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Opis: Despite their discovery already more than a century ago, Cosmic Rays (CRs) still did not divulge all their properties yet. Theories about the origin of ultra-high energy (UHE, > 10^18 eV) CRs predict accompanying primary photons. The existence of UHE photons can be investigated with the world’s largest ground-based experiment for detection of CR-induced extensive air showers (EAS), the Pierre Auger Observatory, which offers an unprecedented exposure to rare UHE cosmic particles. The discovery of photons in the UHE regime would open a new observational window to the Universe, improve our understanding of the origin of CRs, and potentially uncloak new physics beyond the standard model. The novelty of the presented work is the development of a "real-time" photon candidate event stream to a global network of observatories, the Astrophysical Multimessenger Observatory Network (AMON). The stream classifies CR events observed by the Auger surface detector (SD) array as regards their probability to be photon nominees, by feeding to advanced machine learning (ML) methods observational air shower parameters of individual CR events combined in a multivariate analysis (MVA). The described straightforward classification procedure further increases the Pierre Auger Observatory’s endeavour to contribute to the global effort of multi-messenger (MM) studies of the highest energy astrophysical phenomena, by supplying AMON partner observatories the possibility to follow-up detected UHE events, live or in their archival data.
Ključne besede: astroparticle physics, ultra-high energy cosmic rays, ultra-high energy photons, extensive air showers, Pierre Auger Observatory, multi-messenger, AMON, machine learning, multivariate analysis, dissertations
Objavljeno v RUNG: 27.10.2021; Ogledov: 2829; Prenosov: 149
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Opis: 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.
Ključne besede: COVID-19, India, spread exposed infected recovered model, regression model, machine learning, predictions, forecasting
Objavljeno v RUNG: 01.04.2021; Ogledov: 2228; Prenosov: 83
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Opis: 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.
Ključne besede: astroparticle physics, ultra-high energy cosmic rays, extensive air showers, mass composition, Pierre Auger Observatory, machine learning, multivariate analysis
Objavljeno v RUNG: 03.04.2019; Ogledov: 4941; Prenosov: 186
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Opis: Features are the main rule building blocks for rule learning algorithms. They can be simple tests for attribute values or complex logical terms representing available domain knowledge. In contrast to common practice in classification rule learning, we argue that separation of the feature construction and rule construction processes has theoretical and practical justification. Explicit usage of features enables a unifying framework of both propositional and relational rule learning and we present and analyze procedures for feature construction in both types of domains. It is demonstrated that the presented procedure for constructing a set of simple features has the property that the resulting set enables construction of complete and consistent rules whenever it is possible, and that the set does not include obviously irrelevant features. Additionally, the concept of feature relevancy is important for the effectiveness of rule learning. It this work, we illustrate the concept in the coverage space and prove that the relative relevancy has the quality-preserving property in respect to the resulting rules. Moreover, we show that the transformation from the attribute to the feature space enables a novel, theoretically justified way of handling unknown attribute values. The same approach enables that estimated imprecision of continuous attributes can be taken into account, resulting in construction of robust features in respect to this imprecision.
Ključne besede: Machine learning, Feature construction, Rule learning, Unknown attribute values
Objavljeno v RUNG: 14.07.2017; Ogledov: 4343; Prenosov: 0
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