arXiv subjects

Mai Li

Publications and source records attributed to Mai Li.

3 recordsLinked to original sources

arXiv2026

Dynamically Stable Magnetic Fields in Relativistic Neutron Stars: A Diverse Landscape of GRMHD Equilibria

Neutron stars endowed with purely poloidal or purely toroidal magnetic fields are known to be unstable, and settle into mixed poloidal-toroidal configurations. The end state of these instabilities is still poorly understood. It is unclear whether this state is unique or whether the dynamically stable configuration has memory of the initial conditions. In this work, we perform long-term, fully general relativistic magnetohydrodynamic simulations of equilibrium configurations of slowly rotating neutron stars endowed with self-consistent mixed poloidal and toroidal magnetic fields. Our initial configurations differ in their initial magnetization and magnetic field geometry. We evolve the initial equilibria for tens of Alfvén timescales until well after any instabilities have saturated. Employing an array of diagnostic tools from visualizations to the structure of the plasma four-current and a vector spherical harmonic decomposition of the settled magnetic fields, we characterize the structure of the dynamically stable, settled magnetic equilibria. We find that the settled configurations are not unique. Instead, they exhibit diverse multipolar structures, with higher-order modes contributing substantially. Our findings demonstrate that there is memory of the magnetic-field initial conditions, and that there is no unique dynamically stable magnetic-field geometry for neutron stars. Nevertheless, we find that the angle-averaged radial profile of the magnetic field toroidal to poloidal amplitude in the bulk of the settled configurations exhibits some degree of universality with typical values of order $20$--$40\%$.

astro-ph.HE↗

arXiv2025

DAmodel: Hierarchical Bayesian Modelling of DA White Dwarfs for Spectrophotometric Calibration

We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards ($16.5 < V < 19.5$) alongside three CALSPEC standards, from 912 Å to 32 $μ$m. The framework is the first of its kind to jointly infer photometric zeropoints and WD parameters (surface gravity $\log g$, effective temperature $T_{\text{eff}}$, extinction $A_V$, dust relation parameter $R_V$) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic Hubble Space Telescope Wide Field Camera 3 (HST/WFC3) UVIS and IR photometry, HST/STIS UV spectroscopy and ground-based optical spectroscopy to sub-percent precision. Photometric residuals for the sample are the lowest yet yielding $<0.004$ mag RMS on average from the UV to the NIR, achieved by jointly inferring time-dependent changes in system sensitivity and WFC3/IR count-rate nonlinearity. Our GPU-accelerated implementation enables efficient sampling via Hamiltonian Monte Carlo, critical for exploring the high-dimensional posterior space. The hierarchical nature of the model enables population analysis of intrinsic WD and dust parameters. Inferred spectral energy distributions from this model will be essential for calibrating the James Webb Space Telescope as well as next-generation surveys, including Vera Rubin Observatory's Legacy Survey of Space and Time and the Nancy Grace Roman Space Telescope.

astro-ph.IM↗

arXiv2021

Public Health Informatics: Proposing Causal Sequence of Death Using Neural Machine Translation

Each year there are nearly 57 million deaths around the world, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical in public health, as institutions and government agencies rely on death reports to analyze vital statistics and to formulate responses to communicable diseases. Inaccurate death reporting may result in potential misdirection of public health policies. Determining the causes of death is, nevertheless, challenging even for experienced physicians. To facilitate physicians in accurately reporting causes of death, we present an advanced AI approach to determine a chronically ordered sequence of clinical conditions that lead to death, based on decedent's last hospital discharge record. The sequence of clinical codes on the death report is named as causal chain of death, coded in the tenth revision of International Statistical Classification of Diseases (ICD-10); in line with the ICD-9-CM Official Guidelines for Coding and Reporting, the priority-ordered clinical conditions on the discharge record are coded in ICD-9. We identify three challenges in proposing the causal chain of death: two versions of coding system in clinical codes, medical domain knowledge conflict, and data interoperability. To overcome the first challenge in this sequence-to-sequence problem, we apply neural machine translation models to generate target sequence. Along with three accuracy metrics, we evaluate the quality of generated sequences with the BLEU (BiLingual Evaluation Understudy) score and achieve 16.04 out of 100. To address the second challenge, we incorporate expert-verified medical domain knowledge as constraint in generating output sequence to exclude infeasible causal chains. Lastly, we demonstrate the usability of our work in a Fast Healthcare Interoperability Resources (FHIR) interface to address the third challenge.

cs.LG↗