arXiv subjects

Stefan Stefanov

Publications and source records attributed to Stefan Stefanov.

7 recordsLinked to original sources

arXiv2026

Dynamical analysis of the 5:1 mean-motion resonance of the HD 202206 system

The HD 202206 system, which features two substellar companions with a 5:1 period ratio around a solar-type star, offers a rare opportunity to study high-order mean-motion resonances and provides new insights into the formation and evolution of substellar companions in extrasolar planetary systems. We revisited the HD 202206 system, aiming to conduct a more comprehensive analysis of orbital inclinations, companion masses, resonance dynamics, and potential formation mechanisms. Our analysis of the astrometric jitter around the best fit in the Gaia DR3 catalog and the proper motion anomalies between Hipparcos and Gaia places robust constraints on the orbital inclinations. We performed a new dynamical fit to all available radial velocity data from the CORALIE and HARPS spectrographs. We assessed the dynamical configuration and long-term stability of the system using samples generated by nested sampling. Our analysis shows that the orbital inclinations are strongly constrained to about $51^\circ$. Consequently, the best-fit dynamical solution, assuming a coplanar and inclined ($i = 51^\circ$) configuration, yields an inner brown dwarf of $21.56 M_\mathrm{J}$ and an outer giant planet of $3.12 M_\mathrm{J}$. The corresponding orbital periods are 256.26 days and 1298.87 days, with eccentricities of 0.426 and 0.180. An investigation of the five relevant resonance angles shows that only one is librating with a large amplitude. Our stability analysis confirms that the system is dynamically stable. Our results provide new constraints on the masses and inclination of the system, challenging earlier claims of a face-on configuration and shedding light on the formation and evolution of substellar companions in extrasolar planetary systems.

astro-ph.EP↗

arXiv2026

Resolving Cryogenic and Hypersonic Rarefied Flows via Deep Learning-Accelerated Lennard-Jones DSMC

Integrating the physically realistic Lennard--Jones (LJ) potential into Direct Simulation Monte Carlo (DSMC) remains challenging because the long-range potential complicates collision-rate definition and makes repeated scattering-angle evaluation expensive. This study develops an LJ--DSMC framework built around two methodological advances and a transport-level validation of the resulting collision kernel. First, a generalized collision-selection treatment is formulated for Bird's DSMC algorithms (DSMC1, DSMC1S, and DS2V) through a Variable Effective Diameter (VED) model obtained from local Chapman--Enskog viscosity matching. This viscosity-consistent pair-selection model provides a finite DSMC collision-rate closure for the LJ potential and is validated in helium and argon normal shocks, cryogenic supersonic Couette flow, and hypersonic cylinder flows. The results show agreement with VHS in high-temperature repulsive regimes, but reveal clear LJ effects, including reduced shear stress and larger cryogenic wakes, when attractive forces become important. Second, the computational bottleneck of the accepted LJ binary-scattering step is removed by training a Deep Operator Network (DeepONet) to predict the LJ deflection angle from high-fidelity scattering data, replacing the numerical Matsumoto--Koura integral while preserving the standard elastic post-collision update. The surrogate gives a bulk mean wrapped-angle error of $1.6\times10^{-3}\,\mathrm{rad}$ and a 99th-percentile error of $9.9\times10^{-3}\,\mathrm{rad}$, accelerates the collision subroutine by 40\%, and reduces total wall time by 36\%. Finally, the same DeepONet--LJ scattering kernel is tested beyond viscosity-controlled flows through diffusion benchmarks.

physics.flu-dyn↗

arXiv2026

A Warm Massive Pair of Planets around TOI-1232 Revealed with Transit-timing Variations and Doppler Spectroscopy

TOI-1232 is a G-dwarf star with a mass of $1.06_{-0.06}^{+0.07} M_\odot$, a radius of $1.07\pm 0.05 R_\odot$, and slightly higher metallicity than solar of Fe/H = $0.18 \pm 0.05$. The star hosts a transiting warm Jovian-mass planet, TOI-1232 b, with an orbital period of $P_{b} = 14.256_{-0.001}^{+0.001}$ days, identified with data from multiple sectors of the $\textit{TESS}$ space telescope. The $\textit{TESS}$ light curve of TOI-1232 is complex, as it is contaminated by a background eclipsing binary with a period of $1.37$ days. The TOI-1232 b was firmly confirmed by ground-based transit follow-up campaigns from Las Cumbres, Hazelwood, Brierfield, and ASTEP observatories.Additionally, the $\textit{TESS}$ transits of TOI-1232 b exhibit strong transit timing variations (TTVs) with a super-period of $235.5 \pm 0.7$ days and a semi-amplitude of 27 minutes. Radial velocity (RV) follow-up with the FEROS spectrograph confirms the planetary nature of the transiting candidate, while a self-consistent $N$-body analysis of RVs and TTVs pinpoints the presence of a second outer Saturn-mass companion, TOI-1232 c with a period of $P_{c} = 30.356_{-0.012}^{+0.010}$ days. The TOI-1232 warm-giant system is particularly important due to the evidence of two massive planets that reside near the 2:1 commensurability but are not locked in a mean motion resonance (MMR). Thanks to $\textit{TESS}$, we have revealed a handful of these rare systems. Hence, TOI-1232 is an important addition to understanding the formation and dynamical evolution of such compact, massive, warm giant planets.

astro-ph.EP↗

arXiv2026

Physics Constrained Neural Collision Operators for Variable Hard Sphere Surrogates and Ab Initio Angle Prediction in Direct Simulation Monte Carlo

The Direct Simulation Monte Carlo (DSMC) method is the gold standard for non-equilibrium rarefied gas dynamics, yet its computational cost can be prohibitive, especially for near-continuum regimes and high-fidelity \emph{ab initio} potentials. This work develops a unified, physics-constrained neural-operator framework that accelerates DSMC while preserving physical invariants and stochasticity required for long-time kinetic simulations. First, we introduce a local neural collision kernel replacing the phenomenological Variable Hard Sphere (VHS) model. To overcome the variance suppression and artificial cooling inherent to purely deterministic regression surrogates, we augment inference with a physics-constrained stochastic layer. Controlled latent-noise injection restores thermal fluctuations, while cell-wise moment-matching strictly enforces momentum and kinetic-energy conservation. Remarkably, this operator exhibits zero-shot spatial and thermodynamic generalization: a model trained exclusively on 1D Couette flow accurately simulates a complex 2D lid-driven cavity, capturing high-order non-equilibrium moments without retraining.Second, to bypass the extreme cost of quantum-mechanical scattering, we develop a dedicated \emph{ab initio} neural operator for the Jäger interaction potential. Trained via a \emph{physics harvesting} strategy on large-scale collision pairs, it efficiently captures the high-energy scattering dynamics dominating hypersonic regimes. Validated on a Mach~10 rarefied argon flow over a cylinder, the framework reproduces transport behaviors and shock features with high fidelity, achieving an approximate 20\% cost reduction relative to direct numerical integration.

physics.comp-ph↗

arXiv2025

Efficient Collision Algorithms in DSMC for Rarefied Gas Dynamics: Markovian NTC-Pre-Scan and Bernoulli-Trial Schemes

The collision process is essential to the Direct Simulation Monte Carlo (DSMC) method, as it incorporates the fundamental principles of the Boltzmann and Kac stochastic equations. A series of collision algorithms, known as the Bernoulli-trials (BT) family schemes, have been proposed based on the Kac stochastic equation. The primary impetus of this paper is to rectify a long-standing theoretical flaw in the widely used no-time-counter (NTC) collision algorithm. We demonstrate that the standard NTC scheme is fundamentally non-Markovian, relying on a fixed majorant product that introduces a system 'memory' and leads to inaccuracies at low particle counts. We propose a new algorithm, NTC-Pre-Scan, which transforms the scheme into a fully Markovian process. When the repeated collisions are not crucial, our new NTC scheme, called NTC-Pre-Scan, could work accurately with a very low number of particles per cell (PPC), average PPC<1, i.e., PPC=0.01, which means with several empty cells in simulations. This contrasts with the standard NTC schemes, which typically require a PPC greater than 1. Then, a systematic evaluation of different BT-based collision partner selection schemes, including the simplified Bernoulli trials (SBT), generalized Bernoulli trials (GBT), symmetrized and simplified Bernoulli trials (SSBT), and the newly proposed symmetrized and generalized Bernoulli trials (SGBT), is conducted to treat some benchmark rarefied gas dynamics problems. The results show that the BT-based collision algorithms and NTC-Pre-scan successfully maintain the collision frequency as the number of particles per cell decreases.

physics.comp-ph↗

arXiv2024

Towards a Characterisation of Monte-Carlo Tree Search Performance in Different Games

Many enhancements to Monte-Carlo Tree Search (MCTS) have been proposed over almost two decades of general game playing and other artificial intelligence research. However, our ability to characterise and understand which variants work well or poorly in which games is still lacking. This paper describes work on an initial dataset that we have built to make progress towards such an understanding: 268,386 plays among 61 different agents across 1494 distinct games. We describe a preliminary analysis and work on training predictive models on this dataset, as well as lessons learned and future plans for a new and improved version of the dataset.

cs.AI↗

arXiv2014

Hanle-effect measurements of spin injection from Mn5Ge3C0.8/Al2O3-contacts into degenerately doped Ge channels on Si

We report electrical spin injection and detection in degenerately doped n-type Ge channels using Mn5Ge3C0.8/Al2O3/n^{+}-Ge tunneling contacts for spin injection and detection. The whole structure is integrated on a Si wafer for complementary metal-oxide-semiconductor compatibility. From three-terminal Hanle-effect measurements, we observe a spin accumulation up to 10 K. The spin lifetime is extracted to be 38 ps at T = 4K using Lorentzian fitting, and the spin diffusion length is estimated to be 367 nm due to the high diffusion coefficient of the highly doped Ge channel.

cond-mat.mes-hall↗