arXiv subjects

Farhana Taiyebah

Publications and source records attributed to Farhana Taiyebah.

4 recordsLinked to original sources

arXiv2026

A comprehensive simulation framework for multi-modal kilonova observations from all-sky surveys

Current all-sky surveys such as Vera Rubin's Legacy Survey of Space and Time and the Zwicky Transient Facility (ZTF) promise a wealth of scientific gains that are contained in the millions of astrophysical transient candidates produced each night. Kilonovae, one such transient of interest, will be challenging to identify in the alert stream and will require efficient artificial intelligence models to parse the large, real-time influx of data. In order to build these large models, comprehensive multimodal datasets are necessary for training. Due to a lack of numerous kilonova observations, we propose $\texttt{kilonova-multimodal-emulator}$ -- a simulation pipeline for realistic, multimodal kilonova observations comprised of photometry, spectra, and images. We demonstrate this pipeline for ZTF-type observations, based on historical cadence and limiting magnitude information from the Bright Transient Survey (BTS) and using the latest radiative transfer kilonova models to produce a comprehensive dataset meant for the training of large artificial intelligence model.

astro-ph.IM↗

arXiv2026

Multipolar Magnetic-Field Inference for PSR J0740+6620 with Neural-Network-Accelerated NICER Pulse-Profile Modeling

We investigate the multipolar surface magnetic-field structure of the high-mass millisecond pulsar PSR J0740+6620 using the 32-bin bolometric NICER pulse profile of Dittmann et al. (2024). Building on the neural-network surrogate framework of Olmschenk et al. (2025), we model the emitting regions as open-field-line footpoints of an offset dipole plus axisymmetric quadrupole static vacuum field, rather than as prescribed geometric hotspots. We fix the stellar mass, radius, observer inclination, and hotspot temperature ratio to the Dittmann et al. (2024) maximum-likelihood values and explore the resulting 11-dimensional magnetic-field space. To make this feasible, we train convolutional neural-network surrogates on $5.12\times10^7$ synthetic bolometric light curves and use them in a parallel ensemble Markov Chain Monte Carlo calculation on 4000 CPU cores, accelerating likelihood evaluations by a factor of $\gtrsim 400$. We perform independent inferences for two calibrated temperature-weight prescriptions, Tw=1.31 and Tw=1.41, encoding the relative bolometric weight associated with the hotspot temperature difference. The posteriors, posterior-predictive light curves, and maximum-likelihood values are very similar, indicating weak sensitivity to this choice. The offset model reproduces the observed double-peaked profile and yields broad, multimodal posteriors, reflecting both the background-dominated data and degeneracies of the multipolar parameterization. The hotspot-density map shows that pulse phases constrain the approximate azimuthal placement of the emission, while latitude, surface extent, and morphology remain weakly constrained. A restricted zero offset run is disfavored within the adopted field basis. This work extends neural-network-accelerated magnetic-field inference to PSR J0740+6620 and motivates future energy-dependent, force-free, and joint X-ray/$γ$-ray extensions.

astro-ph.HE↗

arXiv2026

Accelerating Posterior Inference from Pulsar Light Curves via Learned Latent Representations and Local Simulator-Guided Optimization

Posterior inference from pulsar observations in the form of light curves is commonly performed using Markov chain Monte Carlo methods, which are accurate but computationally expensive. We introduce a framework that accelerates posterior inference while maintaining accuracy by combining learned latent representations with local simulator-guided optimization. A masked U-Net is first pretrained to reconstruct complete light curves from partial observations and to produce informative latent embeddings. Given a query light curve, we identify similar simulated light curves from the simulation bank by measuring similarity in the learned embedding space produced by pretrained U-Net encoder, yielding an initial empirical approximation to the posterior over parameters. This initialization is then refined using a local optimization procedure using hill-climbing updates, guided by a forward simulator, progressively shifting the empirical posterior toward higher-likelihood parameter regions. Experiments on the observed light curve of PSR J0030+0451, captured by NASA's Neutron Star Interior Composition Explorer (NICER), show that our method closely matches posterior estimates obtained using traditional MCMC methods while achieving 120 times reduction in inference time (from 24 hours to 12 minutes), demonstrating the effectiveness of learned representations and simulator-guided optimization for accelerated posterior inference.

stat.ML↗

arXiv2025

HARD: A Performance Portable Radiation Hydrodynamics Code based on FleCSI Framework

Hydrodynamics And Radiation Diffusion} (HARD) is an open-source application for high-performance simulations of compressible hydrodynamics with radiation-diffusion coupling. Built on the FleCSI (Flexible Computational Science Infrastructure) framework, HARD expresses its computational units as tasks whose execution can be orchestrated by multiple back-end runtimes, including Legion, MPI, and HPX. Node-level parallelism is delegated to Kokkos, providing a single, portable code base that runs efficiently on laptops, small homogeneous clusters, and the largest heterogeneous supercomputers currently available. To ensure scientific reliability, HARD includes a regression-test suite that automatically reproduces canonical verification problems such as the Sod and LeBlanc shock tubes and the Sedov blast wave, comparing numerical solutions against known analytical results. The project is distributed under an OSI-approved license, hosted on GitHub, and accompanied by reproducible build scripts and continuous integration workflows. This combination of performance portability, verification infrastructure, and community-focused development makes HARD a sustainable platform for advancing radiation hydrodynamics research across multiple domains.

physics.comp-ph↗