Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2997193

The critical importance of software for HEP

Agapopoulou, Christina [Univ. Paris-Saclay, Orsay (France); Centre National de la Recherche Scientifique (CNRS), Orsay (France). Inst. National de Physique Nucleaire et de Physique des Particules (IN2P3), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab)] (ORCID:0000000223680147)·Antel, Claire [European Organization for Nuclear Research (CERN), Geneva (Switzerland)] (ORCID:0000000196830890)·Bhattacharya, Saptaparna [Southern Methodist Univ., Dallas, TX (United States)] (ORCID:0000000205266161)·Gardiner, Steven [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000283685898)·Genser, Krzysztof L. [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000331588022)·Gooding, James Andrew [Technische Univ. of Dortmund (Germany)] (ORCID:0000000333539750)·Held, Alexander [Univ. of Wisconsin, Madison, WI (United States)] (ORCID:0000000289245885)·Hernandez Villanueva, Michel [Brookhaven National Laboratory (BNL), Upton, NY (United States)] (ORCID:0000000263225587)·Jouvin, Michel [Univ. Paris-Saclay, Orsay (France); Centre National de la Recherche Scientifique (CNRS), Orsay (France). Inst. National de Physique Nucleaire et de Physique des Particules (IN2P3), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab)] (ORCID:0000000295011276)·Lari, Tommaso [Istituto Nazionale di Fisica Nucleare (INFN), Milano (Italy)] (ORCID:000000021388869X)·Lukashenko, Valeriia [Univ. of Zurich (Switzerland)] (ORCID:0000000206305185)·Malik, Sudhir [Univ. of Puerto Rico, Mayaguez, PR (United States)] (ORCID:0000000263562655)·Moreno Briceño, Alexander [Univ. Antonio Nariño, Ibagué (Colombia)] (ORCID:0000000184152543)·Mrenna, Stephen [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:000000018731160X)·Ochoa, Inês [Laboratory of Instrumentation and Experimental Particle Physics (LIP), Lisbon (Portugal)] (ORCID:0000000161561790)·Osborn, Joseph D. [Brookhaven National Laboratory (BNL), Upton, NY (United States)] (ORCID:0000000306977704)·Pivarski, Jim [Princeton Univ., NJ (United States)] (ORCID:000000026649343X)·Price, Alan [Jagiellonian Univ., Krakow (Poland)] (ORCID:0000000203721060)·Rodrigues, Eduardo [Univ. of Liverpool (United Kingdom)] (ORCID:0000000328467625)·Sharma, Richa [Univ. of Puerto Rico, Mayaguez, PR (United States)] (ORCID:0000000246564683)·Smith, Nicholas [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000203243054)·Stewart, Graeme Andrew [European Organization for Nuclear Research (CERN), Geneva (Switzerland)] (ORCID:0000000301827088)·Zaborowska, Anna [European Organization for Nuclear Research (CERN), Geneva (Switzerland)] (ORCID:0000000162101921)·Zerwas, Dirk [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Centre National de la Recherche Scientifique (CNRS), Hamburg (Germany). Inst. National de Physique Nucleaire et de Physique des Particules (IN2P3)] (ORCID:0000000241983029)·van Veghel, Maarten [Nikhef Theory Group, Amsterdam (Netherlands)] (ORCID:0000000161786623)
View original source ↗Read source document ↗Export metadata ↓

Abstract

Particle physics has an ambitious and broad global experimental programme for the coming decades. Large investments in building new facilities are already underway or under consideration. Scaling the present processing power and data storage needs by the foreseen increase in data rates in the next decade for HL-LHC is not sustainable within the current budgets. As a result, a more efficient usage of computing resources is required in order to realise the physics potential of future experiments. Software and computing are an integral part of experimental design, trigger and data acquisition, simulation, reconstruction, and analysis, as well as related theoretical predictions. A significant investment in computing and software is therefore critical. Advances in software and computing, including artificial intelligence (AI) and machine learning (ML), will be key for solving these challenges. Making better use of new processing hardware such as graphical processing units (GPUs) or ARM chips is a growing trend. This forms part of a computing solution that makes efficient use of facilities and contributes to the reduction of the environmental footprint of HEP computing. The HEP community already provided a roadmap for software and computing for the last EPPSU, and this paper updates that, with a focus on the most resource critical parts of our data processing chain.

Explore related subjects

97 MATHEMATICS AND COMPUTING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Agapopoulou, Christina [Univ. Paris-Saclay, Orsay (France); Centre National de la Recherche Scientifique (CNRS), Orsay (France). Inst. National de Physique Nucleaire et de Physique des Particules (IN2P3), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab)] (ORCID:0000000223680147), Antel, Claire [European Organization for Nuclear Research (CERN), Geneva (Switzerland)] (ORCID:0000000196830890), Bhattacharya, Saptaparna [Southern Methodist Univ., Dallas, TX (United States)] (ORCID:0000000205266161), Gardiner, Steven [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000283685898), Genser, Krzysztof L. [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000331588022), Gooding, James Andrew [Technische Univ. of Dortmund (Germany)] (ORCID:0000000333539750), Held, Alexander [Univ. of Wisconsin, Madison, WI (United States)] (ORCID:0000000289245885), Hernandez Villanueva, Michel [Brookhaven National Laboratory (BNL), Upton, NY (United States)] (ORCID:0000000263225587), Jouvin, Michel [Univ. Paris-Saclay, Orsay (France); Centre National de la Recherche Scientifique (CNRS), Orsay (France). Inst. National de Physique Nucleaire et de Physique des Particules (IN2P3), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab)] (ORCID:0000000295011276), Lari, Tommaso [Istituto Nazionale di Fisica Nucleare (INFN), Milano (Italy)] (ORCID:000000021388869X), Lukashenko, Valeriia [Univ. of Zurich (Switzerland)] (ORCID:0000000206305185), Malik, Sudhir [Univ. of Puerto Rico, Mayaguez, PR (United States)] (ORCID:0000000263562655), Moreno Briceño, Alexander [Univ. Antonio Nariño, Ibagué (Colombia)] (ORCID:0000000184152543), Mrenna, Stephen [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:000000018731160X), Ochoa, Inês [Laboratory of Instrumentation and Experimental Particle Physics (LIP), Lisbon (Portugal)] (ORCID:0000000161561790), Osborn, Joseph D. [Brookhaven National Laboratory (BNL), Upton, NY (United States)] (ORCID:0000000306977704), Pivarski, Jim [Princeton Univ., NJ (United States)] (ORCID:000000026649343X), Price, Alan [Jagiellonian Univ., Krakow (Poland)] (ORCID:0000000203721060), Rodrigues, Eduardo [Univ. of Liverpool (United Kingdom)] (ORCID:0000000328467625), Sharma, Richa [Univ. of Puerto Rico, Mayaguez, PR (United States)] (ORCID:0000000246564683), Smith, Nicholas [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)] (ORCID:0000000203243054), Stewart, Graeme Andrew [European Organization for Nuclear Research (CERN), Geneva (Switzerland)] (ORCID:0000000301827088), Zaborowska, Anna [European Organization for Nuclear Research (CERN), Geneva (Switzerland)] (ORCID:0000000162101921), Zerwas, Dirk [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Centre National de la Recherche Scientifique (CNRS), Hamburg (Germany). Inst. National de Physique Nucleaire et de Physique des Particules (IN2P3)] (ORCID:0000000241983029), van Veghel, Maarten [Nikhef Theory Group, Amsterdam (Netherlands)] (ORCID:0000000161786623). 2025-10-13. The critical importance of software for HEP. https://doi.org/10.1140/epjc%2Fs10052-025-14571-6

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

TANTE: Time-adaptive operator learning via neural Taylor expansion

Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics. However, most existing methods rely on fixed time step sizes during rollout, which limits their ability to adapt to varying temporal complexity and often leads to error accumulation. In this work, we propose the Time-Adaptive Transformer with Neural Taylor Expansion (TANTE), a novel operator-learning framework that produces continuous-time predictions with adaptive step sizes. TANTE predicts future states by performing a Taylor expansion at the current state, where neural networks learn both the higher-order temporal derivatives and the local radius of convergence. This allows the model to dynamically adjust its rollout based on the local behavior of the solution, thereby reducing cumulative error and improving computational efficiency. We demonstrate the effectiveness of TANTE across a wide range of PDE benchmarks, achieving superior accuracy and adaptability compared to fixed-step baselines, delivering accuracy gains of 60-80 % and speed-ups of 30-40 % at inference time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Structured illumination for surface-resolved grazing-incidence X-ray scattering

Grazing-incidence (GI) scattering techniques are widely used to characterize thin films, offering high surface sensitivity and insight into morphology and structure. However, these approaches typically provide statistical averaged information due to elongated footprint or limited spatial resolution due to beam size. Here we introduce a method that combines structured illumination with GI X-ray scattering and leverages our computational imaging approach to resolve local structural details. We demonstrate that our method captures local features of an organic semiconductor thin film without the need for sample rotation as in tomography. The method expands GI techniques from statistical averaging to high-resolution imaging, thereby providing the capability for detailed analysis of local material properties, such as domain shape, orientation and polymorphism, which are critical for advancing material design towards more efficient and tailored materials.

97 MATHEMATICS AND COMPUTING↗