11. Mass composition of Telescope Array's surface detectors events using deep learningI. Kharuk, Jon Paul Lundquist, 2022, objavljeni znanstveni prispevek na konferenci Opis: 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.
Ključne besede: Telescope Array, indirect detection, ground array, surface detection, ultra-high energy, cosmic rays, composition, deep learning, machine learning, neural networks
Objavljeno v RUNG: 29.09.2023; Ogledov: 1724; Prenosov: 5
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12. Sensitivity to keV-MeV dark matter from cosmic-ray scattering with current and the upcoming ground-based arrays CTA and SWGOIgor Reis, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, objavljeni znanstveni prispevek na konferenci Opis: A wealth of astrophysical and cosmological observational evidence shows that the matter content of the universe is made of about 85% of non-baryonic dark matter. Huge experimental efforts have been deployed to look for the direct detection of dark matter via their scattering on target nucleons, their production in colliders, and their indirect detection via their annihilation products. Inelastic scattering of high-energy cosmic rays off dark matter particles populating the Milky Way halo would produce secondary gamma rays in the final state from the decay of the neutral pions produced in such interactions, providing a new avenue to probe dark matter properties. We compute here the sensitivity for H.E.S.S.-like observatory, a current-generation ground-based Cherenkov telescopes, to the expected gamma-ray flux from collisions of Galactic cosmic rays and dark matter in the center of the Milky Way. We also derive sensitivity prospects for the upcoming Cherenkov Telescope Array (CTA) and Southern Wide-field Gamma-ray Observatory (SWGO). The expected sensitivity allows us to probe a poorly-constrained range of dark matter masses so far, ranging from keV to sub-GeV, and provide complementary constraints on the dark matter-proton scattering cross section traditionally probed by deep underground direct dark matter experiments.
Ključne besede: Cherenkov Telescope Array, CTA, very-high-energy gamma-ray astroparticle physics, instrument response functions, machine learning
Objavljeno v RUNG: 26.09.2023; Ogledov: 1748; Prenosov: 8
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13. Detecting and characterizing pulsar halos with the Cherenkov Telescope Array ObservatoryChristopher Eckner, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, objavljeni znanstveni prispevek na konferenci Opis: The recently identified source class of pulsar halos may be populated and bright enough at TeV energies to constitute a large fraction of the sources that will be observed with the Cherenkov Telescope Array (CTA), especially in the context of the planned Galactic Plane Survey (GPS). In this study, we examine the prospects offered by CTA for the detection and characterization of such objects. CTA will cover energies from 20 GeV to 300 TeV, bridging the ranges already probed with the Fermi Large Area Telescope and High Altitude Water Cherenkov Observatory, and will also have a better angular resolution than the latter instruments, thus providing a complementary look at the phenomenon. From simple models for individual pulsar halos and their population in the Milky Way, we examine under which conditions such sources can be detected and studied from the GPS observations. In the framework of a full spatial-spectral likelihood analysis, using the most recent estimates for the instrument response function and prototypes for the science tools, we derive the spectral and morphological sensitivity of the CTA GPS to the specific intensity distribution of pulsar halos. From these, we quantify the physical parameters for which pulsar halos can be detected, identified, and characterized, and what fraction of the Galactic population could be accessible. We also discuss the effect of interstellar emission and data analysis systematics on these prospects.
Ključne besede: Cherenkov Telescope Array, CTA, very-high-energy gamma-ray astroparticle physics, instrument response functions, machine learning
Objavljeno v RUNG: 26.09.2023; Ogledov: 1457; Prenosov: 8
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14. Performance update of an event-type based analysis for the Cherenkov Telescope ArrayJ. Bernete, Saptashwa Bhattacharyya, Judit Pérez Romero, Samo Stanič, Veronika Vodeb, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Miha Živec, 2023, objavljeni znanstveni prispevek na konferenci Opis: The Cherenkov Telescope Array (CTA) will be the next-generation observatory in the field of very-high-energy (20 GeV to 300 TeV) gamma-ray astroparticle physics. The traditional approach to data analysis in this field is to apply quality cuts, optimized using Monte Carlo simulations, on the data acquired to maximize sensitivity. Subsequent steps of the analysis typically use the surviving events to calculate one set of instrument response functions (IRFs) to physically interpret the results. However, an alternative approach is the use of event types, as implemented in experiments such as the Fermi-LAT. This approach divides events into sub-samples based on their reconstruction quality, and a set of IRFs is calculated for each sub-sample. The sub-samples are then combined in a joint analysis, treating them as independent observations. In previous works we demonstrated that event types, classified using Machine Learning methods according to their expected angular reconstruction quality, have the potential to significantly improve the CTA angular and energy resolution of a point-like source analysis. Now, we validated the production of event-type wise full-enclosure IRFs, ready to be used with science tools (such as Gammapy and ctools). We will report on the impact of using such an event-type classification on CTA high-level performance, compared to the traditional procedure.
Ključne besede: Cherenkov Telescope Array, CTA, very-high-energy gamma-ray astroparticle physics, instrument response functions, machine learning
Objavljeno v RUNG: 26.09.2023; Ogledov: 1853; Prenosov: 8
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17. Neural net pattern recognition based auscultation of croup cough and pertussis using phase portrait featuresMohanachandran Nair Sindhu Swapna, 2021, izvirni znanstveni članek Opis: Cough signal analysis for understanding the pathological condition has become important from the outset of the exigency posed by the epidemic COVID-19. The present work suggests a surrogate approach for the classification of cough signals - croup cough (CC) and pertussis (PT) – based on spectral, fractal, and nonlinear time-series techniques. The spectral analysis of CC reveals the presence of more frequency components in the short duration cough sound compared to PT. The musical nature of CC is unveiled not only through the spectral analysis but also through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and Hurst exponent (Hb). The modifications in the internal morphology of the respiratory tract, giving rise to more frequency components associated with the complex airflow dynamics, get staged through the higher fractal dimension of CC. Among the two supervised classification tools, cubic KNN (CKNN) and neural net pattern recognition (NNPR), used for classifying the CC and PT signals based on nonlinear time series parameters, NNPR is found better. Thus, the study opens the possibility of identification of pulmonary pathological conditions through cough sound signal analysis.
Ključne besede: Croup cough
Pertussis
Fractal dimension
Phase portrait
Sample entropy
Machine learning techniques
Objavljeno v RUNG: 04.07.2022; Ogledov: 1837; Prenosov: 0
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18. Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: A machine learning approachMohanachandran Nair Sindhu Swapna, 2021, izvirni znanstveni članek Opis: The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.
Ključne besede: Auscultation, Biomedical signal processing, Fractals, Machine learning, Phase portrait, Pulmonary crackle.
Objavljeno v RUNG: 30.06.2022; Ogledov: 1717; Prenosov: 0
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19. Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approachMohanachandran Nair Sindhu Swapna, 2022, izvirni znanstveni članek Opis: Objectives: The present work reports the study of 34 rhonchi (RB) and
Bronchial Breath (BB) signals employing machine learning techniques, timefrequency, fractal, and non-linear time-series analyses. Methods: The timefrequency analyses and the complexity in the dynamics of airflow in BB
and RB are studied using both Power Spectral Density (PSD) features and
non-linear measures. For accurate prediction of these signals, PSD and nonlinear measures are fed as input attributes to various machine learning
models. Findings: The spectral analyses reveal fewer, low-intensity frequency
components along with its overtones in the intermittent and rapidly damping
RB signal. The complexity in the dynamics of airflow in BB and RB is investigated
through the fractal dimension, Hurst exponent, phase portrait, maximal
Lyapunov exponent, and sample entropy values. The greater value of entropy
for the RB signal provides an insight into the internal morphology of the airways
containing mucous and other obstructions. The Principal Component Analysis
(PCA) employs PSD features, and Linear Discriminant Analysis (LDA) along
with Pattern Recognition Neural Network (PRNN) uses non-linear measures
for predicting BB and RB. Signal classification based on phase portrait
features evaluates the multidimensional aspects of signal intensities, whereas
that based on PSD features considers mere signal intensities. The principal
components in PCA cover about 86.5% of the overall variance of the data class,
successfully distinguishing BB and RB signals. LDA and PRNN that use nonlinear time-series parameters identify and predict RB and BB signals with 100%
accuracy, sensitivity, specificity, and precision. Novelty: The study divulges the
potential of non-linear measures and PSD features in classifying these signals
enabling its application to be extended for low-cost, non-invasive COVID-19
detection and real-time health monitoring.
Ključne besede: lung signal, fractal analysis, sample entropy, nonlinear timeseries, machine learning techniques
Objavljeno v RUNG: 30.06.2022; Ogledov: 2137; Prenosov: 0
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20. Complex network-based cough signal analysis for digital auscultation: a machine learning approachMohanachandran Nair Sindhu Swapna, 2022, izvirni znanstveni članek Opis: The paper proposes a novel approach to bring out the potential of complex networks based on graph theory to unwrap the hidden characteristics of cough signals, croup (BC), and pertussis (PS). The spectral and complex network analyses of 48 cough sounds are utilized for understanding the airflow through the infected respiratory tract. Among the different phases of the cough sound time-domain signals of BC and PS – expulsive (X), intermediate (I), and voiced (V) - the phase ‘I’ is noisy in BC due to improper glottal functioning. The spectral analyses reveal high-frequency components in both cough signals with an additional high-intense low-frequency spread in BC. The complex network features created by the correlation mapping approach, like number of edges (E), graph density (G), transitivity (), degree centrality (D), average path length (L), and number of components () distinguishes BC and PS. The higher values of E, G, and for BC indicate its musical nature through the strong correlation between the signal segments and the presence of high-intense low-frequency components in BC, unlike that in PS. The values of D, L, and discriminate BC and PS in terms of the strength of the correlation between the nodes within them. The linear discriminant analysis (LDA) and quadratic support vector machine (QSVM) classifies BC and PS, with greater accuracy of 94.11% for LDA. The proposed work opens up the potentiality of employing complex networks for cough sound analysis, which is vital in the current scenario of COVID-19.
Ključne besede: Complex network analysis, Auscultation, Croup cough, Pertussis
Spectral analysis, Machine learning techniques
Objavljeno v RUNG: 30.06.2022; Ogledov: 2554; Prenosov: 0
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