Workshop on Machine Learning for Analysis of High-Energy Cosmic Particles

Session

Talks

28 Jan 2025, 11:00

Clayton Hall (University of Delaware)

Conveners

Talks

• Aswathi Balagopal V (UW-Madison)

Talks

• Matthias Plum (South Dakota School of Mines and Technology)

Talks

• Agnieszka Leszczynska (University of Delaware)

Talks

• Steffen Traugott Hahn (KIT - IAP/ETP)

Talks

• Felix Yu

Talks

• Abdul Rehman (University of Delaware)

Talks

• Andrea Parenti

Talks

• Jonas Glombitza (Erlangen Centre for Astroparticle Physics (ECAP), Friedrich-Alexander-Universität Erlangen-Nürnberg )

Talks

• Stef Verpoest (University of Delaware)

Talks

• Larissa Paul (South Dakota School of Mines & Technology)

Talks

• Spencer Axani (The University of Delaware)

29. Deep Learning in Astroparticle Physics

Jonas Glombitza (RWTH AACHEN UNIVERSITY)

28/01/2025, 11:00

Deep Learning in Astroparticle Physics

Algorithms based on machine learning have been extraordinarily successful across many domains, including computer vision, machine translation, engineering, and science.
Moreover, in the field of physics, the importance of machine learning is growing quickly, driven by the need for precise and efficient algorithms that can effectively handle vast...

25. A Hybrid Approach to Event Reconstruction for Atmospheric Cherenkov Telescopes Combining Machine Learning and Likelihood Fitting (Remote)

Mr Georg Schwefer (Max-Planck-Institut für Kernphysik)

28/01/2025, 11:45

Talk

The imaging atmospheric Cherenkov technique currently provides the highest angular resolution achievable in astronomy at very high energies. High resolution measurements provide the key to progress on many of the key questions in high energy astrophysics. The huge potential of the next generation Cherenkov Telescope Array Observatory (CTAO) in this regard can be realised with the help of...

24. Deep Learning applied to CTAO LST-1 and the difficulty to go from simulated to real data (Remote)

Thomas Vuillaume (LAPP, Univ. Savoie Mont-Blanc, CNRS)

28/01/2025, 12:15

Talk

GammaLearn is a project developing deep learning solutions for the Cherenkov Telescope Array Observatory (CTAO) data analysis. Its first application is event reconstruction based on images acquired by the Large-Sized Telescope (LST-1), currently under commissioning at La Palma.
In this talk, we present a review of the project: the architecture $\gamma$-PhysNet we have developed to tackle this...

14. Machine Learning Techniques for Neutrino Reconstructions in IceCube

Philip Weigel (Massachusetts Institute of Technology)

28/01/2025, 14:00

Talk

Advancements in machine learning have improved event reconstruction in the analyses of IceCube data, providing fast and accurate estimations of neutrino properties. These methods typically use pulse series data and the spatial information of digital optical modules as inputs to neural networks. I will discuss current reconstruction techniques in IceCube and their applications to physics...

26. Interpretable Deep Learning for Event Reconstruction in IceCube

Mirco Huennefeld (Universität Dortmund)

28/01/2025, 14:45

Talk

Event reconstruction is a critical step in the analysis of data at the IceCube Neutrino Observatory. Traditional maximum-likelihood methods, while provably optimal under certain conditions, can be computationally expensive and infeasible in practice. A reconstruction method is presented that combines the statistical rigor of maximum-likelihood estimation with the powerful representation...

2. Towards improving efficiency of machine learning techniques in neutrino telescopes

Felix Yu

28/01/2025, 16:00

Talk

Neutrino telescopes detect rare interactions of particles produced from some of the most extreme environments in the Universe. Given the rate of backgrounds, these telescopes amass an enormous quantity of large variance, high-dimensional data. These attributes create substantial challenges for analyzing and reconstructing neutrinos, particularly when utilizing machine learning (ML) techniques....

8. Machine learning-based analyses using surface detector data of the Pierre Auger Observatory

Steffen Traugott Hahn (KIT - IAP/ETP)

28/01/2025, 16:45

Talk

The Pierre Auger Observatory, the world’s largest detector for studying ultra-high-energy cosmic rays (UHECRs), employs multiple detection techniques to observe the different components of extensive air showers. In order to accurately understand the physics of UHECRs, it is essential to determine their mass composition. Since UHECRs can only be measured indirectly, it is necessary to study...

15. Machine Learning at Telescope Array (Remote)

Ivan Kharuk (Institute for Nuclear Research RAS)

29/01/2025, 09:00

Talk

Telescope Array is a large-scale cosmic-ray observatory studying ultra-high-energy cosmic rays. Its Surface Detector array consists of 507 scintillation stations arranged in a rectangular grid covering approximately 700 km². This talk presents our deep learning approach to reconstructing cosmic ray properties from Telescope Array Surface Detector data. We demonstrate how combining multiple...

28. Evaluation of energy reconstruction performance of the Telescope Array surface detector using a deep neural network and hybrid data (Remote)

Anton Prosekin (Institute of Physics, Academia Sinica)

29/01/2025, 09:30

Talk

Accurate reconstruction of Ultra-High-Energy Cosmic Ray (UHECR) properties is crucial for studying their origins and composition. In this work, we introduce a Deep Neural Network (DNN) model based on the AixNet architecture to reconstruct UHECR parameters using data from the Telescope Array surface detector (SD). The DNN predicts key parameters, such as energy, arrival direction, core...

32. Reconstruction of energy and arrival directions of UHECRs registered by fluorescence telescopes with a neural network (Remote)

Mikhail Zotov (Lomonosov Moscow State University)

29/01/2025, 10:00

Talk

Fluorescence telescopes are important instruments widely used in modern experiments for registering ultraviolet radiation from extensive air showers (EASs) generated by cosmic rays of ultra-high energies. We present a proof-of-concept convolutional neural network aimed at reconstruction of energy and arrival directions of primary particles using model data for two telescopes developed by the...

11. Generative Neural Networks for Simulating Radio Emission from Air Showers (Remote)

Pranav Sampathkumar (IAP, KIT)

29/01/2025, 11:00

Talk

The simulations of radio emission from EAS are essential for reconstructing various shower parameters from the measured radio signals. As bigger experiments use more and more antennas, the computational cost of these simulations gets prohibitively large. These simulations also scale exponentially with higher primary energies and linearly with the number of antennas. Thus there is a need to...

30. Detection of Radio Signals from Cosmic Rays Using Convolutional Neural Networks with Data from SKALA antennas at IceTop

Paula Gálvez Molina

29/01/2025, 11:20

Talk

Cosmic rays colliding with atmospheric particles produce cascades of secondary particles known as extensive air showers. These showers emit electromagnetic radiation whose radio component is detectable by radio antennas. At the surface of the IceCube Neutrino Observatory in Antarctica, a prototype station equipped with three antennas has been collecting background and air-shower data since...

4. Direction and energy reconstruction with uncertainty quantification for GRAND using graph neural network (Remote)

Arsène Ferrière (CEA-List), Aurélien Benoit-Lévy (CEA-List), for the GRAND Collaboration

29/01/2025, 11:40

Talk

For experiments such as GRAND, a distributed radio-antenna array for ultra-high-energy neutrino detection, a precise direction and energy reconstruction is essential. Machine-learning methods and in particular graph neural networks (GNN) appear to be an interesting solution given their ability to use localised and variable-size inputs. In this contribution, we will present and summarize the...

16. Denoising Radio Pulses from Air Showers Using Machine Learning Methods (Remote)

Mr Zhisen Lai (SFSU)

29/01/2025, 12:00

Talk

The Giant Radio Array for Neutrino Detection (GRAND) aims to detect radio signals from extensive air showers caused by ultra-high-energy cosmic particles. Galactic, instrumental, and anthropogenic noise are expected to contaminate these signals.
To address this problem, we propose training an unsupervised convolutional network known as an autoencoder. This network is used to learn a coded...

13. Convolutional Neural Network Processing of Radio Emission for Nuclear Composition Classification of Ultra-High-Energy Cosmic Rays (Remote)

Mrs Cosmina Mihoreanu (Faculty of Automatic Control and Computer Science, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania), Dr Paula Gina Isar (Institute of Space Science — INFLPR Subsidiary, 077125 Bucharest-Magurele, Romania), Mr Tudor Alexandru Calafeteanu (Faculty of Automatic Control and Computer Science, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania)

29/01/2025, 12:15

Talk

Ultra-high-energy cosmic rays (UHECRs) are the most mysterious particles in the Universe originating from extragalactic sources, which yet rise a couple of fundamental open questions, such as where do they come from, how do they propagate, and how do they reach the energies they exhibit. Due to the very low flux, i.e. one particle per km2 per century at about 1020 eV, UHECRs are detected...

31. UHE Cosmic Ray Candidate Identification in RNO-G Deep Antennas Using Machine Learning

Bryan Hendricks (Pennsylvania State University)

29/01/2025, 14:00

Talk

Ultra-high-energy cosmic rays (UHECRs) are believed to originate from the universe's most cataclysmic events, yet their sources remain unidentified. Composed primarily of protons and nuclei ranging from light elements to iron, these charged particle emissions are deflected en route to Earth by magnetic fields, obscuring their true source directions. The Radio Neutrino Observatory in Greenland...

10. In-situ pulser depth reconstruction for RNO-G using Neural Network

Sanyukta Agarwal (University of Kansas)

29/01/2025, 14:20

Talk

Radio Neutrino Observatory in Greenland (RNO-G) aims to detect Askaryan emission from ultra-high energy astrophysical and cosmogenic neutrinos above 10 PeV. Situated at Summit Station, it is proposed to have 35 stations of which 7 stations have been installed so far. Search for neutrinos and their direction reconstruction using interferometry requires precise control of parameters such as...

18. A Simulation-Based Inference Method for Electric Field Reconstruction (Remote)

Mr Thomas McKinley (SFSU)

29/01/2025, 14:40

Talk

The primary goal of the Giant Radio Array for Neutrino Detection (GRAND) is to uncover the mysterious sources of ultra-high-energy cosmic rays (UHECRs). GRAND aims to achieve this by detecting electric fields generated by UHECR interactions with Earth's atmosphere and magnetic field. Reconstructing the electric field from measured antenna voltages is difficult due to the need for a detailed...

17. Reconstructing the Direction of Ultra-High-Energy Cosmic Rays Using a Simulation-Based Inference Method

Mr Zach Mason (SFSU)

29/01/2025, 14:55

Talk

GRAND (Giant Radio Array for Neutrino Detection) is a proposed next-generation observatory designed to detect ultra-high-energy (UHE) cosmic particles. It aims to accomplish this by identifying the radio signals generated when these particles interact with the atmosphere and Earth's magnetic field. We present a novel pipeline utilizing simulation-based inference (SBI) methods to reconstruct...

34. The Radar Echo Telescope for Cosmic rays

Krishna Nivedita Gopinath (Radboud University)

29/01/2025, 15:10

Talk

The Radar Echo Telescope for Cosmic Rays (RET-CR) was deployed this year at the high-altitude Summit Station in Greenland. Its primary goal is to detect in-ice continuations of high-energy cosmic-ray-induced air showers using the radar echo method. Successfully detecting in-ice cosmic-ray signals through this technique would provide significant insights and serve as a foundation for the...

6. A graph neural network reconstruction for the IceAct telescopes

Larissa Paul (South Dakota School of Mines & Technology)

29/01/2025, 16:00

Talk

Situated at the geographic South Pole, the imaging air shower telescopes IceAct observe the atmosphere above the IceCube Neutrino Observatory. Therefore, the IceAct telescopes measure the electromagnetic air-shower development complementary to the air shower at the surface with IceTop and the high-energetic muonic component measured by the in-ice detector. Currently, three IceAct telescopes...

7. Gamma/Hadron Separation using Machine Learning Methods with the IceAct Telescopes

Logan Molchany (University of Delaware)

29/01/2025, 16:30

Talk

The IceCube Neutrino Observatory, located at the South Pole, is a multi-component detector array capable of observing cosmic-rays on the TeV to EeV scale. In addition to the InIce component, and the surface component IceTop, three new Imaging Air Cherenkov Telescopes, called IceAct, were installed. One of the primary goals of the IceAct telescopes is to search for high-energy photons in the...

5. Mass composition study with machine learning on KASCADE archival data (Remote)

Nikita Petrov (Novosibirsk State University)

30/01/2025, 09:00

Talk

Despite many experiments in the 1–100 PeV range, accurately measuring mass component spectra in this energy region remains challenging. Discrepancies between experiments are attributed to factors including the choice of the hadronic interaction model used.
In this study, we present a reanalysis of archival data from the KASCADE experiment, which recorded extensive air showers from 1996 to...

20. Improving Gamma-ray Angular Resolution with Convolutional Neural Network De-noiser

Dr Ruo-Yu Shang (Barnard College, Columbia University)

30/01/2025, 09:20

Talk

Imaging Atmospheric Cherenkov Telescopes (IACT) reconstruct the locations of gamma-ray sources using stereo analysis of images of gamma-ray air showers. The images of gamma-ray showers suffer from the noise fluctuation arises from night-sky brightness. Understanding the quality of an image is crucial for estimating the uncertainty of the gamma-ray arrival direction. In this presentation, we...

19. Modeling IACT Gamma-ray Background using Singular Value Decomposition

Dr Ruo-Yu Shang (Barnard College, Columbia University)

30/01/2025, 09:40

Talk

Extended $\gamma$-ray sources, such as TeV halos, evolved pulsar wind nebulae, and star clusters, impose a challenge to analyses of Imaging Atmospheric Cherenkov Telescope (IACT) data due to the difficulty in estimating irreducible background originating from cosmic-ray-induced $\gamma$-ray-like events in the source regions. A background estimation method is necessary to address IACT analyses...

23. Stereograph: stereoscopic event reconstruction using graph neural networks applied to CTAO (Remote)

Mrs Hana Ali Messaoud (LAPP, Univ. Savoie Mont-Blanc, CNRS)

30/01/2025, 10:05

The CTAO (Cherenkov Telescope Array Observatory) is an international observatory currently under construction. With more than sixty telescopes, it will eventually be the largest and most sensitive ground-based gamma-ray observatory.

CTAO studies the high-energy universe by observing gamma rays emitted by violent phenomena (supernovae, black hole environments, etc.). These gamma rays produce...

21. Fast Generation of Realistic Data-Driven Stereoscopic Shower Images using Generative Adversarial Networks (Remote)

Dr Kameswara Bharadwaj Mantha (University of Minnesota)

30/01/2025, 11:00

Talk

Effective identification and characterization of particle showers captured by ground-based cherenkov telescopes is critical for very high energy gamma-ray astrophysics. A common step employed in the field is to use synthetic gamma-ray and hadronic events generated based on computationally-expensive simulations and use them for downstream analyses. Leveraging the power of generative deep...

12. AI Agents for Ground-Based Gamma Astronomy (Remote)

Dmitriy Kostunin (DESY)

30/01/2025, 11:30

Talk

The next generation instruments for ground-based gamma-ray astronomy are marked by a substantial increase in complexity with dozens of telescopes. This leap in scale introduces significant challenges in managing system operations and offline data analysis. The methods, which depend on advanced personnel training and sophisticated software, become increasingly strained as the system's...

1. Application of graph networks to a next generation wide-field gamma-ray observatory in the southern sky

Franziska Leitl (Erlangen Centre for Astroparticle Physics (ECAP), Friedrich-Alexander-Universität Erlangen-Nürnberg)

30/01/2025, 12:00

Talk

Water-Cherenkov detectors have long proven their importance for the research of high energetic gamma rays in numerous experiments in the Northern Hemisphere.
The Southern Wide-field Gamma-ray Observatory (SWGO) will be the first observatory using this technology in the Southern Hemisphere to observe gamma-ray emission in an energy range of 100s of GeV up to the PeV scale.
The proposed layout...

22. Graph Neural Networks for Photon Search with the Underground Muon Detector of the Pierre Auger Observatory (Remote)

Ezequiel Rodriguez (ITeDA-KIT)

30/01/2025, 14:00

Talk

Ultra-high-energy (UHE) photons are expected as by-products of cosmic-ray acceleration, propagation, or decay of super-heavy dark matter particles. Predicted diffuse photon fluxes are usually several orders of magnitude below the UHE cosmic-ray flux. This contribution presents a method for discriminating photon-initiated air showers in the overwhelming cosmic-ray background with the Pierre...

9. IceTop gamma-hadron separation and angular error estimation using machine learning techniques

Sebastian Vergara Carrasco

30/01/2025, 14:20

Talk

The IceCube Neutrino Observatory, located at the South Pole, combines two detector systems to study high-energy cosmic-ray events. The surface array, called IceTop, indirectly detects cosmic rays within the 100 TeV to EeV range through ice-Cherenkov tanks, providing reconstructed observables such as primary energy and direction. The in-ice optical array detects high-energy muonic components of...

27. IceTop-CNN: Cosmic-Ray Reconstruction in IceTop using a Convolutional Neural Network with Low-Level Inputs (Remote)

Ethan Dorr

30/01/2025, 14:40

Talk

We present on the development of an application for training and evaluating convolutional neural networks for use with high-statistics, minimally-cut cosmic-ray anisotropy studies. This application has been built to utilize computing resources from the IceCube Observatory using the HTCondor workload management system. Our goal is to streamline the creation of lightweight models that can...

3. Optimizing a Cosmic-ray Energy Estimator with Machine learning for the HAWC observatory (Remote)

Tomás Capistrán (Università degli Studi di Torino & INFN Sezione di Torino)

30/01/2025, 15:00

Talk

Situated at an elevation of 4,100 meters a.s.l. in Puebla, Mexico, the High-Altitude Water Cherenkov (HAWC) gamma-ray observatory detects TeV gamma-rays from astrophysical sources. Additionally, it gathers substantial data on hadronic air showers, expanding HAWC’s research capabilities to explore cosmic rays with energies from 1 TeV to 1 PeV. The initial energy estimation method optimized for...

37. Searching for Rare Astrophysical Events with Rare AI

Prof. Aobo Li

30/01/2025, 15:50

Rare event searches are fundamental to our understanding of crucial astrophysical phenomena, including neutrinoless double beta decay, dark matter detection, and binary black hole mergers. While artificial intelligence has revolutionized many scientific fields, its application to rare event searches presents unique challenges due to the inherent scarcity of training data. This talk presents...

33. Machine learning using NuDot

Masooma Sarfraz

30/01/2025, 16:35

Talk

NuDot is a ton-scale liquid scintillator research and development testbed. It aims to develop techniques to reduce one of the dominant backgrounds in large modern and future liquid scintillator neutrinoless double beta decay (0νββ) searches: the solar neutrino background. With the help of machine learning and high-speed electronics, NuDot will demonstrate the ability to extract directional...