1. Investigations of a novel energy estimator using deep learning for the surface detector of the Pierre Auger ObservatoryFiona Ellwanger, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2023, published scientific conference contribution Abstract: 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.
Keywords: ultra-high energy cosmic rays, Pierre Auger Observatory, surface detector, neural network
Published in RUNG: 22.01.2024; Views: 358; Downloads: 4
 Full text (1,78 MB)
This document has many files! More... |
2. Search for EeV photon-induced events at the Telescope ArrayI. Kharuk, R. U. Abbasi, Y. Abe, T. Abu-Zayyad, M. Allen, Yasuhiko Arai, R. Arimura, E. Barcikowski, J. W. Belz, Douglas R. Bergman, 2023, published scientific conference contribution Abstract: 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.
Keywords: Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, photons, neural network, machine learning
Published in RUNG: 09.10.2023; Views: 719; Downloads: 6
Full text (543,46 KB)
This document has many files! More... |
3. Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural NetworksJ.M. Carceller, Andrej Filipčič, Jon Paul Lundquist, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2022, published scientific conference contribution Abstract: 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.
Keywords: Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, muons, machine learning, recurrent neural network
Published in RUNG: 04.10.2023; Views: 678; Downloads: 6
Full text (1,08 MB)
This document has many files! More... |
4. Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep LearningO. Kalashev, Jon Paul Lundquist, 2022, published scientific conference contribution Abstract: 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.
Keywords: Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, energy, arrival directions, reconstruction, machine learning, neural network
Published in RUNG: 04.10.2023; Views: 592; Downloads: 6
Full text (1,10 MB)
This document has many files! More... |
5. Event-by-event reconstruction of the shower maximum Xmax with the Surface Detector of the Pierre Auger Observatory using deep learningJ. Glombitza, Andrej Filipčič, Jon Paul Lundquist, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2022, published scientific conference contribution Abstract: 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.
Keywords: Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, neural network, machine learning
Published in RUNG: 29.09.2023; Views: 666; Downloads: 5
Full text (1,66 MB)
This document has many files! More... |
6. Time series and mel frequency analyses of wet and dry cough signals : a neural net classificationAmmini Renjini, Mohanachandran Nair Sindhu Swapna, K. Satheesh Kumar, Sankaranarayana Iyer Sankararaman, 2023, original scientific article Keywords: time series, mel frequency, cough signal, wet cough, dry cough, phase portrait, mel coefficients, fractal dimension, neural network
Published in RUNG: 29.09.2023; Views: 767; Downloads: 5
 Link to file
This document has many files! More... |
7. Investigating the VHE gamma-ray sources using deep neural networksVeronika Vodeb, Saptashwa Bhattacharyya, G. Principe, Gabrijela Zaharijas, R. Austri, F. Stoppa, S. Caron, D. Malyshev, 2023, published scientific conference contribution Abstract: 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∘Keywords: deep neural network, cosmic-rays, CTA, classification
Published in RUNG: 31.08.2023; Views: 715; Downloads: 6
Full text (962,45 KB)
This document has many files! More... |
8. Graph based feature extraction and classification of wet and dry cough signals: A machine learning approachMohanachandran Nair Sindhu Swapna, 2021, original scientific article Abstract: This article proposes a unique approach to bring out the potential of graph-based features to reveal the
hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various
respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are
employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The
different phases of WE and DE are observed from their time-domain signals, indicating the operation of the
glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract.
The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density
and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better
distinguishability between WE and DE is obtained through the supervised machine learning techniques
(MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to
the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision
of 97.00% suggests NN as a better classifier using complex network features. The study opens up the
possibility of complex network analysis in remote auscultation.
Keywords: wet cough, dry cough, complex network, quadratic SVM, neural net
Published in RUNG: 30.06.2022; Views: 1063; Downloads: 0
This document has many files! More... |
9. |
