arXiv subjects

Etienne Dreyer

Publications and source records attributed to Etienne Dreyer.

At least 19 recordsLinked to original sources

arXiv2025

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↗

arXiv2025

Automatizing the search for mass resonances using BumpNet

Physics Beyond the Standard Model (BSM) has yet to be observed at the Large Hadron Collider (LHC), motivating the development of model-agnostic, machine learning-based strategies to probe more regions of the phase space. As many final states have not yet been examined for mass resonances, an accelerated approach to bump-hunting is desirable. BumpNet is a neural network trained to map smoothly falling invariant-mass histogram data to statistical significance values. It provides a unique, automatized approach to mass resonance searches with the capacity to scan hundreds of final states reliably and efficiently.

hep-ph↗

arXiv2025

GLOW: A Unified Particle Flow Transformer

We present GLOW, a transformer-based particle flow model that combines incidence matrix supervision from HGPflow with a MaskFormer architecture. Evaluated on CLIC detector simulations, GLOW achieves state-of-the-art performance and, together with prior work, demonstrates that a single unified transformer architecture can effectively address diverse reconstruction tasks in particle physics.

hep-ex↗

arXiv2025

Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction

In preparation for Higgs factories and energy-frontier facilities, future colliders are moving toward high-granularity calorimeters to improve reconstruction quality. However, the cost and construction complexity of such detectors is substantial, making software-based approaches like super-resolution an attractive alternative. This study explores integrating super-resolution techniques into an LHC-like reconstruction pipeline to effectively enhance calorimeter granularity and suppress noise. We find that this software preprocessing step significantly improves reconstruction quality without physical changes to the detector. To demonstrate its impact, we propose a novel transformer-based particle flow model that offers improved particle reconstruction quality and interpretability. Our results demonstrate that super-resolution can be readily applied at collider experiments.

hep-ex↗

arXiv2025

HGPflow: Extending Hypergraph Particle Flow to Collider Event Reconstruction

In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been explored in the context of single jets. In this paper, we expand the scope to full proton-proton and electron-positron collision events and study reconstruction quality using metrics at the particle, jet, and event levels. Instead of passing entire events through HGPflow, we train it on smaller partitions for scalability and to avoid potential bias from long-range correlations related to the physics process. We demonstrate that this approach is feasible and that on most metrics, HGPflow outperforms both traditional particle flow algorithms and a machine learning-based benchmark model.

hep-ex↗

arXiv2025

Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events

We extend the Particle-flow Neural Assisted Simulations (Parnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative Artificial Intelligence (genAI) tools, continuous normalizing flows and diffusion models, to create a set of reconstructed particle-flow objects conditioned on truth-level particles from CMS Open Simulations. While previous work focused on jets, our updated methods now can accommodate all particle-flow objects in an event along with particle-level attributes like particle type and production vertex coordinates. This approach is fully automated, entirely written in Python, and GPU-compatible. Using a variety of physics processes at the LHC, we show that the extended Parnassus is able to generalize beyond the training dataset and outperforms the standard, public tool Delphes.

hep-ex↗

arXiv2025

Self-Supervised Learning Strategies for Jet Physics

We extend the re-simulation-based self-supervised learning approach to learning representations of hadronic jets in colliders by exploiting the Markov property of the standard simulation chain. Instead of masking, cropping, or other forms of data augmentation, this approach simulates pairs of events where the initial portion of the simulation is shared, but the subsequent stages of the simulation evolve independently. When paired with a contrastive loss function, this naturally leads to representations that capture the physics in the initial stages of the simulation. In particular, we force the hard scattering and parton shower to be shared and let the hadronization and interaction with the detector evolve independently. We then evaluate the utility of these representations on downstream tasks.

hep-ph↗

arXiv2025

Automatizing the search for mass resonances using BumpNet

The search for resonant mass bumps in invariant-mass distributions remains a cornerstone strategy for uncovering Beyond the Standard Model (BSM) physics at the Large Hadron Collider (LHC). Traditional methods often rely on predefined functional forms and exhaustive computational and human resources, limiting the scope of tested final states and selections. This work presents BumpNet, a machine learning-based approach leveraging advanced neural network architectures to generalize and enhance the Data-Directed Paradigm (DDP) for resonance searches. Trained on a diverse dataset of smoothly-falling analytical functions and realistic simulated data, BumpNet efficiently predicts statistical significance distributions across varying histogram configurations, including those derived from LHC-like conditions. The network's performance is validated against idealized likelihood ratio-based tests, showing minimal bias and strong sensitivity in detecting mass bumps across a range of scenarios. Additionally, BumpNet's application to realistic BSM scenarios highlights its capability to identify subtle signals while managing the look-elsewhere effect. These results underscore BumpNet's potential to expand the reach of resonance searches, paving the way for more comprehensive explorations of LHC data in future analyses.

physics.data-an↗

arXiv2024

Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles

The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector simulation and reconstruction package (COCOA), we show how diffusion models can accurately model the complex spectrum of reconstructed particles inside jets.

hep-ex↗

arXiv2024

Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the CMS experiment. We show that Parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution.

physics.data-an↗

arXiv2024

PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction

In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations (LCAG) matrix, to encode the particle decay tree structure. Then, we introduce a perturbative augmentation technique applied to node features, aiming to mimic experimental uncertainties and increase data diversity. We further propose a supervised graph contrastive learning algorithm to utilize the information of inter-particle relations from multiple decay processes. Extensive experiments show that our proposed supervised graph contrastive learning with perturbative augmentation (PASCL) method outperforms state-of-the-art baseline models on an existing physics-based dataset, significantly improving the reconstruction accuracy. This method provides a more effective training strategy for models with the same parameters and makes way for more accurate and efficient high-energy particle physics data analysis.

hep-ph↗

arXiv2024

CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry

Denoising diffusion models have gained prominence in various generative tasks, prompting their exploration for the generation of calorimeter responses. Given the computational challenges posed by detector simulations in high-energy physics experiments, the necessity to explore new machine-learning-based approaches is evident. This study introduces a novel graph-based diffusion model designed specifically for rapid calorimeter simulations. The methodology is particularly well-suited for low-granularity detectors featuring irregular geometries. We apply this model to the ATLAS dataset published in the context of the Fast Calorimeter Simulation Challenge 2022, marking the first application of a graph diffusion model in the field of particle physics.

hep-ex↗

arXiv2023

Set-Conditional Set Generation for Particle Physics

The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued data is generated conditional on a set of incoming particles. To accelerate this task, we present a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.

hep-ex↗

arXiv2023

Reconstructing particles in jets using set transformer and hypergraph prediction networks

The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlations in the input data. We deploy three separate set-to-set neural network architectures to reconstruct particles in events containing a single jet in a fully-simulated calorimeter. Performance is evaluated in terms of particle reconstruction quality, properties regression, and jet-level metrics. The results demonstrate that such a high dimensional end-to-end approach succeeds in surpassing basic parametric approaches in disentangling individual neutral particles inside of jets and optimizing the use of complementary detector information. In particular, the performance comparison favors a novel architecture based on learning hypergraph structure, HGPflow, which benefits from a physically-interpretable approach to particle reconstruction.

hep-ex↗

arXiv2023

Configurable calorimeter simulation for AI applications

A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning algorithms in high energy physics that rely on realistic particle shower descriptions, such as reconstruction, fast simulation, and low-level analysis. Specifications such as the granularity and material of its nearly hermetic geometry are user-configurable. The tool is supplemented with simple event processing including topological clustering, jet algorithms, and a nearest-neighbors graph construction. Formatting is also provided to visualise events using the Phoenix event display software.

hep-ex↗

arXiv2022

Machine Learning and LHC Event Generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

hep-ph↗

arXiv2022

Report of the Topical Group on Physics Beyond the Standard Model at Energy Frontier for Snowmass 2021

This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection.

hep-ph↗

arXiv2022

Summarizing experimental sensitivities of collider experiments to dark matter models and comparison to other experiments

Comparisons of the coverage of current and proposed dark matter searches can help us to understand the context in which a discovery of particle dark matter would be made. In some scenarios, a discovery could be reinforced by information from multiple, complementary types of experiments; in others, only one experiment would see a signal, giving only a partial, more ambiguous picture; in still others, no experiment would be sensitive and new approaches would be needed. In this whitepaper, we present an update to a similar study performed for the European Strategy Briefing Book performed within the dark matter at the Energy Frontier (EF10) Snowmass Topical Group We take as a starting point a set of projections for future collider facilities and a method of graphical comparisons routinely performed for LHC DM searches using simplified models recommended by the LHC Dark Matter Working Group and also used for the BSM and dark matter chapters of the European Strategy Briefing Book. These comparisons can also serve as launching point for cross-frontier discussions about dark matter complementarity.

hep-ph↗