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1.
Investigations of a novel energy estimator using deep learning for the surface detector of the Pierre Auger Observatory
Fiona Ellwanger, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2023, objavljeni znanstveni prispevek na konferenci

Opis: Exploring physics at energies beyond the reach of human-built accelerators by studying cosmic rays requires an accurate reconstruction of their energy. At the highest energies, cosmic rays are indirectly measured by observing a shower of secondary particles produced by their interaction in the atmosphere. At the Pierre Auger Observatory, the energy of the primary particle is either reconstructed from measurements of the emitted fluorescence light, produced when secondary particles travel through the atmosphere, or shower particles detected with the surface detector at the ground. The surface detector comprises a triangular grid of water-Cherenkov detectors that measure the shower footprint at the ground level. With deep learning, large simulation data sets can be used to train neural networks for reconstruction purposes. In this work, we present an application of a neural network to estimate the energy of the primary particle from the surface detector data by exploiting the time structure of the particle footprint. When evaluating the precision of the method on air shower simulations, we find the potential to significantly reduce the composition bias compared to methods based on fitting the lateral signal distribution. Furthermore, we investigate possible biases arising from systematic differences between simulations and data.
Ključne besede: ultra-high energy cosmic rays, Pierre Auger Observatory, surface detector, neural network
Objavljeno v RUNG: 22.01.2024; Ogledov: 577; Prenosov: 4
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2.
The time evolution of the surface detector of the Pierre Auger Observatory
Orazio Zapparrata, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2023, objavljeni znanstveni prispevek na konferenci

Opis: The surface detector array of the Pierre Auger Observatory, consisting of 1660 water Cherenkov tanks, has been in operation for nearly 20 years. During this long period of data acquisition, ageing effects in the detector response have been observed. The temporal evolution of the signals recorded by the surface detector is mostly compensated by continuous calibration with atmospheric muons; however, effects persist in the signal rise time and in high-level data analysis using neural networks. We have implemented a detailed description of the time evolution of the detector response and of the uptimes of individual stations in GEANT4-based detector simulations. These new simulations reproduce the observed time dependencies in the data. Using air-shower simulations that take into account the evolution of individual stations, we show that the reconstructed energy is stable at the sub-percent level, and its resolution is affected by less than 5% in 15 years. For a few specific stations, the collected light produced by muons has decreased to the point where it is difficult to distinguish it from the electromagnetic background in the calibration histograms. The upgrade of the Observatory with scintillator detectors mitigates this problem: by requiring a coincidence between the water-Cherenkov and scintillator detectors, we can enhance the muon relative contribution to the calibration histogram. We present the impact and performance of this coincidence calibration method.
Ključne besede: surface detector, Pierre Auger Observatory, neural networks, air-shower simulations
Objavljeno v RUNG: 22.01.2024; Ogledov: 673; Prenosov: 4
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3.
Search for EeV photon-induced events at the Telescope Array
I. Kharuk, R. U. Abbasi, Y. Abe, T. Abu-Zayyad, M. Allen, Yasuhiko Arai, R. Arimura, E. Barcikowski, J. W. Belz, Douglas R. Bergman, 2023, objavljeni znanstveni prispevek na konferenci

Opis: We report on the updated results on the search for photon-like-induced events in the data, collected by Telescope Array's Surface Detectors during the last 14 years. In order to search for photon-like-induced events, we trained a neural network on Monte-Carlo simulated data to distinguish between the proton-induced and photon-induced air showers. Both reconstructed composition-sensitive parameters and raw signals registered by the Surface Detectors are used as input data for the neural network. The classification threshold was optimized to provide the strongest possible constraint on the photons' flux.
Ključne besede: Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, photons, neural network, machine learning
Objavljeno v RUNG: 09.10.2023; Ogledov: 944; Prenosov: 6
.pdf Celotno besedilo (543,46 KB)
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4.
Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks
J.M. Carceller, Andrej Filipčič, Jon Paul Lundquist, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2022, objavljeni znanstveni prispevek na konferenci

Opis: We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 10^18.5 eV and 10^20 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration.
Ključne besede: Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, muons, machine learning, recurrent neural network
Objavljeno v RUNG: 04.10.2023; Ogledov: 906; Prenosov: 6
.pdf Celotno besedilo (1,08 MB)
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5.
Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning
O. Kalashev, Jon Paul Lundquist, 2022, objavljeni znanstveni prispevek na konferenci

Opis: A novel ultra-high-energy cosmic rays energy and arrival direction reconstruction method for Telescope Array surface detector is presented. The analysis is based on a deep convolutional neural network using detector signal time series as the input and the network is trained on a large Monte-Carlo dataset. This method is compared in terms of statistical and systematic energy and arrival direction determination errors with the standard Telescope Array surface detector event reconstruction procedure.
Ključne besede: Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, energy, arrival directions, reconstruction, machine learning, neural network
Objavljeno v RUNG: 04.10.2023; Ogledov: 835; Prenosov: 6
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6.
Event-by-event reconstruction of the shower maximum Xmax with the Surface Detector of the Pierre Auger Observatory using deep learning
J. Glombitza, Andrej Filipčič, Jon Paul Lundquist, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2022, objavljeni znanstveni prispevek na konferenci

Opis: The measurement of the mass composition of ultra-high energy cosmic rays constitutes a prime challenge in astroparticle physics. Most detailed information on the composition can be obtained from measurements of the depth of maximum of air showers, Xmax, with the use of fluorescence telescopes, which can be operated only during clear and moonless nights. Using deep neural networks, it is now possible for the first time to perform an event-by-event reconstruction of Xmax with the Surface Detector (SD) of the Pierre Auger Observatory. Therefore, previously recorded data can be analyzed for information on Xmax, and thus, the cosmic-ray composition. Since the SD operates with a duty cycle of almost 100% and its event selection is less strict than for the Fluorescence Detector (FD), the gain in statistics with respect to the FD is almost a factor of 15 for energies above 10^19.5 eV. In this contribution, we introduce the neural network particularly designed for the SD of the Pierre Auger Observatory. We evaluate its performance using three different hadronic interaction models, verify its functionality using Auger hybrid measurements, and find that the method can extract mass information on an event level.
Ključne besede: Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, neural network, machine learning
Objavljeno v RUNG: 29.09.2023; Ogledov: 864; Prenosov: 5
.pdf Celotno besedilo (1,66 MB)
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7.
Mass composition of Telescope Array's surface detectors events using deep learning
I. Kharuk, Jon Paul Lundquist, 2022, objavljeni znanstveni prispevek na konferenci

Opis: We report on an improvement of deep learning techniques used for identifying primary particles of atmospheric air showers. The progress was achieved by using two neural networks. The first works as a classifier for individual events, while the second predicts fractions of elements in an ensemble of events based on the inference of the first network. For a fixed hadronic model, this approach yields an accuracy of 90% in identifying fractions of elements in an ensemble of events.
Ključne besede: Telescope Array, indirect detection, ground array, surface detection, ultra-high energy, cosmic rays, composition, deep learning, machine learning, neural networks
Objavljeno v RUNG: 29.09.2023; Ogledov: 968; Prenosov: 4
.pdf Celotno besedilo (788,87 KB)
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8.
9.
Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data
Jann Aschersleben, Saptashwa Bhattacharyya, Barbara MARČUN, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Gabrijela Zaharijas, Marko Zavrtanik, Danilo Zavrtanik, Miha Živec, 2021, objavljeni znanstveni prispevek na konferenci

Opis: The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, o˙ering 5 − 10 × better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for convolutional neural networks (CNNs) to determine the energy of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA. Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is di˙erent from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance.
Ključne besede: Cherenkov Telescope Array, very-high-energy astronomy, convolutional neural networks
Objavljeno v RUNG: 18.09.2023; Ogledov: 725; Prenosov: 4
.pdf Celotno besedilo (1,24 MB)
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10.
Investigating the VHE gamma-ray sources using deep neural networks
Veronika Vodeb, Saptashwa Bhattacharyya, G. Principe, Gabrijela Zaharijas, R. Austri, F. Stoppa, S. Caron, D. Malyshev, 2023, objavljeni znanstveni prispevek na konferenci

Opis: The upcoming Cherenkov Telescope Array (CTA) will dramatically improve the point-source sensitivity compared to the current Imaging Atmospheric Cherenkov Telescopes (IACTs). One of the key science projects of CTA will be a survey of the whole Galactic plane (GPS) using both southern and northern observatories, specifically focusing on the inner galactic region. We extend a deep learning-based image segmentation software pipeline (autosource-id) developed on Fermi-LAT data to detect and classify extended sources for the simulated CTA GPS. Using updated instrument response functions for CTA (Prod5), we test this pipeline on simulated gamma-ray sources lying in the inner galactic region (specifically 0∘Ključne besede: deep neural network, cosmic-rays, CTA, classification
Objavljeno v RUNG: 31.08.2023; Ogledov: 1093; Prenosov: 6
.pdf Celotno besedilo (962,45 KB)
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