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1.
Novel approach to fish classification : fractalysis and machine learning-based approach
Jothy Thampy, Mohanachandran Nair Sindhu Swapna, Sankaranarayana Iyer Sankararaman, 2024, published scientific conference contribution

Keywords: fractals, fish, machine learning
Published in RUNG: 15.04.2024; Views: 713; Downloads: 1
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2.
Bodies of noise at the Bell Laboratories : early automated speech recognition, contribution at the Editorial Workshop - A Special Issue on Acoustic Space, November 9-10, 2022, Frankfurt/Main
Eszter Polónyi, 2022, other performed works

Abstract: This paper is about the first automated systems developed to recognize identity. While automated recognition in the twenty-first century is widely associated with images of the human face, its roots are to be found in attempts to visualize identity in other, non-figural types of trace left by human bodies, ranging as widely as shadows, astrological signs, handwriting, the prints left by palms and fingers and the acoustics of the human voice. This paper investigates one such system of recognition as it emerged from within the telecommunications industry context in the midcentury U.S. Ostensibly built to reduce human labor and cable bandwidth, Bell Labs developed three different phone devices in the 1950s to photograph, formalize and analyze the sounds of speech as they traveled through the telephony system. And while the device called “Audrey” indeed succeeded in recognizing spoken digits, it was its failure to recognize the speech contents without prior awareness of the identity of the speaker, that is to distinguish between the individuality of the speaking “medium” and their intended meaning, that arguably made the experiment a landmark in the history of machine-driven recognition. Accounting for the “noise” made by the body and the environment from which sound emanated into the device, which the lab’s technicians defined as ranging from “speech defects” to “inflection” and “background interference” proved more important than phonetic analysis in determining the intended message of given speech spectogram. Similarly to a range of experiments with noise by formalist filmmakers such as Tony Conrad, John Cage, Kurt Kren and others, it was on the principle of contingency and irreproducible uniqueness that Bell Lab technicians sought to train machine-driven intelligence.
Keywords: History of computer science, machine learning, Bell Labs, history of telecommunications, sound studies
Published in RUNG: 19.02.2024; Views: 704; Downloads: 6
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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, 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: 1073; Downloads: 6
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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, 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: 1039; Downloads: 6
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5.
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: 987; Downloads: 6
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6.
Mass composition anisotropy with the Telescope Array Surface Detector data
Y. Zhezher, Jon Paul Lundquist, 2022, published scientific conference contribution

Abstract: Mass composition anisotropy is predicted by a number of theories describing sources of ultra-high-energy cosmic rays. Event-by-event determination of a type of a primary cosmic-ray particle is impossible due to large shower-to-shower fluctuations, and the mass composition usually is obtained by averaging over some composition-sensitive observable determined independently for each extensive air shower (EAS) over a large number of events. In the present study we propose to employ the observable ξ used in the TA mass composition analysis for the mass composition anisotropy analysis. The ξ variable is determined with the use of Boosted Decision Trees (BDT) technique trained with the Monte-Carlo sets, and the ξ value is assigned for each event, where ξ=1 corresponds to an event initiated by the primary iron nuclei and ξ=−1 corresponds to a proton event. Use of ξ distributions obtained for the Monte-Carlo sets allows us to separate proton and iron candidate events from a data set with some given accuracy and study its distributions over the observed part of the sky. Results for the TA SD 11-year data set mass composition anisotropy will be presented.
Keywords: Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, anisotropy, machine learning, boosted decision tree
Published in RUNG: 04.10.2023; Views: 876; Downloads: 5
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7.
Cosmic-ray mass composition with the TA SD 12-year data
Y. Zhezher, Jon Paul Lundquist, 2022, published scientific conference contribution

Abstract: Telescope Array (TA) is the largest ultra-high-energy cosmic-ray (UHECR) observatory in the Northern Hemisphere. It is dedicated to detect extensive air showers (EAS) in hybrid mode, both by measuring the shower’s longitudinal profile with fluorescence telescopes and their particle footprint on the ground from the surface detector (SD) array. While fluorescence telescopes can measure the most composition-sensitive characteristic of EAS, the depth of the shower maximum (\xmax), they also have the drawback of small duty cycle. This work aims to study the UHECR composition based solely on the surface detector data. For this task, a set of composition-sensitive observables obtained from the SD data is used in a machine-learning method -- the Boosted Decision Trees. We will present the results of the UHECR mass composition based on the 12-year data from the TA SD using this technique, and we will discuss of the possible systematics imposed by the hadronic interaction models.
Keywords: Telescope Array, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, machine learning, boosted decision tree
Published in RUNG: 04.10.2023; Views: 872; Downloads: 7
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8.
Telescope Array 10-Year Monocular Spectrum Measurement
D. Bergman, Jon Paul Lundquist, 2022, published scientific conference contribution

Abstract: Telescope Array (TA) is the largest cosmic ray detector in the northern hemisphere. We present a measurement of the cosmic ray energy spectrum for energies above 10^17.5 eV using fluorescence telescopes in monocular mode. A novel weather classification scheme using machine learning was used to select data parts with good weather to ensure the quality of the fluorescence data. The data from the Black Rock Mesa (BR) and Long Ridge (LR) fluorescence telescope stations were analysed separately in monocular mode, with the calculated fluxes combined into a single spectrum. The 10-year monocular combined cosmic ray energy spectrum is in excellent agreement with previous measurements from the northern hemisphere. We present fits of the combined spectrum to a series of broken power law models. A thrice-broken power law is observed to be the best fit considering the Poisson deviance per degrees of freedom. The three breaks suggest an additional feature of the spectrum between the previously observed Ankle at 10^18.7 eV and the GZK suppression at 10^19.8 eV.
Keywords: Telescope Array, indirect detection, fluorescence detection, ultra-high energy, cosmic rays, energy spectrum, composition, machine learning, weather classification
Published in RUNG: 02.10.2023; Views: 1021; Downloads: 5
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9.
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: 959; Downloads: 6
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10.
Mass composition of Telescope Array's surface detectors events using deep learning
I. Kharuk, Jon Paul Lundquist, 2022, published scientific conference contribution

Abstract: 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.
Keywords: Telescope Array, indirect detection, ground array, surface detection, ultra-high energy, cosmic rays, composition, deep learning, machine learning, neural networks
Published in RUNG: 29.09.2023; Views: 1087; Downloads: 4
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