arXiv subjects

Oleg Korobkin

Publications and source records attributed to Oleg Korobkin.

At least 19 recordsLinked to original sources

arXiv2026

Evaluating Short-Warning Mitigation via Intentional Robust Disruption of a Hypothetical Impact of Asteroid 2023 NT1

We investigate various short-warning mitigation scenarios via fragmentation for a hypothetical impact of asteroid 2023 NT1, a Near-Earth Object (NEO) that was discovered on July 15, 2023, two days after its closest approach to Earth on July 13. The asteroid passed by Earth within ~0.25 lunar distances, with a closest approach of ~1$\times10^5$ km and velocity of 11.27 km/s. Its size remains largely uncertain, with an estimated diameter range of 26-58 m and a most probable estimate of 34 m [JPL Sentry, September 15, 2023] (weighted by the NEO size frequency distribution). If 2023 NT1 had collided with Earth, it could have caused significant local damage. Assuming a spherical asteroid with a diameter of 34 m, uniform density of 2.6 g/cm$^3$, and impact velocity of 15.59 km/s, a collision would have yielded an estimated impact energy of ~1.5 Mt, approximately three times the energy of the Chelyabinsk airburst in 2013. We analyze the effectiveness of mitigation via intentional robust disruption (IRD) for objects similar to 2023 NT1. We utilize Pulverize It (PI), a NASA Innovative Advanced Concepts (NIAC) study of planetary defense via fragmentation, to model potential mitigation scenarios through simulations of hypervelocity asteroid disruption and atmospheric ground effects in the case of a terminal defense mode. Simulations suggest that PI is an effective multi-modal approach for planetary defense that can operate in extremely short interdiction modes, in addition to long interdiction time scales with extended warning. Our simulations support the proposition that threats like 2023 NT1 can be effectively mitigated with intercepts of one day (or less) prior to impact, yielding minimal to no ground damage.

astro-ph.EP↗

arXiv2026

Effects of magnetically driven shocks on nucleosynthesis and kilonovae from neutron star mergers

Neutron-star mergers can launch mildly relativistic to moderately relativistic outflows whose interaction with the ejecta can reshape kilonova emission. We parametrically study magnetically powered outbursts from long-lived merger remnants, such as flare-like eruptions and collapse-driven shocks, and quantify their impact on ejecta dynamics, composition, and observables. Using two-dimensional special-relativistic magnetohydrodynamic simulations, we follow magnetized blast waves injected into expanding merger ejecta for early- and late-launch scenarios across a range of shock strengths. We then post-process Lagrangian tracer histories with the nuclear reaction network WinNet and the radiative-transfer code SuperNu with realistic opacities, to connect shock heating directly to nucleosynthesis and kilonova light curves. We find that sufficiently strong shocks can reheat portions of the ejecta to nuclear statistical equilibrium, increase the electron fraction in the shocked material, and deposit entropy, leading to systematic changes in $r$-process yields. These thermodynamic and compositional changes can leave observable imprints on kilonova emission -- especially in color evolution and late-time light-curve behavior -- indicating that magnetically driven remnant variability can potentially contribute to kilonova diversity.

astro-ph.HE↗

arXiv2026

Kilonovae and Long-duration Gamma-ray Bursts

Recent detections of kilonova-like emission following long-duration gamma-ray bursts GRB211211A and GRB230307A have been interpreted as originating from the merger of two neutron stars. In this work, we demonstrate that these observations are also consistent with nucleosynthesis originating from a collapsar scenario. Our model accurately predicts the observed optical and infrared light curves using a single weak $r$-process component. The absence of lanthanide-rich material in our model, consistent with the data, challenges the prevailing interpretation that a red evolution in such transients necessarily indicates the presence of heavy $r$-process elements.

astro-ph.HE↗

arXiv2026

A comparison of three neodymium atomic data sets for kilonova modeling

We examine the impact of input neodymium (Nd) atomic data on the light curves and spectra of kilonovae, probing the sensitivity of kilonova observables to the atomic physics of this important lanthanide element. We use the SuperNu Monte Carlo radiative transfer code, simulating a simple semi-analytic 1D kilonova with a pure Nd atmosphere, fixing the radiative transfer method while using input atomic data generated by three different codes: the LANL suite of atomic physics codes, HULLAC, and Autostructure. We see that the choice of atomic data significantly shapes the resulting light curves and spectra. Peak bolometric luminosities differ by a ratio of nearly 1.5 between HULLAC/Autostructure and LANL data sets. Moreover, we observe significant near- to mid-IR differences in the structure of the spectra. We specifically attribute these differences to the choice of atomic data for neutral Nd I. Many of the results here have been adapted from a presentation at "Radiative Transfer and Atomic Physics of Kilonovae" in Stockholm, 2023. We additionally present a LANL data set with energies calibrated to available values in the NIST Atomic Spectra Database, and demonstrate that this calibration also significantly affects IR spectral structure at late time. The substantial differences in kilonova observables that arise from tuning the atomic data of just one lanthanide element highlight the special attention that must be paid to atomic physics uncertainties when modeling kilonovae, from AT2017gfo to beyond.

astro-ph.HE↗

arXiv2026

Joint Electromagnetic and Gravitational Wave Inference of Binary Neutron Star Merger GW170817 Using Forward-Modeling Ejecta Predictions

We reassess the capacity for multimessenger inference of AT2017gfo/GW170817 using both kilonova and gravitational wave emission within the context of a recent simulation-based surrogate model for kilonova emission. Independent of the inclusion of gravitational wave observations, comparisons between observations that incorporate our kilonova model favor a narrow range of ejecta properties, even when allowing for a wide range of systematic uncertainties in our modeling approach. Conversely, we find that astrophysical conclusions about the neutron star itself, including its mass and radius, depend strongly on assumptions about how much material is ejected from the neutron star. Looking forward, our analysis highlights the importance of systematic uncertainty in general, the need for better modeling of neutron star merger mass ejection from first principles, and warns against uncontextualized applications of ejecta predictions using fits to numerical relativity simulations.

astro-ph.HE↗

arXiv2026

Material Identification using Multi-Modal Intrinsic Radiation and Radiography

We investigate multi-modal material identification for special nuclear material (SNM) configurations using a combination of X-ray radiography, high-resolution γ-ray spectroscopy, and neutron multiplicity measurements. We consider a Beryllium Reflected Plutonium sphere (BeRP) ball surrounded by one or two concentric shielding shells of unknown composition whose radii are assumed known from radiography. High-purity germanium (HPGe) spectra are reduced to net counts in selected Pu-239 photo-peaks, while neutron multiplicity information is summarized by Feynman variances Y2 and Y3 computed from factorial moments of the neutron counting statistics. Using synthetic data generated with the Gamma Detector Response and Analysis Software (GADRAS) for a range of shielding materials and thicknesses, we cast the material identification problem as a supervised multi-class classification task over all admissible shell-material combinations. We demonstrate that a random forest classifier trained on combined gamma and neutron features achieves almost perfect identification accuracy for single-shell cases, and substantial performance gains for more challenging double-shell configurations relative to gamma-only classification. Alternative statistical and machine-learning formulations for this multi-class problem are examined along with examination of the impact of model-mismatch between the forward model and the test cases as given by variations in the statistical noise. Opportunities for extending the approach to more complex geometries and experimental data are also discussed.

physics.ins-det↗

arXiv2025

Space-Time Spectral Collocation Tensor-Network Approach for Maxwell's Equations

In this work, we develop a space--time Chebyshev spectral collocation method for three-dimensional Maxwell's equations and combine it with tensor-network techniques in Tensor-Train (TT) format. Under constant material parameters, the Maxwell system is reduced to a vector wave equation for the electric field, which we discretize globally in space and time using a staggered spectral collocation scheme. The staggered polynomial spaces are designed so that the discrete curl and divergence operators preserve the divergence-free constraint on the magnetic field. The magnetic field is then recovered in a space--time post-processing step via a discrete version of Faraday's law. The global space--time formulation yields a large but highly structured linear system, which we approximate in low-rank TT-format directly from the operator and data, without assuming that the forcing is separable in space and time. We derive condition-number bounds for the resulting operator and prove spectral convergence estimates for both the electric and magnetic fields. Numerical experiments for three-dimensional electromagnetic test problems confirm the theoretical convergence rates and show that the TT-based solver maintains accuracy with approximately linear complexity in the number of grid points in space and time.

math.NA↗

arXiv2025

Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the estimated parameters can be used in a hydrodynamics code to obtain density fields and hydrodynamic shock and outer edge features that are consistent with the data. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model.

physics.comp-ph↗

arXiv2025

Spatial models of r-process remnants and their gamma-ray detectability

We investigate the detectability of gamma-ray emission from long-lived radioactive isotopes in r-process-enriched remnants, focusing on how assumptions about their spatial distribution introduce uncertainty into detection prospects. Using a suite of physically motivated models for the Galactic distribution of kilonova and supernova remnants, we simulate synthetic remnant populations and compute their time-evolving gamma-ray spectra. We then compare these flux predictions to the sensitivity limits of next-generation instruments such as COSI and HEX-P. We find that even under optimistic assumptions, detection probabilities with COSI are extremely low ($\ll 1\%$), and that marginal improvements are only possible with instruments like HEX-P if prior localization is available. The choice of spatial distribution model can lead to more than an order-of-magnitude variation in expected line fluxes at low instrument sensitivities, underscoring the role of spatial modeling as a dominant source of uncertainty. Nevertheless, instrumental capability remains the fundamental bottleneck, and a hybrid mission combining COSI-like sky coverage with HEX-P-level line sensitivity would be required to make detection more probable than not.

astro-ph.HE↗

arXiv2025

Inferring neutron star merger ejecta morphologies with kilonovae

In this study we incorporate a new grid of kilonova simulations produced by the Monte Carlo radiative transfer code SuperNu in an inference pipeline for astrophysical transients, and evaluate their performance. These simulations contain four different two-component ejecta morphology classes. We analyze follow-up observational strategies by Vera Rubin Observatory in optical, and James Webb Space Telescope (JWST) in mid-infrared (MIR). Our analysis suggests that, within these strategies, it is possible to discriminate between different morphologies only when late-time JWST observations in MIR are available. We conclude that follow-ups by the new Vera Rubin Observatory alone are not sufficient to determine ejecta morphology. Additionally, we make comparisons between surrogate models based on radiative transfer simulation grids by SuperNu and POSSIS, by analyzing the historic kilonova AT2017gfo that accompanied the gravitational wave event GW170817. We show that both SuperNu and POSSIS models provide similar fits to photometric observations. Our results show a slight preference for SuperNu models, since the wind ejecta parameters recovered with these models are in better agreement with expectations from numerical simulations.

astro-ph.HE↗

arXiv2024

Kilonova Light-Curve Interpolation with Neural Networks

Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multi-band inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle.

astro-ph.HE↗

arXiv2024

Improving Radiography Machine Learning Workflows via Metadata Management for Training Data Selection

Most machine learning models require many iterations of hyper-parameter tuning, feature engineering, and debugging to produce effective results. As machine learning models become more complicated, this pipeline becomes more difficult to manage effectively. In the physical sciences, there is an ever-increasing pool of metadata that is generated by the scientific research cycle. Tracking this metadata can reduce redundant work, improve reproducibility, and aid in the feature and training dataset engineering process. In this case study, we present a tool for machine learning metadata management in dynamic radiography. We evaluate the efficacy of this tool against the initial research workflow and discuss extensions to general machine learning pipelines in the physical sciences.

cs.LG↗

arXiv2024

Tensor-Train WENO Scheme for Compressible Flows

In this study, we introduce a tensor-train (TT) finite difference WENO method for solving compressible Euler equations. In a step-by-step manner, the tensorization of the governing equations is demonstrated. We also introduce \emph{LF-cross} and \emph{WENO-cross} methods to compute numerical fluxes and the WENO reconstruction using the cross interpolation technique. A tensor-train approach is developed for boundary condition types commonly encountered in Computational Fluid Dynamics (CFD). The performance of the proposed WENO-TT solver is investigated in a rich set of numerical experiments. We demonstrate that the WENO-TT method achieves the theoretical $\text{5}^{\text{th}}$-order accuracy of the classical WENO scheme in smooth problems while successfully capturing complicated shock structures. In an effort to avoid the growth of TT ranks, we propose a dynamic method to estimate the TT approximation error that governs the ranks and overall truncation error of the WENO-TT scheme. Finally, we show that the traditional WENO scheme can be accelerated up to 1000 times in the TT format, and the memory requirements can be significantly decreased for low-rank problems, demonstrating the potential of tensor-train approach for future CFD application. This paper is the first study that develops a finite difference WENO scheme using the tensor-train approach for compressible flows. It is also the first comprehensive work that provides a detailed perspective into the relationship between rank, truncation error, and the TT approximation error for compressible WENO solvers.

math.NA↗

arXiv2024

On a spectral method for $β$-particle bound excitation collisions in kilonovae

The interaction of $β$-particles with the weakly ionized plasma background is an important mechanism for powering the kilonova transient signal from neutron star mergers. For this purpose, we present an implementation of the approximate fast-particle collision kernel, described by Inokuti (1971) following the seminal formulation of Bethe (1930), in a spectral solver of the Vlasov-Maxwell-Boltzmann equations. In particular, we expand the fast-particle plane-wave atomic excitation kernel into coefficients of the Hermite basis, and derive the relevant discrete spectral system. In this fast-particle limit, the approach permits the direct use of atomic data, including optical oscillator strengths, normally applied to photon-matter interaction. The resulting spectral matrix is implemented in the MASS-APP spectral solver framework, in a way that avoids full matrix storage per spatial zone. We numerically verify aspects of the matrix construction, and present a proof-of-principle 3D simulation of a 2D axisymmetric kilonova ejecta snapshot. Our preliminary numerical results indicate that a reasonable choice of Hermite basis parameters for $β$-particles in the kilonova are a bulk velocity parameter $\vec{u}=0$, a thermal velocity parameter $\vecα=0.5c$, and a 9x9x9 mode velocity basis set (Hermite orders 0 to 8 in each dimension). For ejecta-interior sample zones, we estimate the ratio of thermalization from large-angle ($\gtrsim2.5^{\circ}$) bound excitation scattering to total thermalization is $\sim$0.002-0.003.

astro-ph.HE↗

arXiv2023

Isotopic gamma lines for identification of shielding materials

Identifying the constituting materials of concealed objects is crucial in a wide range of sectors, such as medical imaging, geophysics, nonproliferation, national security investigations, and so on. Existing methods face limitations, particularly when multiple materials are involved or when there are challenges posed by scattered radiation and large areal mass. Here we introduce a novel brute-force statistical approach for material identification using high spectral resolution detectors, such as HPGe. The method relies upon updated semianalytic formulae for computing uncollided flux from source of gamma radiation, shielded by a sequence of nested spherical or cylindrical materials. These semianalytical formulae make possible rapid flux estimation for material characterization via combinatorial search through all possible combinations of materials, using a high-resolution HPGe counting detector. An important prerequisite for the method is that the geometry of the objects is known (for example, from X-ray radiography). We demonstrate the viability of this material characterization technique in several use cases with both simulated and experimental data.

physics.app-ph↗

arXiv2023

Modeling Solids in Nuclear Astrophysics with Smoothed Particle Hydrodynamics

Smoothed Particle Hydrodynamics (SPH) is a frequently applied tool in computational astrophysics to solve the fluid dynamics equations governing the systems under study. For some problems, for example when involving asteroids and asteroid impacts, the additional inclusion of material strength is necessary in order to accurately describe the dynamics. In compact stars, that is white dwarfs and neutron stars, solid components are also present. Neutron stars have a solid crust which is the strongest material known in nature. However, their dynamical evolution, when modeled via SPH or other computational fluid dynamics codes, is usually described as a purely fluid dynamics problem. Here, we present the first 3D simulations of neutron-star crustal toroidal oscillations including material strength with the Los Alamos National Laboratory SPH code FleCSPH. In the first half of the paper, we present the numerical implementation of solid material modeling together with standard tests. The second half is on the simulation of crustal oscillations in the fundamental toroidal mode. Here, we dedicate a large fraction of the paper to approaches which can suppress numerical noise in the solid. If not minimized, the latter can dominate the crustal motion in the simulations.

astro-ph.IM↗

arXiv2023

The Effect of the Velocity Distribution on Kilonova Emission

The electromagnetic emission from the non-relativistic ejecta launched in neutron star mergers (either dynamically or through a disk wind) has the potential to probe both the total mass and composition of this ejecta. These observations are crucial in understanding the role of these mergers in the production of r-process elements in the universe. However, many properties of the ejecta can alter the light-curves and we must both identify which properties play a role in shaping this emission and understand the effects these properties have on the emission before we can use observations to place strong constraints on the amount of r-process elements produced in the merger. This paper focuses on understanding the effect of the velocity distribution (amount of mass moving at different velocities) for lanthanide-rich ejecta on the light-curves and spectra. The simulations use distributions guided by recent calculations of disk outflows and compare the velocity-distribution effects to those of ejecta mass, velocity and composition. Our comparisons show that uncertainties in the velocity distribution can lead to factor of 2-4 uncertainties in the inferred ejecta mass based on peak infra-red luminosities. We also show that early-time UV or optical observations may be able to constrain the velocity distribution, reducing the uncertainty in the ejecta mass.

astro-ph.HE↗

arXiv2023

The LIGO HET Response (LIGHETR) Project to Discover and Spectroscopically Follow Optical Transients Associated with Neutron Star Mergers

The LIGO HET Response (LIGHETR) project is an enterprise to follow up optical transients (OT) discovered as gravitational wave merger sources by the LIGO/Virgo collaboration (LVC). Early spectroscopy has the potential to constrain crucial parameters such as the aspect angle. The LIGHETR collaboration also includes the capacity to model the spectroscopic evolution of mergers to facilitate a real-time direct comparison of models with our data. The principal facility is the Hobby-Eberly Telescope. LIGHETR uses the massively-replicated VIRUS array of spectrographs to search for associated OTs and obtain early blue spectra and in a complementary role, the low-resolution LRS-2 spectrograph is used to obtain spectra of viable candidates as well as a densely-sampled series of spectra of true counterparts. Once an OT is identified, the anticipated cadence of spectra would match or considerably exceed anything achieved for GW170817 = AT2017gfo for which there were no spectra in the first 12 hours and thereafter only roughly once daily. We describe special HET-specific software written to facilitate the program and attempts to determine the flux limits to undetected sources. We also describe our campaign to follow up OT candidates during the third observational campaign of the LIGO and Virgo Scientific Collaborations. We obtained VIRUS spectroscopy of candidate galaxy hosts for 5 LVC gravitational wave events and LRS-2 spectra of one candidate for the OT associated with S190901ap. We identified that candidate, ZTF19abvionh = AT2019pip, as a possible Wolf-Rayet star in an otherwise unrecognized nearby dwarf galaxy.

astro-ph.HE↗