the HEP community.

physics.comp-ph↗

arXiv2025

t-channel dark matter at the LHC -- a whitepaper

This report, summarising work achieved in the context of the LHC Dark Matter Working Group, investigates the phenomenology of $t$-channel dark matter models, spanning minimal setups with a single dark matter candidate and mediator to more complex constructions closer to UV-complete models. For each considered class of models, we examine collider, cosmological and astrophysical implications. In addition, we explore scenarios with either promptly decaying or long-lived particles, as well as featuring diverse dark matter production mechanisms in the early universe. By providing a unified analysis framework, numerical tools and guidelines, this work aims to support future experimental and theoretical efforts in exploring $t$-channel dark matter models at colliders and in cosmology.

hep-ph↗

arXiv2025

Recommendations for Best Practices for Data Preservation and Open Science in HEP

These recommendations are the result of reflections by scientists and experts who are, or have been, involved in the preservation of high-energy physics data. The work has been done under the umbrella of the Data Lifecycle panel of the International Committee of Future Accelerators (ICFA), drawing on the expertise of a wide range of stakeholders. A key indicator of success in the data preservation efforts is the long-term usability of the data. Experience shows that achieving this requires providing a rich set of information in various forms, which can only be effectively collected and preserved during the period of active data use. The recommendations are intended to be actionable by the indicated actors and specific to the particle physics domain. They cover a wide range of actions, many of which are interdependent. These dependencies are indicated within the recommendations and can be used as a road map to guide implementation efforts. These recommendations are best accessed and viewed through the web application, see https://icfa-data-best-practices.app.cern.ch/

hep-ex↗

arXiv2025

Enhancing European Cooperation in the Search for Dark Matter

The search for dark matter is an exciting topic that is pursued in different communities over a wide range of masses and using a variety of experimental approaches. The result is a strongly correlated matrix of activities across Europe and beyond, both on the experimental and the theoretical side. We suggest to encourage and foster the collaboration of the involved institutions on technical, scientific and organisational level, in order to realise the synergies that are required to increase the impact of dark matter research and to cope with the increasing experiment sizes. The suggested network -- loosely titled "DMInfraNet" -- could be realised as a new initiative of the European strategy or be based on existing structures like iDMEu or DRD. The network can also serve as a nucleus for future joint funding proposals.

hep-ex↗

arXiv2025

Input to European Strategy Update for Particle Physics: Sustainability

Human activity continues to have an enormous negative impact on the ability of the planet to sustain human and other forms of life. Six out of the nine planetary boundaries have been crossed, a seventh is close to threshold. Prominent manifestations of this development are climate change caused by greenhouse gas emissions, as well as loss of biodiversity. In recognition of the urgency of these problems, several international agreements have been ratified to achieve net-zero emissions and to halt and reverse biodiversity loss. Significant reductions in emissions are required by 2030 to meet international climate targets. The field of particle physics has an obligation and an opportunity to contribute to such mitigation efforts and to avoid causing further harm. This document urges the European Strategy Update in Particle Physics to set a clear and bold mandate for embedding environmental sustainability throughout the future scientific programme, and advocates for a series of actions that will enable this.

physics.soc-ph↗

arXiv2025

Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF

Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development (R&D) to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.

astro-ph.IM↗

arXiv2025

JENA Computing Initiative WP2 Report: Software and Heterogeneous Architectures

The scientific communities of nuclear, particle, and astroparticle physics are continuing to advance and are facing unprecedented software challenges due to growing data volumes, complex computing needs, and environmental considerations. As new experiments emerge, software and computing needs must be recognised and integrated early in design phases. This document synthesises insights from ECFA, NuPECC and APPEC, representing particle physics, nuclear physics, and astroparticle physics, and presents collaborative strategies for improving software, computing frameworks, infrastructure, and career development within these fields.

physics.comp-ph↗

arXiv2025

Probing the sensitivity of semi-visible jets to current LHC measurements using the CONTUR toolkit

Semi-visible jets arise from a hypothetical, strongly interacting ``dark sector'' -- a dark counterpart of quantum chromodynamics whose partial decays back to Standard Model particles introduce new types of collider BSM signature. CMS and ATLAS have have searched for semi-visible jets in the resonant and non-resonant production modes and set constraints on mediator mass values. In this work, indirect constraints on various model parameters, such as dark hadron masses and coupling strengths, are explored using LHC measurements.

hep-ph↗

arXiv2024

Summary of the trigger systems of the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb

In modern High Energy Physics (HEP) experiments, triggers perform the important task of selecting, in real time, the data to be recorded and saved for physics analyses. As a result, trigger strategies play a key role in extracting relevant information from the vast streams of data produced at facilities like the Large Hadron Collider (LHC). As the energy and luminosity of the collisions increase, these strategies must be upgraded and maintained to suit the experimental needs. This whitepaper compiled by the SMARTHEP Early Stage Researchers presents a high-level overview and reviews recent developments of triggering practices employed at the LHC. The general trigger principles applied at modern HEP experiments are highlighted, with specific reference to the current trigger state-of-the-art within the ALICE, ATLAS, CMS and LHCb collaborations. Furthermore, a brief synopsis of the new trigger paradigm required by the upcoming high-luminosity upgrade of the LHC is provided.

physics.ins-det↗

arXiv2024

Snowmass 2021 Cross Frontier Report: Dark Matter Complementarity (Extended Version)

The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of experiments and searches while broadening the types of candidates we can pursue. Over the next decade, there is great potential for discoveries that would transform our understanding of dark matter. In the following, we outline a road map for discovery developed in collaboration among the frontiers. A strong portfolio of experiments that delves deep, searches wide, and harnesses the complementarity between techniques is key to tackling this complicated problem, requiring expertise, results, and planning from all Frontiers of the Snowmass 2021 process.

hep-ph↗

arXiv2024

Baler -- Machine Learning Based Compression of Scientific Data

Storing and sharing increasingly large datasets is a challenge across scientific research and industry. In this paper, we document the development and applications of Baler - a Machine Learning based data compression tool for use across scientific disciplines and industry. Here, we present Baler's performance for the compression of High Energy Physics (HEP) data, as well as its application to Computational Fluid Dynamics (CFD) toy data as a proof-of-principle. We also present suggestions for cross-disciplinary guidelines to enable feasibility studies for machine learning based compression for scientific data.

physics.comp-ph↗

arXiv2023

iDMEu: An initiative for Dark Matter in Europe and beyond

We introduce the initiative for Dark Matter in Europe and beyond (iDMEu), a collective effort by a group of particle and astroparticle physicists to set up an online resource meta-repository, a common discussion platform and a series of meetings on everything concerning Dark Matter. This document serves as a status report as well as a citable item concerning iDMEu.

hep-ph↗

arXiv2023

Current Status and Future Prospects for the Light Dark Matter eXperiment

The constituents of dark matter are still unknown, and the viable possibilities span a vast range of masses. The physics community has established searching for sub-GeV dark matter as a high priority and identified accelerator-based experiments as an essential facet of this search strategy. A key goal of the accelerator-based dark matter program is testing the broad idea of thermally produced sub-GeV dark matter through experiments designed to directly produce dark matter particles. The most sensitive way to search for the production of light dark matter is to use a primary electron beam to produce it in fixed-target collisions. The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target missing-momentum experiment that realizes this approach and provides unique sensitivity to light dark matter in the sub-GeV range. This contribution provides an overview of the theoretical motivation, the main experimental challenges, how LDMX addresses these challenges, and projected sensitivities. We further describe the capabilities of LDMX to explore other interesting new and standard physics, such as visibly-decaying axion and vector mediators or rare meson decays, and to provide timely electronuclear scattering measurements that will inform the modeling of neutrino-nucleus scattering for DUNE.

hep-ex↗

arXiv2023

FAIR for AI: An interdisciplinary and international community building perspective

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

cs.CY↗