arXiv subjects

L. Heinrich

Publications and source records attributed to L. Heinrich.

6 recordsLinked to original sources

arXiv2026

Light Antinuclei Coalescence: Femtoscopic Constraints via Neural-Flow Surrogates

Precise predictions of cosmic-ray antinuclei fluxes, a prime dark matter signature, are limited by the lack of data constraining production rates of antinuclei. We mitigate this bottleneck with a fast, differentiable normalizing-flow surrogate for the femtoscopic source model (CECA), fit to 49 ALICE proton-proton correlation functions, and extrapolated via scaling laws to the low-multiplicity domain relevant for cosmic rays. The surrogate reproduces CECA with sub-percent emulation fidelity, yielding data-constrained source functions that remove the dominant uncertainty in coalescence-based antinuclei production rates. The resulting uncertainties on the coalescence parameters $B_2$ and $B_3$ shrink from factors of 10-100 and ${\sim}$1000 to the few percent and ten-percent level, respectively, with an additional ${\sim}15\%$ wavefunction systematic for $B_{2}$.

astro-ph.HE↗

arXiv2024

Analysis Facilities White Paper

This white paper presents the current status of the R&D for Analysis Facilities (AFs) and attempts to summarize the views on the future direction of these facilities. These views have been collected through the High Energy Physics (HEP) Software Foundation's (HSF) Analysis Facilities forum, established in March 2022, the Analysis Ecosystems II workshop, that took place in May 2022, and the WLCG/HSF pre-CHEP workshop, that took place in May 2023. The paper attempts to cover all the aspects of an analysis facility.

hep-ex↗

arXiv2023

Potential of the Julia programming language for high energy physics computing

Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified

hep-ph↗

arXiv2023

Data Preservation in High Energy Physics

Data preservation is a mandatory specification for any present and future experimental facility and it is a cost-effective way of doing fundamental research by exploiting unique data sets in the light of the continuously increasing theoretical understanding. This document summarizes the status of data preservation in high energy physics. The paradigms and the methodological advances are discussed from a perspective of more than ten years of experience with a structured effort at international level. The status and the scientific return related to the preservation of data accumulated at large collider experiments are presented, together with an account of ongoing efforts to ensure long-term analysis capabilities for ongoing and future experiments. Transverse projects aimed at generic solutions, most of which are specifically inspired by open science and FAIR principles, are presented as well. A prospective and an action plan are also indicated.

hep-ex↗

arXiv2021

The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to $10~\rm{fb}^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

hep-ph↗

arXiv2018

HEP Software Foundation Community White Paper Working Group - Data and Software Preservation to Enable Reuse

In this chapter of the High Energy Physics Software Foundation Community Whitepaper, we discuss the current state of infrastructure, best practices, and ongoing developments in the area of data and software preservation in high energy physics. A re-framing of the motivation for preservation to enable re-use is presented. A series of research and development goals in software and other cyberinfrastructure that will aid in the enabling of reuse of particle physics analyses and production software are presented and discussed.

physics.comp-ph↗