Hasil untuk "hep-ex"

M. A. Sells, M. L. Chen, G. Acs

Eric A. Moreno, Samuel Bright-Thonney, Andrzej Novak et al.

Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipeline with minimal expert-curated input. Given access to a HEP dataset, an execution framework, and a corpus of prior experimental literature, we find that Claude Code succeeds in automating all stages of a typical analysis: event selection, background estimation, uncertainty quantification, statistical inference, and paper drafting. We argue that the experimental HEP community is underestimating the current capabilities of these systems, and that most proposed agentic workflows are too narrowly scoped or scaffolded to specific analysis structures. We present a proof-of-concept framework, Just Furnish Context (JFC), that integrates autonomous analysis agents with literature-based knowledge retrieval and multi-agent review, and show that this is sufficient to plan, execute, and document a credible high energy physics analysis. We demonstrate this by conducting analyses on open data from ALEPH, DELPHI, and CMS to perform electroweak, QCD, and Higgs boson measurements. Rather than replacing physicists, these tools promise to offload the repetitive technical burden of analysis code development, freeing researchers to focus on physics insight, truly novel method development, and rigorous validation. Given these developments, we advocate for new strategies for how the community trains students, organizes analysis efforts, and allocates human expertise.

Cecilia Borca, Javier Jiménez Peña, David Marckx et al.

A 2021 study by the ECFA Early-Career Researchers Panel revealed that 71% of 334 respondents used open-source software tools in their instrumentation work, yet 70% reported receiving no training for these tools. In response, the Software and Machine Learning for Instrumentation group was formed in the ECFA Early-Career Researchers Panel to assess the accessibility and quality of training programs in machine learning and software for early-career researchers in experimental and applied physics. This group launched a new survey, reaching 174 participants. This report summarises the survey results in detail, and is intended to serve as a guiding document to improve the training programs that are available to early-career researchers.

Martino Borsato, Giovanni Laganà, Maurizio Martinelli

Cherenkov rings play a crucial role in identifying charged particles in high-energy physics (HEP) experiments. Most Cherenkov ring pattern reconstruction algorithms currently used in HEP experiments rely on a likelihood fit to the photo-detector response, which often consumes a significant portion of the computing budget for event reconstruction. We present a novel approach to Cherenkov ring reconstruction using YOLO, a computer vision algorithm capable of real-time object identification with a single pass through a neural network. We obtain a reconstruction efficiency above 95% and a pion misidentification rate below 5% across a wide momentum range for all particle species.

Qianqian Shi, Teng Li, Xingtao Huang

Software framework serves as a skeleton for the offline data processing software for many high energy physics (HEP) experiments. The event data management, including the event data model (EDM), transient event store and data input/output, implements the core functionalities of the framework, and has a great impact on the performance of the entire offline software. Future HEP experiments are generating increasingly large amounts of data, bringing challenges to offline data processing. To address this issue, a common event data management system that supports efficient parallelized data processing applications has been developed based on SNiPER (Software for Non-collider Physics ExpeRiments) common software framework as well as PODIO, a common EDM toolkit for future HEP experiments. In this paper, the implementation of a parallelized event data management (PEDM) system is introduced, including the integration with MT-SNiPER and PODIO, as well as the implementation of GlobalStore to support multi-threaded event processing. Finally, the application and performance evaluation of the data management system in OSCAR (offline software of Super Tau Charm Facility) is presented.

Lin-Qing Song, Hai-Qing Zhou

This study presents a systematic estimation of the relativistic correction to the binding energies of two-body hadronic molecular states by comparing the numerical solutions of the three-dimensional (3D) Schr{ö}dinger, 3D Salpeter, and fully relativistic four-dimensional (4D) Bethe-Salpeter (BS) equations derived from the same underlying interaction. The numerical results reveal a counter-intuitive property: for hadronic molecular states whose binding energies are in the MeV range, the relativistic correction is unexpectedly large. This finding contradicts the conventional expectation that a heavier exchanged mass in the interaction implies suppressed relativistic effects. Specifically, we first benchmark the results using the Wick-Cutkosky model with a one-boson-exchange (OBE) interaction of mass $m_{ex}$, and then extend the analysis to the physical $D\bar{D}$ system. We find within the $1\sim 50$ MeV binding energy region, the relativistic correction is substantial, amounting to $-90\% \sim -70\%$ of the non-relativistic result. Such a significant correction strongly suggests that analyses based solely on the 3D Schr{ö}dinger or 3D Salpeter equations for hadronic molecular states should be treated with caution.

J. P. Marquez

This contribution explores the ability to probe BSM physics by using the experimental prospects for measuring the forward-backward asymmetry ($A_{FB}$) in $e^{+}e^{-}\rightarrow b\bar{b}$ and $e^{+}e^{-}\rightarrow c\bar{c}$ processes at the baseline energy points of ILC: 250 and 500 GeV. The studies are based on the full simulation samples and reconstruction chains from the ILD concept group. The BSM models studied are two different types of gauge-Higgs unification (GHU) models that predict BSM Z$^\prime$ resonances at the TeV scale.

Gyula Bencédi

Event classifiers based either on the charged-particle multiplicity or on event topologies, such as spherocity and underlying event, became very useful tools to study collective-like behaviors in small collision systems. However, multiplicity-based event classifiers were shown to bias the data sample in a way that can obscure the effects of multi-parton interactions, and, this way, make it difficult to pin down the origins of small-system collectivity. In this proceedings, the measurement of the transverse momentum ($p_{\mathrm{T}}$) spectra of primary charged pions, kaons, (anti)protons and unidentified hadrons in inelastic pp collisions at $\sqrt{s}=13~\mathrm{TeV}$ are reported. Events are classified using a novel event shape observable, flattenicity, that was proposed to select minijet-enhanced pp collisions. Particle production is studied as a function of flattenicity and double-differentially as a function of flattenicity and charged-particle multiplicity. The results are compared with theoretical predictions from the PYTHIA 8 and EPOS LHC Monte Carlo models.

Oksana Shadura, Alexander Held

The IRIS-HEP Analysis Grand Challenge (AGC) is designed to be a realistic environment for investigating how analysis methods scale to the demands of the HL-LHC. The analysis task is based on publicly available Open Data and allows for comparing the usability and performance of different approaches and implementations. It includes all relevant workflow aspects from data delivery to statistical inference. The reference implementation for the AGC analysis task is heavily based on tools from the HEP Python ecosystem. It makes use of novel pieces of cyberinfrastructure and modern analysis facilities in order to address the data processing challenges of the HL-LHC. This contribution compares multiple different analysis implementations and studies their performance. Differences between the implementations include the use of multiple data delivery mechanisms and caching setups for the analysis facilities under investigation.

S. Jacob, J. S. Finub, Anand Narayanan

Raghav Kansal, Anni Li, Javier Duarte et al.

There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP). In order to use such alternative simulators in practice, we need well-defined metrics to compare different generative models and evaluate their discrepancy from the true distributions. We present the first systematic review and investigation into evaluation metrics and their sensitivity to failure modes of generative models, using the framework of two-sample goodness-of-fit testing, and their relevance and viability for HEP. Inspired by previous work in both physics and computer vision, we propose two new metrics, the Fréchet and kernel physics distances (FPD and KPD, respectively), and perform a variety of experiments measuring their performance on simple Gaussian-distributed, and simulated high energy jet datasets. We find FPD, in particular, to be the most sensitive metric to all alternative jet distributions tested and recommend its adoption, along with the KPD and Wasserstein distances between individual feature distributions, for evaluating generative models in HEP. We finally demonstrate the efficacy of these proposed metrics in evaluating and comparing a novel attention-based generative adversarial particle transformer to the state-of-the-art message-passing generative adversarial network jet simulation model. The code for our proposed metrics is provided in the open source JetNet Python library.

The HSF Physics Event Generator WG, :, Efe Yazgan et al.

This paper has been prepared by the HEP Software Foundation (HSF) Physics Event Generator Working Group (WG), as an input to the second phase of the LHCC review of High-Luminosity LHC (HL-LHC) computing, which is due to take place in November 2021. It complements previous documents prepared by the WG in the context of the first phase of the LHCC review in 2020, including in particular the WG paper on the specific challenges in Monte Carlo event generator software for HL-LHC, which has since been updated and published, and which we are also submitting to the November 2021 review as an integral part of our contribution.

Titus Mombächer

In the extensive efforts to understand the nature of Dark Matter and searches at colliders, the LHCb experiment has a unique sensitivity to low mass Dark Matter candidates. These proceedings present recent results and prospects on Dark Matter searches with the LHCb experiment that achieve world-leading sensitivities.

James Mulligan

Jet substructure, defined by observables constructed from the distribution of constituents within a jet, provides the versatility to tailor observables to specific regions of QCD radiation phase space. This flexibility provides exciting new opportunities to study jet quenching in heavy-ion collisions and to ultimately help reveal the nature of the quark-gluon plasma. The ALICE detector is particularly well-suited to jet substructure measurements in heavy-ion collisions due to its high-precision tracking system. In these proceedings, we report several new jet substructure measurements in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV with ALICE. These include the first fully corrected measurements of the groomed jet momentum splitting fraction, $z_{\rm{g}}$, and the groomed jet radius, $θ_{\rm{g}} \equiv R_{\rm{g}}/R$, as well as $N$-subjettiness and the fragmentation distribution of reclustered sub-jets. These measurements are compared to theoretical calculations and provide new constraints on the physics underlying jet quenching.

Faruk Diblen, Luc Hendriks, Bob Stienen et al.

In this manuscript, we propose to expand the use of scientific repositories such as Zenodo and HEP Data, in particular in order to better examine multi-parametric solutions of physical models. The implementation of interactive web-based visualizations enables fast and comfortable re-analysis and comparisons of phenomenological data. In order to illustrate our point of view, we present some examples and demos for dark matter models, supersymmetry exclusions and LHC simulations.

A. N. Ivanov, R. Höllwieser, N. I. Troitskaya et al.

We discuss an investigation of the dark matter decay modes of the neutron, proposed by Fornal and Grinstein (Phys. Rev. Lett. 120 191801 (2018)) and Ivanov et al. ( arXiv:1806.10107 [hep-ph]) for solution of the neutron lifetime anomaly problem, through the analysis of the electrodisintegration of the deuteron d into dark matter fermions chi and protons p close to threshold. We calculate the triple-differential cross section for the reaction e^- + d ->χ+ p + e^- and propose to search for such a dark matter channel in coincidence experiments on the electrodisintegration of the deuteron e^- + d -> n + p + e^- into neutrons n and protons close to threshold with outgoing electrons, protons and neutrons in coincidence. A missing of neutron signals should testify a detection of dark matter fermions.

Francesco Capozzi, Shirley Weishi Li, Guanying Zhu et al.

We show that the Deep Underground Neutrino Experiment (DUNE), with significant but feasible new efforts, has the potential to deliver world-leading results in solar neutrinos. With a 100 kton-year exposure, DUNE could detect $\gtrsim 10^5$ signal events above 5 MeV electron energy. Separate precision measurements of neutrino-mixing parameters and the $^8$B flux could be made using two detection channels ($ν_e + \, ^{40}$Ar and $ν_{e,μ,τ} + e^-$) and the day-night effect ($> 10 σ$). New particle physics may be revealed through the comparison of solar neutrinos (with matter effects) and reactor neutrinos (without), which is discrepant by $\sim 2 σ$ (and could become $5.6 σ$). New astrophysics may be revealed through the most precise measurement of the $^8$B flux (to 2.5\%) and the first detection of the {\it hep} flux (to 11\%). {\it DUNE is required:} No other experiment, even proposed, has been shown capable of fully realizing these discovery opportunities.

Wen-Wen Huang, S. Tsai, Shu‐Fen Peng et al.

P. Foggia, G. Percannella, Alessia Saggese et al.

Wei Li, Huiping Ma, Ji Sun