arXiv subjects

Jonas Glombitza

Publications and source records attributed to Jonas Glombitza.

11 recordsLinked to original sources

arXiv2026

Enhancing event reconstruction for $\gamma$-ray particle detector arrays using transformers

Gamma-ray astronomy from hundreds of GeV to PeV is confined to ground-based experiments that detect air showers induced by $\gamma$-rays entering Earth's atmosphere. While particle detector arrays feature huge detection areas, accurately reconstructing the primary particle properties is difficult due to the sparse sampling of the air shower and its intrinsic fluctuations. In this work, using simulations of a future water-Cherenkov array, we investigate two end-to-end deep learning approaches based on the transformer architecture with different computational complexities that utilize calibrated raw data. We benchmark both methods against well-established methods in the field in terms of $\gamma$-hadron separation, angular, core, and energy reconstruction. Our results show significant improvements across the whole energy range, particularly at low and intermediate energies. This work is the first to consistently demonstrate improved performance in both event reconstruction and $\gamma$-hadron separation using a single architecture.

astro-ph.IM↗

arXiv2026

Generation of Imaging Air Cherenkov Telescope images using Diffusion Models

Substantial amounts of air-shower simulations are needed to derive the instrument response for analyzing Imaging Air Cherenkov Telescope (IACT) data. This process is both computationally intensive and requires repetition under varying observation conditions, due to detector aging, changes in the atmosphere, or the instrument hardware. Generative models offer an efficient alternative, significantly accelerating simulations while compactly storing extensive simulation libraries, and providing a differentiable surrogate model of the instrument. However, their applicability has so far been limited in gamma-ray astronomy, particularly for modeling hadronic showers that dominate the background and exhibit significant intrinsic fluctuations that are challenging to model. In this study, we present the first application of score-based diffusion models to generate monoscopic $\gamma$-ray and proton shower images with nearly 2,000 pixels and benchmark the performance against Wasserstein GANs using H.E.S.S. simulations. We examine quality using both low-level parameters and well-established shower-shape observables, and take the first step towards analysis readiness by investigating $\gamma$-hadron separation. While GAN-based approaches can reproduce $\gamma$-ray showers with high fidelity, they fail to generate proton events of comparable quality, leading to a measurable degradation in analysis performance. In contrast, score-based diffusion modles achieve significantly superior quality for $\gamma$-ray and proton showers, accurately reproducing high-level correlations and generating events that are statistically indistinguishable from simulations at the analysis level. These results establish diffusion-based models as the first analysis-ready surrogate model of a single IACT, opening new prospects for fast instrument response generation, detector optimization, and connected downstream tasks.

astro-ph.IM↗

arXiv2025

Application of Graph Networks to a wide-field Water-Cherenkov-based Gamma-Ray Observatory

With their wide field of view and high duty cycle, water-Cherenkov-based observatories are integral to studying the very high-energy gamma-ray sky. For gamma-ray observations, precise event reconstruction and highly effective background rejection are crucial and have been continuously improving in recent years. In this work, we investigate the application of graph neural networks (GNNs) to background rejection and energy reconstruction and benchmark their performance against state-of-the-art methods. In our simulation study, we find that GNNs outperform hand-designed classification algorithms and observables in background rejection and find an improved energy resolution compared to template-based methods.

astro-ph.IM↗

arXiv2024

Ultra-Fast Generation of Air Shower Images for Imaging Air Cherenkov Telescopes using Generative Adversarial Networks

For the analysis of data taken by Imaging Air Cherenkov Telescopes (IACTs), a large number of air shower simulations are needed to derive the instrument response. The simulations are very complex, involving computational and memory-intensive calculations, and are usually performed repeatedly for different observation intervals to take into account the varying optical sensitivity of the instrument. The use of generative models based on deep neural networks offers the prospect for memory-efficient storing of huge simulation libraries and cost-effective generation of a large number of simulations in an extremely short time. In this work, we use Wasserstein Generative Adversarial Networks to generate photon showers for an IACT equipped with the FlashCam design, which has more than $1{,}500$ pixels. Using simulations of the H.E.S.S. experiment, we demonstrate the successful generation of high-quality IACT images. The analysis includes a comprehensive study of the generated image quality based on low-level observables and the well-known Hillas parameters that describe the shower shape. We demonstrate for the first time that the generated images have high fidelity with respect to low-level observables, the Hillas parameters, their physical properties, as well as their correlations. The found increase in generation speed in the order of $10^5$ yields promising prospects for fast and memory-efficient simulations of air showers for IACTs.

astro-ph.IM↗

arXiv2023

Application of Graph Networks to background rejection in Imaging Air Cherenkov Telescopes

Imaging Air Cherenkov Telescopes (IACTs) are essential to ground-based observations of gamma rays in the GeV to TeV regime. One particular challenge of ground-based gamma-ray astronomy is an effective rejection of the hadronic background. We propose a new deep-learning-based algorithm for classifying images measured using single or multiple Imaging Air Cherenkov Telescopes. We interpret the detected images as a collection of triggered sensors that can be represented by graphs and analyzed by graph convolutional networks. For images cleaned of the light from the night sky, this allows for an efficient algorithm design that bypasses the challenge of sparse images in deep learning approaches based on computer vision techniques such as convolutional neural networks. We investigate different graph network architectures and find a promising performance with improvements to previous machine-learning and deep-learning-based methods.

astro-ph.IM↗

arXiv2022

Report of the Topical Group on Cosmic Probes of Fundamental Physics for for Snowmass 2021

Cosmic Probes of Fundamental Physics take two primary forms: Very high energy particles (cosmic rays, neutrinos, and gamma rays) and gravitational waves. Already today, these probes give access to fundamental physics not available by any other means, helping elucidate the underlying theory that completes the Standard Model. The last decade has witnessed a revolution of exciting discoveries such as the detection of high-energy neutrinos and gravitational waves. The scope for major developments in the next decades is dramatic, as we detail in this report.

hep-ph↗

arXiv2022

Shared Data and Algorithms for Deep Learning in Fundamental Physics

We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies. The datasets contain hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories. While public datasets from multiple fundamental physics disciplines already exist, the common interface and provided reference models simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. We discuss the design and structure and line out how additional datasets can be submitted for inclusion. As showcase application, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks. We show that our approach reaches performance close to dedicated methods on all datasets. To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. Implementations are also provided for our proposed method and all reference algorithms.

cs.LG↗

arXiv2020

Identification of Patterns in Cosmic-Ray Arrival Directions using Dynamic Graph Convolutional Neural Networks

We present a new approach for the identification of ultra-high energy cosmic rays from sources using dynamic graph convolutional neural networks. These networks are designed to handle sparsely arranged objects and to exploit their short- and long-range correlations. Our method searches for patterns in the arrival directions of cosmic rays, which are expected to result from coherent deflections in cosmic magnetic fields. The network discriminates astrophysical scenarios with source signatures from those with only isotropically distributed cosmic rays and allows for the identification of cosmic rays that belong to a deflection pattern. We use simulated astrophysical scenarios where the source density is the only free parameter to show how density limits can be derived. We apply this method to a public data set from the AGASA Observatory.

astro-ph.HE↗

arXiv2020

Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network

Simulations of particle showers in calorimeters are computationally time-consuming, as they have to reproduce both energy depositions and their considerable fluctuations. A new approach to ultra-fast simulations are generative models where all calorimeter energy depositions are generated simultaneously. We use GEANT4 simulations of an electron beam impinging on a multi-layer electromagnetic calorimeter for adversarial training of a generator network and a critic network guided by the Wasserstein distance. The generator is constraint during the training such that the generated showers show the expected dependency on the initial energy and the impact position. It produces realistic calorimeter energy depositions, fluctuations and correlations which we demonstrate in distributions of typical calorimeter observables. In most aspects, we observe that generated calorimeter showers reach the level of showers as simulated with the GEANT4 program.

physics.ins-det↗

arXiv2018

Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks

We use adversarial network architectures together with the Wasserstein distance to generate or refine simulated detector data. The data reflect two-dimensional projections of spatially distributed signal patterns with a broad spectrum of applications. As an example, we use an observatory to detect cosmic ray-induced air showers with a ground-based array of particle detectors. First we investigate a method of generating detector patterns with variable signal strengths while constraining the primary particle energy. We then present a technique to refine simulated time traces of detectors to match corresponding data distributions. With this method we demonstrate that training a deep network with refined data-like signal traces leads to a more precise energy reconstruction of data events compared to training with the originally simulated traces.

astro-ph.IM↗

arXiv2017

A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers

We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed locations on a regular grid. The detector's responses to traversing shower particles are signal amplitudes as a function of time, which provide information on transverse and longitudinal shower properties. In order to take advantage of convolutional network techniques specialized in local pattern recognition, we convert all information to the image-like grid of the detectors. In this way, multiple features, such as arrival times of the first particles and optimized characterizations of time traces, are processed by the network. The reconstruction quality of the cosmic ray arrival direction turns out to be competitive with an analytic reconstruction algorithm. The reconstructed shower direction, energy and shower depth show the expected improvement in resolution for higher cosmic ray energy.

astro-ph.IM↗