arXiv subjects

Ernazar Abdikamalov

Publications and source records attributed to Ernazar Abdikamalov.

At least 19 recordsLinked to original sources

arXiv2026

EP250302a: violent shell collision in a soft-X-ray-selected GRB-like transient

The Einstein Probe opens a previously unexplored soft X-ray window onto gamma-ray bursts, filling a critical observational gap in the soft X-ray coverage of their prompt emission. In this letter, we present EP250302a, a soft-X-ray-selected, GRB-like transient at $z=1.131$ detected by the Einstein Probe. Follow-up observations from X-ray to radio reveal a narrow X-ray flare at $\sim 1.1$\, ks and subsequent achromatic optical and X-ray rebrightening. These features challenge a standard single-component afterglow model and indicate the need for multiple ejecta components. A violent collision between a late relativistic shell and the decelerated leading blast wave provides a plausible interpretation: the flare arises from internal dissipation of the late ejecta, while the rebrightening is powered by the shocked emission produced in the collision. Quantitative modeling constrains the kinetic energy ratio between the late shell and the initial ejecta to $E_{\rm k,iso,2}/E_{\rm k,iso,1} \sim 5$ (with $E_{\rm k,iso,2} \sim 10^{53}$~erg and $E_{\rm k,iso,1} \sim 2\times10^{52}$~erg), as well as the Lorentz factor contrast to $Γ_{2,0}/Γ_{1,0} \approx 0.98$--$2.27$, required to reproduce the observed flare luminosity and rebrightening amplitude. Such an energetic late shell can be launched in a radiatively inefficient second episode of central-engine activity. Thanks to the well-sampled, early-time multiband coverage facilitated by the EP trigger, EP250302a provides a valuable case to test the physical connection between central-engine activity and shell collisions.

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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.

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arXiv2026

Parameter Estimation Horizon of Core-Collapse Supernovae with a Network of Gravitational-Wave Detectors

Core-collapse supernovae are among the most promising yet still undetected sources of gravitational waves. A future detection would provide a direct view of the physical processes occurring deep inside a collapsing star. In this work, we investigate how networks of current and future gravitational-wave detectors can constrain the properties of rapidly rotating core-collapse supernovae using their characteristic core-bounce and early post-bounce signals. Using deep-learning techniques, we estimate the peak frequency, rotation rate, and signal amplitude from noisy detector data and compare the performance of different detector-network configurations. We find that detector networks improve both parameter recovery and sky coverage. For current-generation networks, estimation of the peak frequency is possible out to about 30 kpc, while the rotation rate and signal amplitude remain recoverable out to distances exceeding 100 kpc. Third-generation observatories extend these distances by nearly an order of magnitude.

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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.

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arXiv2026

Centrally concentrated star formation in young clusters II: Jet feedback

Protostellar jets are one of the earliest forms of stellar feedback, but their impact on star formation and cluster assembly in centrally concentrated molecular clouds remains poorly understood. We study how protostellar jets affect the star formation efficiency, the temporal variability of star formation, star cluster structure, and the early dynamical state of centrally concentrated, newly forming star clusters using the Torch star cluster formation framework. We adopt a centrally concentrated initial cloud model with mass M = 2.5 x 10^3 solar masses and compare six pairs of simulations with and without protostellar jets, supplemented by one additional higher resolution pair of simulations. We analyze our simulations using global star formation diagnostics together with structural and dynamical measures of the stellar population. Models with jet feedback achieve star formation efficiencies of 12-16%, while the corresponding models without jets yield higher efficiencies of 19-33%. Jets also cause star formation to occur in discrete bursts rather than continuously, to produce more extended and substructured stellar systems, and to leave behind stellar populations that are less tightly bound and have higher virial parameters. In our centrally concentrated initial conditions, runs with jets form stellar systems that better reproduce the observed range of the projected structural parameter Q_2D in young clusters than runs without jets, indicating that protostellar jets are an important early feedback channel even in centrally concentrated clouds that regulates star formation efficiencies and shapes the emerging cluster structure.

astro-ph.GA↗

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.

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arXiv2026

Systematic Study of Forward and Reverse Shock Afterglow Emission from Two-Component Jets

Two-component jets are frequently invoked to explain complex features in gamma-ray burst (GRB) afterglows, such as late-time rebrightening and chromatic breaks. While many studies fit these models to individual events, a systematic exploration mapping the broader parameter space, particularly the reverse shock contribution, is currently lacking. To address this, we present a comprehensive systematic analysis of two-component jet signatures using numerical modeling with the VegasAfterglow code. Our modeling shows that observable rebrightenings in the forward shock require the wing to carry substantially more energy, while for the reverse shock the energies can be comparable. Because the two components can occupy different spectral regimes, spectral breaks may arise when the wing emission overtakes the core. When the wing's initial velocity is high, relativistic beaming can render its emission invisible to the on-axis observer. As the flow decelerates, the resulting debeaming produces a steeper rise in the observed emission, reaching temporal slopes as steep as about $4.5$ and peaking shortly after the core jet break. In this case, the wing masks the core's break, leaving only a single late-time break. Slower wings that are not initially beamed away do not obscure the core, allowing the observer to see two distinct jet breaks. At late times, the decaying post-jet-break slopes are unaffected and limited to temporal slopes of about $-p$. Additionally, the forward shock dominates the emission across most of the parameter space, while the reverse shock contributes noticeably only under conditions of high magnetization and long engine durations.

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arXiv2025

Assessing the Distance for Probing the Nuclear Equation of State with Supernova Gravitational Waves

Gravitational waves from core-collapse supernovae provide a unique probe of the equation of state (EOS) of high density matter. In this work, we focus on the bounce signal from numerical simulations of rotating supernovae and explore its potential for EOS inference. We employ a support vector machine, previously shown to perform best among tested methods, to classify GW signals simulated for 18 EOS models. For optimally oriented sources, we estimate that the Advanced LIGO A+ detector can probe the EOS for Galactic events, while third-generation observatories such as the Einstein Telescope and Cosmic Explorer can reach substantially farther. For randomly oriented sources, only these next-generation detectors are expected to have sufficient sensitivity. These results represent the potential observational range for probing the nuclear EOS, although, due to the simplifying assumptions adopted, they should be regarded as approximate upper limits.

astro-ph.HE↗

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, Rényi, 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 Rényi entropy from the wavelet domain and the Relief-F selection method yields the most effective discrimination among different gravitational-wave signals.

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arXiv2025

Centrally concentrated star formation in young clusters

The study of star cluster evolution necessitates modeling how their density profiles develop from their natal gas distribution. Observational evidence indicates that many star clusters follow a Plummer-like density profile. However, most studies have focused on the phase after gas ejection, neglecting the influence of gas on early dynamical evolution. We investigate the development of star clusters forming within gas clouds, particularly those with a centrally concentrated gas profile. Simulations were conducted using the \texttt{Torch} framework, integrating the \texttt{FLASH} magnetohydrodynamics code into \texttt{AMUSE}. This permits detailed modeling of star formation, stellar evolution, stellar dynamics, radiative transfer, and gas magnetohydrodynamics. We study the collapse of centrally concentrated, turbulent spheres with a total mass of $2.5\times 10^3\, M_\odot$, investigating the effects of varying numerical resolution and star formation scenarios. The free-fall time is shorter at the center than at the edges of the cloud, with a minimum value of $0.55\,\mathrm{Myr}$. The key conclusions from this study are: (1) the final stellar density profile is more centrally concentrated than analytically predicted, reflecting the role of global gas collapse and feedback; (2) sub-clusters can initially form even in centrally concentrated gas clouds; (3) gas collapses globally toward the center on the central free-fall time scale, contradicting the assumption in analytical models of local fragmentation and star formation; and (4) the mass of the most massive star formed is directly correlated with the cluster effective radius and inversely correlated with the velocity dispersion, while the duration of star formation correlates with the star formation efficiency.

astro-ph.GA↗

arXiv2025

Cloud-cloud collision and star formation in G013.313+0.193

We study the G013.313+0.193 G013.313 region, a complex environment characterized by molecular cloud interactions indicative of cloud-cloud collision (CCC). Observations of the NH3(1,1) and (2,2) inversion transitions were obtained using the Nanshan 26 m radio telescope, while HCO+ (1-0), 12CO, 13CO, and C18O(1-0) transitions from the Purple Mountain Observatory Delingha 14 m telescope. Archival data are also included. We identified key observational signatures of CCC, including complementary spatial distributions, U-shaped structures, bridge features, and V-shaped velocity distributions. The position-velocity diagrams (P-V) reveal clear indications of gas interaction between two velocity components, suggesting an ongoing collision at an estimated angle of approximately 45 degree to the line of sight. The estimated collision timescale is 0.35-1.03 Myr, aligned with the inferred ages of young stellar objects (YSOs) in the region, supporting the hypothesis of collision-induced star formation. Hub-filament system (HFS) are identified in the compressed gas region, where filaments converge toward a dense hub, suggesting the CCC as a potential driver of HFS formation and massive star formation. The high column density suggests favorable conditions for the formation of massive stars. Although alternative kinematic drivers such as longitudinal collapse and shear motion are considered, CCC remains the most plausible explanation for the observed features. Our findings contribute to our understanding of the mechanisms of cloud dynamics and massive star formation in turbulent molecular environments.

astro-ph.GA↗

arXiv2025

Reverse and forward shock afterglow emission from steep jets viewed off axis

We study the morphology of gamma-ray burst (GRB) afterglows viewed off-axis using a simplified analytical model. We consider steep jets, which are expected to be the most common type. These jets, characterized by steep lateral gradients in energy and Lorentz factor, produce highly beamed emission. The observed signal is dominated by their minimum visible angle at any given time. Consequently, the afterglow morphology depends on when this angle begins to decrease, revealing the inner regions of the jet. Depending on whether this decrease occurs before, at, or after the reverse shock crosses the ejecta, three distinct classes of light curves emerge. In the first scenario, the de-beamed emission can produce a rapidly rising signal even prior to the reverse shock crossing. This is expected in GRBs with long duration, low energy, dense circumburst media, or combinations thereof. In some cases, the ejecta shell can be considered as effectively thick in the inner regions and effectively thin in the outer regions. For forward shocks, the temporal slopes in both regimes are identical, which makes it hard to detect the transition. Reverse shocks, however, have distinct temporal slopes, allowing potential detection of the transition in light curves if their emission surpasses that of the forward shock. The characteristic synchrotron frequency of de-beamed emission evolves independently of jet structure for forward shocks but depends on the lateral energy and Lorentz factor gradients for the reverse shock, with slower evolution for steep energy and shallow Lorentz factor gradients.

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arXiv2025

Nucleosynthesis Conditions in Outflows of White Dwarfs Collapsing to Neutron Stars

Accretion-induced collapse (AIC) or merger-induced collapse (MIC) of white dwarfs (WDs) in binary systems is an interesting path to neutron star (NS) and magnetar formation, alternative to stellar core collapse and NS mergers. Such events could add a population of compact remnants in globular clusters, they are expected to produce yet unidentified electromagnetic transients including gamma-ray and radio bursts, and to act as sources of trans-iron elements, neutrinos, and gravitational waves. Here we present the first long-term (>5s post bounce) hydrodynamical simulations in axi-symmetry (2D), using energy- and velocity-dependent three-flavor neutrino transport based on a two-moment scheme. Our set of six models includes initial WD configurations for different masses, central densities, rotation rates, and angular momentum profiles. Our simulations demonstrate that rotation plays a crucial role for the proto-neutron star (PNS) evolution and ejecta properties. We find early neutron-rich ejecta and an increasingly proton-rich neutrino-driven wind at later times in a non-rotating model, in agreement with electron-capture supernova models. In contrast to that and different from previous results, our rotating models eject proton-rich material initially and increasingly more neutron-rich matter as time advances, because an extended accretion torus forms around the PNS and feeds neutrino-driven bipolar outflows for many seconds. AIC and MIC events are thus potential sites of r-process element production, which may imply constraints on their occurrence rates. Finally, our simulations neglect the effects of triaxial deformation and magnetic fields, yet they provide valuable reference cases for comparison with future long-term magneto-hydrodynamic and three-dimensional AIC studies.

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arXiv2025

Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification

We investigate the potential of using gravitational wave (GW) signals from rotating core-collapse supernovae to probe the equation of state (EOS) of nuclear matter. By generating GW signals from simulations with various EOSs, we train machine learning models to classify them and evaluate their performance. Our study builds on previous work by examining how different machine learning models, parameters, and data preprocessing techniques impact classification accuracy. We test convolutional and recurrent neural networks, as well as six classical algorithms: random forest, support vector machines, naïve Bayes, logistic regression, $k$-nearest neighbors, and eXtreme gradient boosting. All models, except naïve Bayes, achieve over 90 per cent accuracy on our dataset. Additionally, we assess the impact of approximating the GW signal using the general relativistic effective potential (GREP) on EOS classification. We find that models trained on GREP data exhibit low classification accuracy. However, normalizing time by the peak signal frequency, which partially compensates for the absence of the time dilation effect in GREP, leads to a notable improvement in accuracy. Despite this, the accuracy does not exceed 70 per cent, suggesting that GREP lacks the precision necessary for EOS classification. Finally, our study has several limitations, including the omission of detector noise and the focus on a single progenitor mass model, which will be addressed in future works.

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arXiv2024

Convective vortices in collapsing stars

Recent studies show that non-radial structures arising from massive star shell convection play an important role in shaping core-collapse supernova explosions. During the collapse phase, convective vortices generate acoustic waves that interact with the supernova shock. This amplifies turbulence in the post-shock region, contributing to explosion. We study how various physical parameters influence the evolution of these convective vortices during stellar collapse using simplified simulations. We model the collapsing star with a transonic Bondi flow and represent convection as solenoidal velocity perturbations. Our results are consistent with previous studies, demonstrating that the peak perturbation amplitude scales linearly with the pre-collapse convective Mach number and inversely with the angular wavenumber of convection. While the shell radius and width primarily determine the timescale of accretion, they have little impact on the peak perturbation amplitudes. Finally, we show that when the convective Mach number is below approximately 0.2, the dynamics remain within the linear regime.

astro-ph.SR↗

arXiv2024

Probing nuclear physics with supernova gravitational waves and machine learning

Core-collapse supernovae are sources of powerful gravitational waves (GWs). We assess the possibility of extracting information about the equation of state (EOS) of high density matter from the GW signal. We use the bounce and early post-bounce signals of rapidly rotating supernovae. A large set of GW signals is generated using general relativistic hydrodynamics simulations for various EOS models. The uncertainty in the electron capture rate is parametrized by generating signals for six different models. To classify EOSs based on the GW data, we train a convolutional neural network (CNN) model. Even with the uncertainty in the electron capture rates, we find that the CNN models can classify the EOSs with an average accuracy of about 87 percent for a set of four distinct EOS models.

astro-ph.HE↗

arXiv2024

Bayesian inference from gravitational waves in fast-rotating, core-collapse supernovae

Core-collapse supernovae (CCSNe) are prime candidates for gravitational-wave detectors. The analysis of their complex waveforms can potentially provide information on the physical processes operating during the collapse of the iron cores of massive stars. In this work we analyze the early-bounce rapidly rotating CCSN signals reported in the waveform catalog of Richers et al 2017, which comprises over 1800 axisymmetric simulations extending up to about 10~ms of post-bounce evolution. It was previously established that for a large range of progenitors, the amplitude of the bounce signal, $Δh$, is proportional to the ratio of rotational-kinetic energy to potential energy, T/|W|, and the peak frequency, $f_{\rm peak}$, is proportional to the square root of the central rest-mass density. In this work, we exploit these relations to suggest that it could be possible to use such waveforms to infer protoneutron star properties from a future gravitational wave observation, if the distance and inclination are well known. Our approach relies on the ability to describe a subset of the waveforms in the early post-bounce phase in a simple form depending only on two parameters, $Δh$ and $f_{\rm peak}$. We use this template to perform a Bayesian inference analysis of waveform injections in Gaussian colored noise for a network of three gravitational wave detectors formed by Advanced LIGO and Advanced Virgo. We show that, for a galactic event, it is possible to recover the peak frequency and amplitude with an accuracy better than 10% for about 80% and 60% of the signals, respectively, given known distance and inclination angle. However, inference on waveforms from outside the Richers catalog is not reliable, indicating a need for carefully verified waveforms of the first 10 ms after bounce of rapidly rotating supernovae of different progenitors with agreement between different codes.

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arXiv2024

Early-time optical spectral shape measurements of GRB 200925B

Optical broad-band spectral shape measurements of gamma-ray bursts (GRBs) are typically made starting an hour or more after the trigger event. With our automated, rapid-response system, the Burst Simultaneous Three-channel Imager (BSTI) on the Nazarbayev University Transient Telescope at Assy-Turgen Astrophysical Observatory (NUTTelA-TAO), we began measurements of GRB200925B 129 s after the Swift BAT trigger. The temporal decay log slopes in the g', r', and i' bands in the time interval 129 s to 1029 s are -0.43 \pm 0.31, -0.43 \pm 0.15, and -0.72 \pm 0.14, respectively. During the decay phase, a shift in color from red to blue, a change in log slope of \{beta} from -2.73 to -1.52 was measured. The evolution in the optical spectral slope is consistent with a decrease in extinction caused by dust destruction.

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