11. Mass composition of ultra-high energy cosmic rays at the Pierre Auger ObservatoryGašper Kukec Mezek, 2019, doctoral dissertation Abstract: Cosmic rays with energies above 10^18 eV, usually referred to as ultra-high energy cosmic rays (UHECR), have been a mystery from the moment they have been discovered. Although we have now more information on their extragalactic origin, their direct sources still remain hidden due to deviations caused by galactic magnetic fields. Another mystery, apart from their production sites, is their nature. Their mass composition, still uncertain at these energies, would give us a better understanding on their production, acceleration, propagation and capacity to produce extensive air showers in the Earth's atmosphere. Mass composition studies of UHECR try to determine their nature from the difference in development of their extensive air showers.
In this work, observational parameters from the hybrid detection system of the Pierre Auger Observatory are used in a multivariate analysis to obtain the mass composition of UHECR. The multivariate analysis (MVA) approach combines a number of mass composition sensitive variables and tries to improve the separation between different UHECR particle masses. Simulated distributions of different primary particles are fitted to measured observable distributions in order to determine individual elemental fractions of the composition. When including observables from the surface detector, we find a discrepancy in the estimated mass composition between a mixed simulation sample and the Pierre Auger data. Our analysis results from the Pierre Auger data are to a great degree independent on hadronic interaction models. Although they differ at higher primary masses, the different models are more consistent, when combining fractions of oxygen and iron. Compared to previously published results, the systematic uncertainty from hadronic interaction models is roughly four times smaller. Our analysis reports a predominantly heavy composition of UHECR, with more than a 50% fraction of oxygen and iron at low energies. The composition is then becoming heavier with increasing energy, with a fraction of oxygen and iron above 80% at the highest energies.
Found in: ključnih besedah
Summary of found: ...showers, mass composition, Pierre Auger Observatory, machine learning, multivariate analysis...
Keywords: astroparticle physics, ultra-high energy cosmic rays, extensive air showers, mass composition, Pierre Auger Observatory, machine learning, multivariate analysis
Published: 03.04.2019; Views: 3764; Downloads: 162
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13. Pregnancy and delivery in online communities: new ownership practices, new forms of technoscientific citizenshipLuca Buccoliero, Donatella Fontanot, Elena Bellio, Chiara Saviane, Yurji Castelfranchi, Giulia Annovi, Nico Pitrelli, Gianluigi Scannapieco, 2017, original scientific article Found in: ključnih besedah
Summary of found: ...knowledge, expertise, collective learning, web, technoscientific citizenship...
Keywords: knowledge, expertise, collective learning, web, technoscientific citizenship
Published: 23.08.2019; Views: 1762; Downloads: 0
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16. Air-Shower Reconstruction at the Pierre Auger Observatory based on Deep LearningLukas Zehrer, Marko Zavrtanik, Danilo Zavrtanik, Lili Yang, Serguei Vorobiov, Marta Trini, Samo Stanič, Gašper Kukec Mezek, Andrej Filipčič, Jonas Glombitza, 2019, published scientific conference contribution Found in: ključnih besedah
Keywords: Pierre Auger Observatory, extensive air showers, event reconstruction, deep learning
Published: 16.06.2020; Views: 1837; Downloads: 69
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17. Action Learning Sets - EmindS Training MaterialsHaris Tsitouras, Dimitrios Doukas, Kirsi Maasalo, Peter Purg, Petroula Mavrkiou, Maria Koutiva, Christiana Knais, reviewed university, higher education or higher vocational education textbook Found in: ključnih besedah
Keywords: action learning, materials, entrepreneurship, interdisciplinary, entrecomp
Published: 05.01.2021; Views: 1406; Downloads: 0
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18. When art gets more rigorous than science2020, radio or television broadcast Found in: ključnih besedah
Summary of found: ...bioart, anthropocene, methodologies, mixed research, temporal community, learning by sharing, sonic film, sound art...
Keywords: research, ethics, bioart, anthropocene, methodologies, mixed research, temporal community, learning by sharing, sonic film, sound art
Published: 25.02.2021; Views: 1413; Downloads: 17
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19. Machine learning models for government to predict COVID-19 outbreakPoonam Chaudhary, Saibal K. Pal, Rajan Gupta, Gaurav Pandey, 2020, original scientific article Abstract: The COVID-19 pandemic has become a major threat to the whole world. Analysis of this disease requires major attention by the government in all countries to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed and trained for Indian region till 10th May, 2020, and testing has been done for the number of cases for the next three weeks. Machine learning models such as SEIR model and Regression model have been used for predictions based on the data collected from the official portal of the Government of India in the time period of 30th January, 2020, to 10th May, 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0, which is the spread of the disease, was calculated to be 2.84. Expected cases are predicted around 175K--200K in the three-week time period of test data, which is very close to the actual numbers. This study will help the government and doctors in preparing their plans for the future.
Found in: ključnih besedah
Keywords: COVID-19, India, spread exposed infected recovered model, regression model, machine learning, predictions, forecasting
Published: 01.04.2021; Views: 1280; Downloads: 74
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20. Comparative analysis of epidemiological models for COVID-19 pandemic predictionsSaibal K. Pal, Rajan Gupta, Gaurav Pandey, 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.
Found in: ključnih besedah
Summary of found: ...(Holt’s exponential and ARIMA), and four machine/deep learning models (Neural Network, LSTM Networks, GANs, and...
Keywords: epidemic modeling, machine learning, neural networks, pandemic forecasting, time-series forecasting
Published: 15.07.2021; Views: 1321; Downloads: 25
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