arXiv subjects

Marat Zaidyn

Publications and source records attributed to Marat Zaidyn.

7 recordsLinked to original sources

arXiv2026

Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors

Core-collapse supernovae (CCSNe) are powerful sources of gravitational waves (GWs). These signals propagate essentially unobstructed, providing a unique probe of the supernova central engine. In this work, we investigate parameter estimation from the bounce and early ring-down GW signal of rotating CCSNe using machine learning. We infer the peak frequency and peak amplitude of the signal as well as the rotation of the core. We extend previous studies in several directions. We consider a range of progenitor models and nuclear equations of state, and we assess the impact of key physical uncertainties, including bounce-time uncertainty and source inclination. We incorporate both current detector noise and the projected sensitivities of next-generation observatories. We find that uncertainty in the bounce time does not significantly affect parameter estimation when the analysis is performed in the Fourier domain. In contrast, orientations when the rotation axis is near the line of sight substantially degrade performance. For optimal orientations, next-generation detectors can constrain rotation out to distances exceeding 100 kpc.

astro-ph.HE↗

arXiv2026

Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves

Gravitational waves from core-collapse supernovae offer a unique probe of the equation of state (EOS) of dense nuclear matter. For rapidly rotating stars, previous machine-learning studies demonstrated promising EOS classification accuracy. However, these analyses relied on several simplifying assumptions. In this work, we relax three key assumptions. First, we include real detector noise. Second, we expand the analysis from a single progenitor model to four models spanning 12 to 40 solar masses, and for each mass we consider multiple rotational configurations, from slow to rapid. Third, we introduce uncertainty in the core bounce time of up to 20 ms, rather than assuming it is known precisely. We find that none of these effects significantly degrades EOS classification performance. Instead, the larger dataset associated with multiple progenitor models and noise realizations improves training and classification accuracy. This study is a step in a broader effort to progressively incorporate more realistic conditions into gravitational-wave inference for core-collapse supernovae.

astro-ph.HE↗

arXiv2026

Evolution of fractality in centrally concentrated young clusters

We investigate the structural evolution of young star clusters forming within centrally concentrated molecular clouds. Our simulations use the Torch framework, which integrates the FLASH magnetohydrodynamics code with the AMUSE environment, enabling a self-consistent treatment of gas dynamics, star formation, stellar evolution, radiative transfer, and gravitational interactions. We quantify cluster structure using the $Q$ parameter for fractality and compute fractal dimensions via two methods: box-counting and correlation dimension. Our results show that clusters generally inherit fractal substructure from their parental clouds, which is typically erased within $\sim 2.5\,t_\mathrm{ff}$ through dynamical relaxation. Massive stars can induce the formation of secondary subclusters via feedback, with outcomes strongly dependent on stellar mass and formation timing. Interactions among subclusters, including mergers and dispersal, can extend fractal structure beyond $4\,t_\mathrm{ff}$. We also find systematic correlations between the fractality parameter $Q$ and the fractal dimension: fractality is positively correlated with both the correlation and box-counting dimensions, with the correlation dimension exhibiting a stronger correlation. These results demonstrate how stellar feedback and internal dynamics jointly shape the measurable fractal properties of embedded star clusters.

astro-ph.GA↗

arXiv2025

Probing Supernovae through gravitational wave entropy

We study an entropy-based framework to analyze gravitational-wave signals from core-collapse supernovae. We use waveforms generated by numerical simulations and analyze them in both the time domain and the time-frequency domain using short-time Fourier and continuous wavelet transforms. From each representation, we compute four entropy measures -- Shannon, exponential, R\'enyi, and Tsallis -- and apply three feature selection methods to identify the most informative features. We then train machine-learning classifiers on these features to compare the performance of different methodological combinations. We find that the combination of R\'enyi entropy from the wavelet domain and the Relief-F selection method yields the most effective discrimination among different gravitational-wave signals.

astro-ph.HE↗

arXiv2025

Machine learning-based classification of variable stars using phase-folded light curves

Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques continues to grow. Traditional methods often rely on manual feature extraction and selection, which can be labor-intensive and less effective for managing extensive datasets. In this study, we present a convolutional neural network (CNN)-based method for classifying variable stars using raw light curve data and their known periods. Our approach eliminates the need for manual feature extraction and preselected preprocessing steps. By applying phase-folding and interpolation to structure the light curves, the model learns variability patterns critical for accurate classification. Trained and evaluated on the All-Sky Automated Survey for Supernovae (ASAS-SN) dataset, our model achieves an average accuracy of 90% and an F1 score of 0.86 across six well-known classes of variable stars. The CNN effectively handles the diverse shapes and sampling cadences of light curves, offering a robust, automated, data-driven solution for classifying variable stars. This automated, data-driven method provides a robust solution for classifying variable stars, enabling the efficient analysis of large datasets from both current and future sky surveys.

astro-ph.SR↗

arXiv2025

The Role of Fractal Dimension in Wireless Mesh Network Performance

Wireless mesh networks (WMNs) depend on the spatial distribution of nodes, which directly influences connectivity, routing efficiency, and overall network performance. Conventional models typically assume uniform or random node placement, which inadequately represent the complex, hierarchical spatial patterns observed in practical deployments. In this study, we present a novel algorithm that constructs WMN topologies with tunable fractal dimensions, allowing precise control over spatial self-similarity. By systematically varying the fractal dimension, the algorithm generates network layouts spanning a continuum of spatial complexities, ranging from sparse fragmented clusters to dense, cohesive structures. Through NS-3 simulations, Key performance metrics including throughput, latency, jitter, and packet delivery ratio were evaluated across a range of fractal dimensions. Comparative evaluations against classical random, small-world, scale-free, grid and hierarchical tree networks models reveal that high-dimensional fractal topologies achieve enhanced resilience and throughput under equivalent conditions. These findings demonstrate the potential of fractal geometry as a design paradigm for scalable and efficient WMN architectures.

cs.NI↗

arXiv2025

Luminis Stellarum et Machina: Applications of Machine Learning in Light Curve Analysis

The rapid advancement of observational capabilities in astronomy has led to an exponential growth in the volume of light curve (LC) data, creating both opportunities and challenges for time-domain astronomy. Traditional analytical methods often struggle to fully extract the scientific value of these large and complex datasets. Machine learning (ML) algorithms are increasingly used for LC analysis, enabling classification, prediction, pattern discovery and anomaly detection. However, research in this area remains fragmented, with no comprehensive synthesis of how ML methods address the specific challenges of LC data. Key difficulties include class imbalance, noisy or sparse measurements, effective feature extraction, and limited interpretability of models. This lack of a unified overview makes it difficult for researchers to identify suitable approaches or recognize unresolved problems that require methodological advances. To address this gap, this survey systematically reviews ML techniques applied to LC analysis, outlining their principles and applications in tasks such as exoplanet detection, variable star classification and supernova identification. By clarifying the current state of the field and highlighting open challenges, this work provides guidance for future research and supports the effective integration of ML into astronomical big data studies.

astro-ph.IM↗