11. 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, 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: 2782; Prenosov: 9
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12. Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep LearningO. 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: 2711; Prenosov: 8
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13. 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, 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: 2286; Prenosov: 8
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14. 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, izvirni znanstveni članek Ključne besede: time series, mel frequency, cough signal, wet cough, dry cough, phase portrait, mel coefficients, fractal dimension, neural network
Objavljeno v RUNG: 29.09.2023; Ogledov: 2338; Prenosov: 7
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15. Investigating the VHE gamma-ray sources using deep neural networksVeronika Vodeb, Saptashwa Bhattacharyya, G. Principe, Gabrijela Zaharijas, R. Austri, F. Stoppa, S. Caron, Denys 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: 3574; Prenosov: 7
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16. Power-aware Traffic Grooming in WDM Optical Mesh Networks for Bandwidth Wastage Minimization: A Genetic Algorithm-based ApproachSoumen Atta, Anirban Mukhopadhyay, 2012, objavljeni znanstveni prispevek na konferenci Opis: 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.
Ključne besede: Optical Network, WDM, Traffic Grooming, Network Components, Green Optical Network, Genetic Algorithm
Objavljeno v RUNG: 05.06.2023; Ogledov: 2604; Prenosov: 0
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17. Perturbation-minimising frequency assignment to address short term demand fluctuation in cellular networkSoumen Atta, Priya Ranjan Sinha Mahapatra, 2018, izvirni znanstveni članek Opis: In cellular network short term demand fluctuation is a very common phenomenon. The demand of any cell may increase or decrease slightly or the system may expand by adding additional cells or the system may shrink if the demands of certain number of cells become zero. In this paper, the perturbation-minimising frequency assignment problem (PMFAP) is considered to address the short term fluctuation in demand vector. PMFAP is a frequency assignment problem in which newly generated demands are satisfied with minimum changes in the already existing frequency assignment keeping all the interference constraints. In this paper, an efficient heuristic algorithm for PMFAP is presented. The efficiency of this algorithm is compared with the existing results from literature. With a slight modification to the proposed algorithm, it can solve the well-known frequency assignment problem (FAP) and its performance is also compared with the existing results using the standard benchmark data sets for FAP.
Ključne besede: short term demand fluctuation, frequency assignment problem, FAP, PMFAP, cellular network, perturbation, heuristic algorithm
Objavljeno v RUNG: 17.04.2023; Ogledov: 2750; Prenosov: 0
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18. A new variant of the p-hub location problem with a ring backbone network for content placement in VoD servicesSoumen Atta, Goutam Sen, 2021, izvirni znanstveni članek Opis: In this article, the single allocation p-hub location problem (SApHLP) with a ring backbone network for content placement in VoD services is proposed. In VoD services, a large volume of digital data is kept as data segments in spatially distributed hubs. In SApHLP, each user is restricted to be allocated only to a single hub, and here hubs form a ring backbone network. SApHLP jointly addresses (i) the locations of hubs, (ii) the placement of segments to hubs, (iii) the allocation of users to hubs as per their demands, and (iv) the optimal paths to route the demands from users to hubs. We have introduced network flow-based 3-subscripted and path-based 4-subscripted MILP formulations of SApHLP. This article presents a novel discrete particle swarm optimization (PSO)-based approach where factoradic numbers are used to encode solution. It also incorporates three problem-specific solution refinement methods for faster convergence. In this article, SApHLP instances are generated from a real-world database of video files obtained from a movie recommender system. The benchmark solutions are generated using IBM’s CPLEX optimizer with default settings and Benders decomposition strategy. The performance of the proposed PSO is compared with the benchmark results produced by CPLEX.
Ključne besede: Single allocation p-hub location problem, Ring backbone network, VoD services, Particle Swarm Optimization (PSO), Factoradics, CPLEX
Objavljeno v RUNG: 17.04.2023; Ogledov: 2064; Prenosov: 0
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19. Time-of-flight photoconductivity investigation of high charge carrier mobility in ▫$Ti_3C_2T_x$▫ MXenes thin-filmJurij Urbančič, Erika Tomsic, Manisha Chhikara, Nadiia Pastukhova, Vadym Tkachuk, Alex Dixon, Andraž Mavrič, Payam Hashemi, Davood Sabaghi, Ali Shaygan Nia, Gvido Bratina, Egon Pavlica, 2023, izvirni znanstveni članek Opis: Charge transport through a randomly oriented multilayered network of two-dimensional (2D) Ti3C2Tx (where Tx is the surface termination and corresponds to O, OH and F) was studied using time-of-flight photoconductivity (TOFP) method, which is highly sensitive to the distribution of charge carrier velocities. We prepared samples comprising Ti3C2Tx with thickness of 12 nm or 6-monolayers. MXene flakes of size up to 16 μm were randomly deposited on the surface by spin-coating from water solution. Using TOFP, we have measured electron mobility that reached values up to 279 cm2/Vs and increase with electric-field in a Poole-Frenkel manner. These values are approximately 50 times higher than previously reported field-effect mobility. Interestingly, our zero-electric-field extrapolate approaches electron mobility measured using terahertz absorption method, which represents intra-flake transport. Our data suggest that macroscopic charge transport is governed by two distinct mechanisms. The high mobility values are characteristic for the intra-flake charge transport via the manifold of delocalized states. On the other hand, the observed Poole-Frenkel dependence of charge carrier mobility on the electric field is typical for the disordered materials and suggest the existence of an important contribution of inter-flake hopping to the overall charge transport.
Ključne besede: charge transport, multilayered network, flakes, time-of-flight photoconductivity, MXene exfoliation, high-mobility solution-cast thin-film, semiconducting MXene
Objavljeno v RUNG: 31.03.2023; Ogledov: 3004; Prenosov: 9
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