1. The time evolution of the surface detector of the Pierre Auger ObservatoryOrazio Zapparrata, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2023, published scientific conference contribution Abstract: 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.
Keywords: surface detector, Pierre Auger Observatory, neural networks, air-shower simulations
Published in RUNG: 22.01.2024; Views: 360; Downloads: 4
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2. Mass composition of Telescope Array's surface detectors events using deep learningI. 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: 721; Downloads: 4
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3. Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array dataJann Aschersleben, Saptashwa Bhattacharyya, Barbara MARČUN, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Gabrijela Zaharijas, Marko Zavrtanik, Danilo Zavrtanik, Miha Živec, 2021, published scientific conference contribution Abstract: 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.
Keywords: Cherenkov Telescope Array, very-high-energy astronomy, convolutional neural networks
Published in RUNG: 18.09.2023; Views: 517; Downloads: 4
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4. Comparative analysis of epidemiological models for COVID-19 pandemic predictionsRajan Gupta, Gaurav Pandey, Saibal K. Pal, 2021, original scientific article Abstract: Epidemiological modeling is an important problem around the world. This research presents COVID-19 analysis to understand which model works better for different regions. A comparative analysis of three growth curve fitting models (Gompertz, Logistic, and Exponential), two mathematical models (SEIR and IDEA), two forecasting models (Holt’s exponential and ARIMA), and four machine/deep learning models (Neural Network, LSTM Networks, GANs, and Random Forest) using three evaluation criteria on ten prominent regions around the world from North America, South America, Europe, and Asia has been presented. The minimum and median values for RMSE were 1.8 and 5372.9; the values for the mean absolute percentage error were 0.005
and 6.63; and the values for AIC were 87.07 and 613.3, respectively, from a total of 125 experiments across 10 regions. The growth curve fitting models worked well where flattening of the cases has started. Based on region’s growth curve, a relevant model from the list can be used for predicting the number of infected cases for COVID-19. Some other models used in forecasting the number of cases have been added in the future work section, which can help researchers to forecast the number of cases in different regions of the world.
Keywords: epidemic modeling, machine learning, neural networks, pandemic forecasting, time-series forecasting
Published in RUNG: 15.07.2021; Views: 2232; Downloads: 33
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