arXiv subjects

Anna Zaborowska

Publications and source records attributed to Anna Zaborowska.

6 recordsLinked to original sources

arXiv2025

ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset

We introduce ColliderML - a large, open, experiment-agnostic dataset of fully simulated and digitised proton-proton collisions in High-Luminosity Large Hadron Collider conditions ($\sqrt{s}=14$ TeV, mean pile-up $\mu = 200$). ColliderML provides one million events across ten Standard Model and Beyond Standard Model processes, plus extensive single-particle samples, all produced with modern next-to-leading order matrix element calculation and showering, realistic per-event pile-up overlay, a validated OpenDataDetector geometry, and standard reconstructions. The release fills a major gap for machine learning (ML) research on detector-level data, provided on the ML-friendly Hugging Face platform. We present physics coverage and the generation, simulation, digitisation and reconstruction pipeline, describe format and access, and initial collider physics benchmarks.

hep-ex↗

arXiv2025

step2point dataset: Detailed shower simulation for data representation studies

This dataset contains a detailed simulation output that allows the construction and study of different data representations for electromagnetic and hadronic showers in calorimeters. It is published so that optimal data representations can be studied, with the ultimate goal of constructing a general tool that takes detailed simulation output and translates it into an optimal representation that can serve as the input to surrogate simulators based on generative models.

hep-ex↗

arXiv2025

A Generalisable Generative Model for Multi-Detector Calorimeter Simulation

Collider experiments, such as those at the Large Hadron Collider, use the Geant4 toolkit to simulate particle-detector interactions with high accuracy. However, these experiments increasingly require larger amounts of simulated data, leading to huge computing cost. Generative machine learning methods could offer much faster calorimeter shower simulations by directly emulating detector responses. In this work, we present CaloDiT-2, a diffusion model which uses transformer blocks. As is the case for other models explored for this task, it can be applied to specific geometries, however its true strength lies in its generalisation capabilities. Our approach allows pre-training on multiple detectors and rapid adaptation to new ones, which we demonstrate on the LEMURS dataset. It reduces the effort required to develop accurate models for novel detectors or detectors which are under development and have geometries that are changed frequently, requiring up to 25x less data and 20x less training time. To the best of our knowledge, this is the first pre-trained model to be published that allows adaptation in the context of particle shower simulations, with the model also included in the Geant4 toolkit. We also present results on benchmarks on Dataset-2 from the community-hosted CaloChallenge, showing that our models provide one of the best tradeoffs between accuracy and speed from the published models. Our contributions include a mechanism for the creation of detector-agnostic data representations, architectural modifications suitable for the data modality, a pre-training and adaptation strategy, and publicly released datasets and pre-trained models for broad use.

physics.ins-det↗

arXiv2025

LEMURS dataset: Large-scale multi-detector ElectroMagnetic Universal Representation of Showers

We present LEMURS: an extensive dataset of simulated calorimeter showers designed to support the development and benchmarking of fast simulation methods in high-energy physics, most notably providing a step towards the development of foundation models. This new dataset is more robust than the well-established CaloChallenge dataset 2, featuring substantially greater statistics, a wider range of incident angles in the detector, and most crucially multiple detector geometries (including more realistic calorimeters). The dataset is provided in HDF5 format, with a file structure inspired by the CaloChallenge shower representation while also including more variables. LEMURS scale and diversity make it particularly suitable for development of foundation models and has been used in the CaloDiT-2 model, a pre-trained model released in the community standard simulation toolkit Geant4 (version 11.4.beta). All data and code for generation and analysis are openly accessible, facilitating reproducibility and reuse across the community.

physics.ins-det↗

arXiv2025

The Critical Importance of Software for HEP

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.

hep-ex↗

arXiv2024

CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including Variational AutoEncoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, Diffusion models, and models based on Conditional Flow Matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in 1-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.

physics.ins-det↗