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1.
Complex network based Fourier analysis for signal processing
Vijayan Vijesh, K. Satheesh Kumar, Mohanachandran Nair Sindhu Swapna, Sankaranarayana Iyer Sankararaman, 2024, published scientific conference contribution

Keywords: fourier analysis, complex network, signal processing
Published in RUNG: 15.04.2024; Views: 597; Downloads: 0
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2.
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, 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: 566; Downloads: 4
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3.
Max-type reliability in uncertain post-disaster networks through the lens of sensitivity and stability analysis
Ahmad Hosseini, 2024, original scientific article

Abstract: The functionality of infrastructures, particularly in densely populated areas, is greatly impacted by natural disasters, resulting in uncertain networks. Thus, it is important for crisis management professionals and computer-based systems for transportation networks (such as expert systems) to utilize trustworthy data and robust computational methodologies when addressing convoluted decision-making predicaments concerning the design of transportation networks and optimal routes. This study aims to evaluate the vulnerability of paths in post-disaster transportation networks, with the aim of facilitating rescue operations and ensuring the safe delivery of supplies to affected regions. To investigate the problem of links' tolerances in uncertain networks and the resiliency and reliability of paths, an uncertainty theory-based model that employs minmax optimization with a bottleneck objective function is used. The model addresses the uncertain maximum reliable paths problem, which takes into account uncertain risk variables associated with links. Rather than using conventional methods for calculating the deterministic tolerances of a single element in combinatorial optimization, this study introduces a generalization of stability analysis based on tolerances while the perturbations in a group of links are involved. The analysis defines set tolerances that specify the minimum and maximum values that a designated group of links could simultaneously fluctuate while maintaining the optimality of the max-type reliable paths. The study shows that set tolerances can be considered as well-defined and proposes computational methods to calculate or bound such quantities - which were previously unresearched and difficult to measure. The model and methods are demonstrated to be both theoretically and numerically efficient by applying them to four subnetworks from our case study. In conclusion, this study provides a comprehensive approach to addressing uncertainty in reliability problems in networks, with potential applications in various fields.
Keywords: Disaster Management, Network Reliability, Stability Analysis, Transportation, Uncertainty
Published in RUNG: 24.11.2023; Views: 649; Downloads: 3
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4.
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, 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: 936; Downloads: 6
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5.
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, 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: 899; Downloads: 6
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6.
Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning
O. 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: 831; Downloads: 6
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7.
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, 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: 858; Downloads: 5
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8.
9.
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, 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: 1082; Downloads: 6
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10.
Power-aware Traffic Grooming in WDM Optical Mesh Networks for Bandwidth Wastage Minimization: A Genetic Algorithm-based Approach
Soumen Atta, Anirban Mukhopadhyay, 2012, published scientific conference contribution

Abstract: The cost of optical backbone network has increased nowadays. So we need to reduce this cost. One of the major contributory costs is the power consumed by the underlying network. Power may also be consumed by different network equipments viz. add-drop multiplexers (ADM), Network Interface Device (NID), Optical Network Terminal (ONT), electrical-to-optical-to-electrical (EOE) conversion etc. In this article we have only considered the power consumption by EOE conversion in a mesh network. We have proposed a genetic algorithm to minimize the EOE conversions needed for a mesh network to satisfy all the traffic requests for a given physical topology. We have also considered the amount of wavelength wastages for our solution and we have minimized these wastages below a user given value. The results have been demonstrated on two optical mesh networks.
Keywords: Optical Network, WDM, Traffic Grooming, Network Components, Green Optical Network, Genetic Algorithm
Published in RUNG: 05.06.2023; Views: 1093; Downloads: 0
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