arXiv subjects

Junda Zhou

Publications and source records attributed to Junda Zhou.

6 recordsLinked to original sources

arXiv2026

Inversion of Hydrogen-rich Atmosphere and Water Content for GJ 486b

GJ~486b is a close-in planet orbiting an M dwarf and is therefore expected to have undergone strong atmospheric escape. Motivated by theoretical and observational studies on the constraints of its water and atmosphere, we investigate which combinations of an primordial hydrogen-rich atmosphere and water inventory could fit the current water content implied by bulk density measurements. We model the atmosphere escape using VPLanet, following the loss of an initial hydrogen-rich atmosphere and the subsequent escape of a water-dominated atmosphere. By scanning a broad parameter space across different stellar ages, we invert for the initial hydrogen-rich atmospheric mass and water inventory consistent with the current constraints. Our results reveal a strong degeneracy between the water reservoir and the initial hydrogen-rich atmosphere. Even a modest hydrogen-rich atmosphere can significantly delay early escape of the water and reduce the water inventory required to reproduce the current water content. We also find that the inferred initial conditions are also strongly age dependent. Incorporating a planet formation dataset as a prior, we derive a probabilistic constraint on the host star age, yielding an expected age of $2.90^{+2.47}_{-2.27}$~Gyr, which is consistent with the results obtained from other methods to determine M dwarf ages.

astro-ph.EP↗

arXiv2026

Complex-Valued-Matrix Permanents: SPA-based Approximations and Double-Cover Analysis

Approximating the permanent of a complex-valued matrix is a fundamental problem with applications in Boson sampling and probabilistic inference. In this paper, we extend factor-graph-based methods for approximating the permanent of non-negative-real-valued matrices that are based on running the sum-product algorithm (SPA) on standard normal factor graphs, to factor-graph-based methods for approximating the permanent of complex-valued matrices that are based on running the SPA on double-edge normal factor graphs. On the algorithmic side, we investigate the behavior of the SPA, in particular how the SPA fixed points change when transitioning from real-valued to complex-valued matrix ensembles. On the analytical side, we use graph covers to analyze the Bethe approximation of the permanent, i.e., the approximation of the permanent that is obtained with the help of the SPA. This combined algorithmic and analytical perspective provides new insight into the structure of Bethe approximations in complex-valued problems and clarifies when such approximations remain meaningful beyond the non-negative-real-valued settings.

cs.IT↗

arXiv2025

A Core-Collapse Supernova Neutrino Parameterization with Enhanced Physical Interpretability

We introduce a novel parameterization of supernova neutrino energy spectra with a clear physical motivation. Its central parameter, $\tau(t)$, quantifies the characteristic thermal-diffusion area during the explosion. When applied to the historic SN1987A data, this parameterization yields statistically significant fits and provides robust constraints on the unobserved low-energy portion of the spectrum. Beyond this specific application, we demonstrate the model's power on a suite of 3D core-collapse supernova simulations, finding that the temporal evolution of $\tau(t)$ distinctly separates successful from failed explosions. Furthermore, we constrain the progenitor mass of SN 1987A to approximately 19 solar masses by applying Smoothed Isotonic Regression, while noting the sensitivity of this estimate to observational uncertainties. Moreover, in these simulations, $\tau(t)$ and the gravitational-wave strain amplitude display a strong, synergistic co-evolution, directly linking the engine's energetic evolution to its geometric asymmetry. This implies that the thermodynamic state of the explosion is imprinted not only on the escaping neutrino flux, but also recorded in the shape of the energy spectrum. Our framework therefore offers a valuable tool for decoding the detailed core dynamics and multi-messenger processes of future galactic supernovae.

astro-ph.HE↗

arXiv2025

Application of interpretable data-driven methods for the reconstruction of supernova neutrino energy spectra following fast neutrino flavor conversions

Neutrinos can experience fast flavor conversions (FFCs) in highly dense astrophysical environments, such as core-collapse supernovae and neutron star mergers, potentially affecting energy transport and other processes. Simulating fast flavor conversions under realistic astrophysical conditions requires substantial computational resources and poses significant analytical challenges. While machine learning methods such as multilayer perceptrons have been used to accurately predict the asymptotic outcomes of FFCs, their "black-box" nature limits the extraction of direct physical insight. To mitigate this limitation, we employ two distinct interpretable machine learning frameworks, Kolmogorov-Arnold Networks (KANs) and Sparse Identification of Nonlinear Dynamics (SINDy), to learn interpretable surrogates for the asymptotic input-output mapping from an FFC simulation dataset. Our analysis reveals a fundamental trade-off between predictive accuracy and model simplicity. KANs demonstrate high fidelity in reconstructing post-conversion neutrino energy spectra, achieving accuracies of up to 90%. In contrast, SINDy yields a low-rank, compact closed-form approximation of the input-output mapping, at the expense of some predictive accuracy. Critically, using these structured and sparse surrogates as diagnostic tools, we identify that the system's evolution is most sensitive to the initial number density of heavy-lepton neutrinos when FFCs are triggered, compared with other physical quantities. Ultimately, this work provides a methodological framework for interpretable machine learning that supports genuine data-driven scientific discovery in astronomy and astrophysics, going beyond prediction alone.

astro-ph.HE↗

arXiv2025

Dark Matter (S)pins the Planet

Dark matter heating in planets has been proposed as a potential probe for dark matter detection. Assuming near-equilibrium conditions, we find that the energy input from dark matter raises planetary temperatures and accelerates rotation. The distribution of energy between heating and rotational acceleration depends on both planetary properties and external inputs, suggesting that previous studies may have overestimated the heating contribution. At high dark matter densities, planetary rotation stabilizes earlier and becomes primarily governed by dark matter effects.

astro-ph.HE↗

arXiv2024

Variable-Length Secret Key Agreement via Random Stopping Time

We consider a key agreement setting where two parties observe correlated random sources, and want to agree on a secret key via public discussions. In order to allow the key length to adapt to the realizations of the random sources, we allow the key to be of variable length, subject to a novel variable-length version of the uniformity constraint based on random stopping time. We propose simple, computationally efficient key agreement schemes under the new constraint. The proposed scheme can be considered as the key agreement analogue of variable-length source coding via Huffman coding, and the Knuth-Yao random number generator.

cs.IT↗