arXiv subjects

Chun Huang

Publications and source records attributed to Chun Huang.

At least 19 recordsLinked to original sources

arXiv2026

Combining the Mass--Radius Posteriors of J0030+0451 Allowing for Unknown Model Systematics

The NASA Neutron star Interior Composition Explorer (\emph{NICER}) mission measures the X-ray pulse profiles of select millisecond pulsars and uses sophisticated pulse profile modeling (PPM) techniques to constrain their masses ($M$) and radii ($R$), in order to probe the state of matter in their interiors. One of the most studied pulsars, PSR J0030+0451, has been analyzed by multiple groups using different choices of hotspot models. The different choices of hotspot prescriptions to fit the same observational data led to different $M$--$R$ posteriors that do not completely agree with one another, resulting in a practical bottleneck for dense-matter equation-of-state (EoS) inference. In this paper, we adapt a robust Bayesian combination framework to the published $M$--$R$ posteriors of PSR J0030+0451 while allowing for unknown systematic uncertainties that might have led to the apparently divergent results. Using this technique, we combine eight existing $M$--$R$ posteriors into a single conservative and reproducible posterior that incorporates unknown model systematics across the currently available analyses and is suitable for direct use in EoS studies. The resulting constraint is $M = 1.46^{+0.09}_{-0.08}\,M_\odot$, $R = 12.69^{+0.64}_{-0.55}\,\mathrm{km}$, and compactness $C = 0.172^{+0.006}_{-0.007}$ (68\% credible interval). Incorporating this combined J0030+0451 constraint in an EoS-agnostic joint analysis with PSR~J0437--4715 and GW170817 yields $R_{1.4} = 11.98^{+0.58}_{-0.68}\,\mathrm{km}$ and $\Lambda_{1.4} = 320^{+216}_{-138}$. Our results provide a combined $M$--$R$ constraint for J0030+0451 and a practical framework for incorporating cross-model uncertainty into neutron star EoS inference pipelines.

astro-ph.HE↗

arXiv2026

AI Cosplaying as Astrophysicists: A Controlled Synthetic-Agent Study of AI-Assisted Astrophysical Research Workflows

Large Language Models (LLMs) are now widely used in astrophysics, but do they actually make our lives easier, or do they merely invent new physics with enough confidence to hide a minus sign? In a specialized field where checking fluent hallucinations is itself labor-intensive, AI assistance can demand as much work as the task it claims to simplify. To evaluate where AI genuinely improves scientific workflows, we bypassed human trials and instead forced AI agents to cosplay as astrophysicists. We simulated 144 synthetic researchers, varying in career stage, AI awareness, and willingness to verify outputs, across 2,592 daily astrophysics research assignments. Comparing solo work against four styles of AI assistance produced 12,960 scored episodes. No assisted policy universally outperformed unassisted work in the primary Qwen production run. Instead, performance depends strongly on the task, the style of AI use, and the identity of the actor. While cautious assistance helps on creative, extractive, and critique-oriented tasks, it can fail catastrophically on derivation-heavy physics. A full actor-swap DeepSeek rerun changes that picture materially: verification-heavy use becomes the strongest assisted policy, two assisted modes enter the higher-utility/lower-risk quadrant, and the derivation-heavy fragility that dominates the Qwen production run largely disappears. In its current form, AI is useful, but only conditionally, its value is uneven, task-specific, and shaped jointly by workflow, usage policy, and which LLM you are using.

astro-ph.IM↗

arXiv2026

First-Principles Polar-Cap Currents in Multipolar Pulsar Magnetospheres

X-ray pulse-profile modeling of millisecond pulsars offers a direct route to measuring neutron star masses and radii, thereby constraining the dense-matter equation of state. However, standard analyses typically rely on \emph{ad hoc} hotspot parameterizations rather than self-consistent physical models. While connecting surface heating directly to the magnetospheric geometry provides a more natural physical pathway, computing global magnetospheric solutions is too computationally expensive to perform on-the-fly during parameter inference. In this work, we bridge this gap by deriving fully analytic, first-principles expressions for surface return currents in mixed dipole--quadrupole magnetospheres. Working within force-free electrodynamics, we generalize the field-aligned current invariant $\Lambda$, the crucial scalar that maps the far-zone magnetic structure to the near-zone heating rate, from the standard dipole approximation to arbitrary quadrupolar configurations. We demonstrate that even when the quadrupole component is sub-dominant in the far zone (the mixing regime), using a dipole-based heating prescription fails to capture the significant enhancement or suppression of the return-current density on the polar cap. Our consistent quadrupole-aware framework reveals that these multipolar currents redistribute the surface heating, leading to systematic discrepancies in predicted pulse profiles that are amplified by atmosphere beaming and can reach $\sim 30\%$ near pulse peaks. These results provide a rigorous analytic foundation for mapping global magnetic geometry to surface heating in multipolar magnetospheres, enabling physically consistent inference beyond the idealized dipole approximation.

astro-ph.HE↗

arXiv2025

Selection of Supervised Learning-based Sparse Matrix Reordering Algorithms

Sparse matrix ordering is a vital optimization technique often employed for solving large-scale sparse matrices. Its goal is to minimize the matrix bandwidth by reorganizing its rows and columns, thus enhancing efficiency. Conventional methods for algorithm selection usually depend on brute-force search or empirical knowledge, lacking the ability to adjust to diverse sparse matrix structures.As a result, we have introduced a supervised learning-based model for choosing sparse matrix reordering algorithms. This model grasps the correlation between matrix characteristics and commonly utilized reordering algorithms, facilitating the automated and intelligent selection of the suitable sparse matrix reordering algorithm. Experiments conducted on the Florida sparse matrix dataset reveal that our model can accurately predict the optimal reordering algorithm for various matrices, leading to a 55.37% reduction in solution time compared to solely using the AMD reordering algorithm, with an average speedup ratio of 1.45.

cs.DC↗

arXiv2025

GPU-Accelerated X-ray Pulse Profile Modeling

Pulse-profile modeling (PPM) of thermal X-ray emission from rotation-powered millisecond pulsars enables simultaneous constraints on the mass $M$, radius $R$, and hence the equation of state of cold, dense matter. However, Bayesian PPM has faced a hard accuracy-speed bottleneck: current production resolutions used to keep inference tractable can under-resolve extreme hotspot geometries and bias the waveform computation, whereas the higher resolutions that remove this bias push forward models to minutes per evaluation, making inference impractical. We break this trade-off with, to our knowledge, the first public GPU-accelerated X-ray PPM framework that matches established benchmarks to within $\sim10^{-3}$ relative accuracy even for extreme geometries, while collapsing minutes-long high-fidelity computations to $2$--$5$ ms on an RTX 4080 ($10^{3}$--$10^{4}\times$ speedups), enabling posterior exploration at resolutions and complexities previously out of reach. We further uncover a bias near the interpolation boundaries of atmosphere lookup tables, demonstrate it with two diagnostic tests, and counter it with a mixed-order interpolator. Together, these advances enlarge the feasible hotspot model space and reduce key systematics in PPM, strengthening inferences for current and future X-ray missions.

astro-ph.HE↗

arXiv2025

Revealing Dark Matter's Role in Neutron Stars Anisotropy: A Bayesian Approach Using Multi-messenger Observations

Dark matter (DM) continues to evade direct detection, but neutron stars (NSs) serve as natural laboratories where even a modest DM component can alter their structure. While many studies have examined DM effects on NSs, they often rely on specific choices of equations of state (EOS) models, assume isotropy, and lack a Bayesian statistical framework, limiting their predictive power. In this work, we present a Bayesian framework that couples pressure-anisotropic nuclear EOS to a self-interacting fermionic DM component, constrained by NICER and GW170817 data. Our results show that DM mass fractions up to $\sim10\%$ remain consistent with current data, which softens the high-density EOS, leading to reduced stellar radii and tidal deformabilities while requiring negligible pressure anisotropy. Bayesian model comparison reveals no statistically significant preference between pure baryonic and DM-admixed NSs, indicating that DM inclusion enhances physical realism without complexity penalties. However, existing data cannot tightly constrain the DM parameters, and our empirical radius definition introduces a systematic bias toward the DM core configurations. To address this, we therefore introduce the DM radius span $\Delta R_\chi \equiv R_{\chi,\mathrm{max}} - R_{\chi,\mathrm{min}}$ as a unified diagnostic for DM distributions. This parameter simultaneously characterizes core-halo transition features while exhibiting strong linear correlations ($\Delta R_\chi < 4\,\mathrm{km}$) with both DM and BM parameters, providing a clear avenue for future constraints. Our approach bridges current limitations and future potential in probing DM through compact star observations.

astro-ph.HE↗

arXiv2025

Covariant Energy Density Functionals for Neutron Star Matter Equation of State Modeling: Cross-Comparison Analysis Using \texttt{CompactObject}

This study analyzes and contrasts different phenomenological methods used to model the nuclear equation of state (EOS) for neutron star matter based on covariant energy density functionals (CEDF). Using two complementary methodologies, we seek to capture a comprehensive picture of the potential behaviors of ultra-dense nucleonic matter and identify the most plausible models based on current observational and experimental constraints. Observational data from radio pulsar timing, gravitational wave detection of GW170817, and X-ray timing provide critical benchmarks for testing the models. We have derived the EOS posteriors for various CEDF models within the \texttt{CompactObject} package, utilizing recent observational data on neutron stars, state-of-the-art theoretical constraints from chiral effective field theory ($\chi$EFT) calculations for pure neutron matter at low densities, and pQCD-derived constraints. Our analysis has demonstrated that while all considered CEDF models broadly reproduce current astrophysical and theoretical constraints, subtle yet important differences persist among them, with each framework exhibiting distinct characteristics at supra-nuclear density. This is in particular true for the proton fraction inside neutron stars, but also supported by the models' behavior with respect to the pure neutron matter EOS and the density dependence of the speed of sound. Our study highlights the sensitivity of dense matter predictions to the underlying EOS parameterizations and the priors considered.

nucl-th↗

arXiv2025

Model-Independent Determination of the Tidal Deformability of a 1.4 $M_{\odot}$ Neutron Star from Gravitational-Wave Measurements

Tidal deformability of a 1.4 $M_\odot$ neutron star provides a pivotal window into the physics of dense nuclear matter, bridging gravitational-wave(GW), electromagnetic observations and nuclear physics. In this work, we present a novel, data-driven approach to constrain $\Lambda_{1.4}$ without invoking specific equation-of-state(EOS) models. By interpolating directly over the mass--tidal-deformability posteriors from GW170817, we obtain an EOS-independent constraint of $ \Lambda_{1.4} \;=\; 222.89_{-98.85}^{+420.33}. $ We further combine these GW-based results with the X-ray EOS-independent constraint from \cite{Huang_2025}, deriving a multimessenger limit of $ \Lambda_{1.4} \;=\; 265.18_{-104.38}^{+237.88}, $ which remains largely EOS agnostic. This framework demonstrates that higher-order terms neglected in linear expansion methods do not significantly affect $\Lambda_{1.4}$ estimates under current observational uncertainties. As gravitational-wave detectors improve in sensitivity and more binary neutron-star mergers are discovered, our purely data-driven strategy can serve as a robust standard baseline for extracting neutron-star interior properties without relying on unverified EOS models.

astro-ph.HE↗

arXiv2025

Hidden Twin Star Solutions from an Agnostic Speed-of-Sound Model: Confronting XTE J1814--338's Extreme Compactness

The twin star configuration, where two neutron stars share the same mass but exhibit different radii, arises from a strong first-order phase transition within the stellar interior. In widely used equation of state (EoS) meta-models, such as the Polytrope (PP) and Speed-of-Sound (CS) models, this first-order phase transition behavior can be naturally mimicked by tuning some model parameters. Here, we systematically explore the under-explored parameter space within one of a widely adopted CS model that leads to twin stars via a strong first-order phase transition. Within this twin-star subspace, we perform a comprehensive Bayesian analysis that integrates mass--radius (MR) constraints from X-ray observations of rotation-powered millisecond pulsars. The resultant twin star branch, situated within the 1--1.2 $M_{\odot}$ mass range and approximately 7 km in radius, surprisingly coincides with the MR ranges proposed for the recent anomaly in the Accreting Millisecond X-ray Pulsars XTE J1814--338 (J1814), suggesting a hybrid twin star configuration. Moreover, incorporating the J1814 observation as an additional constraint yields an extreme phase transition pressure $P_{\text{trans}} = 108.9_{-4.85}^{+6.46}$ MeV/fm$^3$, a transition density of $\varepsilon_{\text{trans}}/\varepsilon_0 = 4.847_{-0.134}^{+0.271}$(where $\varepsilon_0$ is the nuclear saturation energy density) and an energy density jump $\Delta \varepsilon = 558.7_{-278.7}^{+303.6}$ MeV/fm$^3$, corresponding to $\Delta \varepsilon/\varepsilon_0 = 3.716_{-1.854}^{+2.020}$. Notably, to satisfy all astrophysical constraints, the speed of sound inside of the hybrid twin star core is driven toward the speed of light ($c_s^2/c^2 > 0.9$), indicating the potential presence of strongly interacting, exotic matter in this core region.

astro-ph.HE↗

arXiv2025

The persistent shift in spin-down rate following the largest Crab pulsar glitch rules out external torque variations due to starquakes

It was previously believed that, the long-term persistent increase in the spin-down rate of the Crab pulsar following a glitch is direct evidence of a starquake-induced glitch or at least related to a starquake. Using radio data covering 1710 days following the 2017 glitch, we obtain an extreme persistent increase of the spin-down rate, which allows to test two prevailing models related to starquake through an interrelation analysis between glitch size (the amplitude of the frequency step at a glitch) and persistent increase in the spin-down rate of the star. Our results do not support the hypothesis that glitches induce the external torque variation of the Crab pulsar, which may indicate no occurrence of starquake during the Crab pulsar glitch. This can explain why no changes in the radio and X-ray flux, pulse profile and spectrum of the Crab pulsar have been observed. We also suggest an internal mechanism due to superfluidity as an explanation for the long-term persistent shift in spin-down rate of the Crab pulsar following the relatively large glitches.

astro-ph.HE↗

arXiv2025

The Cosmic One-Eyed Smile: Revealing the Hidden Face of Mike Wazowski

We present a novel and somewhat whimsical approach to pulsar hotspot modeling by drawing inspiration from the iconic one-eyed monster, Mike Wazowski, from \emph{Monsters, Inc.}. Utilizing X-ray high-quality timing data from NICER, we apply a Bayesian inference framework to model the X-ray pulse profile of PSR J0437--4715. Our analysis employs a \emph{Wazowski Configuration} (WC) in which the conventional hotspot parametrization is replaced with a predefined image template, whose redness and size are adjusted to mimic temperature variations. The results reveal a configuration where two hotspots--one brighter and smaller in the north represents the energetic ``University time Wazowski", and one larger yet cooler in the south represents the ``Monster, Inc. time Wazowski"--combine to produce the observed X-ray pulse profile. These findings not only demonstrate the sensitivity of pulse profile modeling to hotspot morphology but also open up the intriguing possibility that the X-ray emission of some pulsars may be interpreted as a cosmic homage to our favorite animated character.

astro-ph.HE↗

arXiv2025

Physics motivated models of pulsar X-ray hotspots: off-center dipole configurations

Recently, it was proposed that an off-center dipole magnetic configuration, together with a non-trivial temperature profile, may be the best model to explain the X-ray light curve of PSR J0030+0451 observed by the Neutron Star Interior Composition Explorer (\emph{NICER}). Using a theoretical model for the electric current density in a force-free pulsar magnetosphere, we compute from first principles the distribution of electric current over the polar cap associated with an off-center magnetic dipole. We then use a simple prescription to compute the resulting temperature distribution, which allows us to derive the observed X-ray light curve. We investigate the role of the volumetric return current region in the polar cap and find that although it does not make a big difference in an aligned dipole case, the difference can be bigger in the case of an off-center dipole. Finally, we apply Markov Chain Monte Carlo (MCMC) fitting to the X-ray light curves of pulsars PSR J0030+0451 and PSR J0437--4715 with and without the volumetric return current, and find that our model can reasonably recover the observed X-ray light curves.

astro-ph.HE↗

arXiv2025

Constraining first-order phase transition inside neutron stars with application of Bayesian techniques on PSR J0437-4715 NICER data

Understanding the existence of exotic matter phases and phase transitions within the core of neutron stars is crucial to advancing our knowledge of cold-dense matter physics. Recent multi-messenger observations, including gravitational waves from neutron star mergers and precise X-ray data from NASA's Neutron Star Interior Composition Explorer (NICER) mission, have significantly constrained the neutron star equation of state (EOS). This study investigates the effects of phase transitions in neutron stars, focusing on NICER's latest observation of PSR J0437--4715. We employ Bayesian inference techniques to evaluate the presence of first-order phase transitions using a piecewise polytropic EOS model. Our analysis incorporates data from multiple NICER sources, to refine constraints on key phase transition parameters, including critical density and transition depth. We find that including data from PSR J0437--4715 improves the evidence of phase transitions and tightens the EOS constraints, especially at higher densities. However, Bayes factor analysis only indicates a slight preference for models without phase transitions and current observational precision is insufficient to draw definitive conclusions. In particular, this polytropic model identifies the critical phase transition mass of neutron stars as being close to 1.4 solar masses, which coincides with the approximate mass range of PSR J0437--4715. This work emphasizes the importance of precise measurements of PSR J0437--4715 for deepening our understanding of neutron star interiors and exploring potential new physics at extreme densities.

astro-ph.HE↗

arXiv2025

Data determination of HQET parameters in inclusive charm decays

This work delves into the phenomenology of electronic inclusive decays of $D$ mesons, encompassing $D^0, D^+, D^+_s\to Xe^{+}\nu$. The theoretical formulas for the decay widths and electron energy moments of these decays are presented as expansions with powers of $\alpha_s$ and $\Lambda_{\rm QCD}/m_c$. Remarkably, the expansion exhibits excellent convergence properties when we choose the 1S mass scheme for charm. The formulas are subsequently fitted to experimental data, and the $D$ meson matrix elements of operators in the heavy quark effective theory are hence determined by data for the first time, including \begin{align} \mu^2_\pi(D^{0,+}) &= (0.09\pm 0.05) \mathrm{GeV}^2, \qquad \qquad \mu^2_\pi(D^{+}_s) = (0.11\pm 0.05) \mathrm{GeV}^2, \nonumber \\ \mu^2_G(D^{0,+}) &= (0.32\pm 0.02) \mathrm{GeV}^2, \qquad \qquad \mu^2_G(D^{+}_s) = (0.43\pm 0.02) \mathrm{GeV}^2, \nonumber \\ \rho_D^3(D^{0,+}) &= (-0.003\pm 0.002) \mathrm{GeV}^3, \qquad\ \rho_D^3(D^{+}_s) = (-0.004\pm 0.002) \mathrm{GeV}^3, \nonumber \\ \rho_{LS}^3(D^{0,+}) &= (0.004\pm 0.002) \mathrm{GeV}^3, \qquad \ \ \ \rho_{LS}^3(D^{+}_s) = (0.005\pm 0.002) \mathrm{GeV}^3 . \nonumber \end{align} These determined parameters will play a crucial role as inputs in various physical quantities, including $D$ meson lifetimes.

hep-ph↗

arXiv2024

Equation of State Independent Determination on the Radius of a 1.4 $M_{\odot}$ Neutron Star Using Mass-Radius Measurements

Traditional methods for determining the radius of a 1.4 $M_{\odot}$ neutron star ($R_{1.4}$) rely on specific equations of state (EOS) models that describe various types of dense nuclear matter. This dependence on EOS models can introduce substantial systematic uncertainties, which may exceed the measurement uncertainties when constraining $R_{1.4}$. In this study, we explore a novel approach to constraining $R_{1.4}$ using data from NICER observations of PSR J0030+0451 (J0030) and PSR J0437-4715 (J0437). However, this work presents a more data-driven analysis framework, substantially decreasing the need for EOS assumptions. By analyzing the Mass-Radius measurements of these two neutron stars, we infer $R_{1.4}$ using statistical methods based mostly on observational data. We examine various hotspot configurations for J0030, along with new J0437 observations, and their effects on the inferred radius. Our results are consistent with X-ray timing, gravitational wave, and nuclear physics constraints, while avoiding EOS-related biases. The same method has also been applied to a simulated mass-radius dataset, based on our knowledge of future X-ray telescopes, demonstrating the model's ability to recover the injected $R_{1.4}$ value in certain cases. This method provides a data-driven pathway for extracting neutron star properties and offers a new approach for future observational efforts in neutron star astrophysics.

astro-ph.HE↗

arXiv2024

Bayesian inference of strangeon matter using the measurements of PSR J0437-4715 and GW190814

The observations of compact star inspirals from LIGO/Virgo combined with mass and radius measurements from NICER provide a valuable tool to study the highly uncertain equation of state (EOS) of dense matter at the densities characteristic of compact stars. In this work, we constrain the solid states of strange-cluster matter, called strangeon matter, as the putative basic units of the ground state of bulk strong matter using a Bayesian statistical method, incorporating the mass and radius measurements of PSR J0030+0451, PSR J0740+6620, and the recent data for the $1.4\ M_{\odot}$ pulsar PSR J0437-4715. We also include constraints from gravitational wave events GW170817 and GW190814. Under the prior assumption of a finite number of quarks in a strangeon, $N_{\rm q}$, our analysis reveals that current mass-radius measurements favor a larger $N_{\rm q}$. Specifically, the results support the scenario where a strangeon forms a stable bound state with $N_{\rm q}=18$, symmetric in color, flavor, and spin spaces, compared to the minimum $N_{\rm q}$ prior. The comparative analyses of the posterior EOS parameter spaces derived from three-parameter model and two-parameter model demonstrate a consistent prediction under identical observational constraints. In particular, our results indicate that the most probable values of the maximum mass are found to be $3.58^{+0.16}_{-0.12}\ M_{\odot}$ ($3.65^{+0.18}_{-0.16}\ M_{\odot}$) at $90\%$ confidence level for three-parameter (two-parameter) EOS considering the constraints of GW190814. The corresponding radii for $1.4\ M_{\odot}$ and $2.1\ M_{\odot}$ stars are $12.04^{+0.27}_{-0.31}~\rm km$ ($12.16^{+0.26}_{-0.31}~\rm km$) and $13.43^{+0.31}_{-0.32}~\rm km$ ($13.60^{+0.29}_{-0.34}~\rm km$), respectively. This result may impact interestingly on the research of multiquark states, which could improve our understanding of the nonperturbative strong force.

astro-ph.HE↗

arXiv2024

CompactObject: An open-source Python package for full-scope neutron star equation of state inference

The CompactObject package is an open-source software framework developed to constrain the neutron star equation of state (EOS) through Bayesian statistical inference. It integrates astrophysical observational constraints from X-ray timing, gravitational wave events, and radio measurements, as well as nuclear experimental constraints derived from perturbative Quantum Chromodynamics (pQCD) and Chiral Effective Field Theory ($\chi$EFT). The package supports a diverse range of EOS models, including meta-model like and several physics-motivated EOS models. It comprises three independent components: an EOS generator module that currently provides seven EOS choices, a Tolman-Oppenheimer-Volkoff (TOV) equation solver, that allows the determination of the Mass Radius and Tidal deformability as observables, and a comprehensive Bayesian inference workflow module, including a complete pipeline for implementing EOS Bayesian inference. Each component can be used independently in different scientific research contexts, such as nuclear physics and astrophysics. In addition, CompactObject is designed to work in synergy with existing software such as CompOSE, allowing the use of the CompOSE EOS database to extend the EOS options available.

astro-ph.HE↗

arXiv2024

Constraining a relativistic mean field model using neutron star mass-radius measurements II: Hyperonic models

We investigate whether measurements of the neutron star mass and radius or the tidal deformability can provide information about the presence of hyperons inside a neutron star. This is achieved by considering two inference models, with and without hyperons, based on a field-theoretical approach. While current observations do not distinguish between the two scenarios, we have shown that data simulating expected observations from future large area X-ray timing telescopes could provide some information through Bayes factors. Inference using simulated data generated from an EOS containing hyperons decisively favours the hyperonic model over the nucleonic model. However, a 2\% uncertainty in the mass and radius determination may not be sufficient to constrain the parameters of the model when only six neutron star mass-radius measurements are considered.

astro-ph.HE↗