arXiv subjects

A. J. Kalita

Publications and source records attributed to A. J. Kalita.

3 recordsLinked to original sources

arXiv2026

The symphony of pulsations and binarity among massive stars using HERMES spectroscopy and TESS photometry

A wide range of variability mechanisms exist among intermediate mass and massive stars, which are not yet fully understood. Using complementary data sources for a large population of B- and O-type stars, we aim to study the prevalence and interplay of different types of variability, including binarity, pulsation, and rotation, to prepare for future modelling. To this end, we analyse high-resolution HERMES spectra and 2-min cadence TESS photometry and characterise the diverse variability observed within a population of 873 O- and B-type stars. The spectroscopic data were normalised using machine-learning techniques, compared to a grid of synthetic TLUSTY spectra to determine stellar parameters, and used to identify radial velocity variability. Photometric time series were analysed using standard frequency analysis methods to detect pulsations and rotational modulation signatures. We find that more than 93 per cent of the sample exhibits photometric variability. Photometric variability caused by pulsations is identified in 82 per cent of the sample, with dominant contributions from $β$ Cep and slowly pulsating B-type stars, as well as stochastic low-frequency variability. Based on a limited number of spectroscopic epochs, at least 14 per cent of the stars show evidence of binarity, including both eclipsing and spectroscopic systems. This work represents one of the largest homogeneous surveys of variability for intermediate-mass and massive stars in the Northern hemisphere, and complementing similar efforts in the Southern hemisphere. It provides a statistical framework for future studies of stellar structure and evolution, particularly in the context of asteroseismology.

astro-ph.SR↗

arXiv2026

SpecFANN: Spectral Fitting via Artificial Neural Networks I. A deep learning based fastwind emulator and fitting suite

The importance of massive stars cannot be overstated: they are powerful probes of the early universe, play a vital role in the chemical and mechanical evolution of their host environments and their end products allow us to study the most extreme physics in the universe. Obtaining accurate stellar and surface parameters for large samples of massive stars is vital to our understanding of how they evolve, and how their births, lives and deaths affect their surroundings. With the large volume of data expected from upcoming spectroscopic surveys, computational limitations will likely be the most important bottleneck impeding our progress. To address this and dramatically decrease computing times, we aim to develop a robust emulator for the FASTWIND radiative transfer and spectral synthesis code. Additionally, we aim to explore alternative fitting methods that have not been feasible until now due to computational costs. We calculate a set of FASTWIND synthetic spectra of OB-type stars, and we train a collection of neural networks to emulate these models. We also develop the open-source python package SpecFANN, which provides users with a suite of fitting methods that can be used with these or other user-generated neural networks. The majority of the trained neural networks reach average accuracies of better than ~0.01-0.1% for photospheric lines and better than ~0.1-1% for wind lines. SpecFANN is able to obtain robust and accurate stellar parameters that are consistent with the literature for a sample of 52 early-type stars. Using SpecFANN we find that we can achieve the same fit in ~1/360,000 of the time when compared to alternative techniques that rely on on-the-fly FASTWIND computations. We have demonstrated that neural networks offer a viable path forward to address the computational limitations of our current atmosphere analysis and stellar parameter determination methods for hot stars.

astro-ph.SR↗

arXiv2025

Large-scale variability in macroturbulence driven by pulsations in the rapidly rotating massive star Zeta Oph from high-cadence ESPRESSO spectroscopy and TESS photometry

Despite their importance, the dynamical properties of massive stars remain poorly understood. Rotation is a key driver of internal mixing and angular momentum transport, significantly influencing massive star evolution. However, constraining rotation from spectroscopy is challenging, as spectral lines often exhibit excess broadening beyond rotation. The origin of this additional broadening, typically attributed to large-scale velocity fields and commonly referred to as macroturbulence, remains uncertain and unconstrained. Here, we present the combined analysis of TESS photometry and rapid time-series spectroscopy using the high-resolution ESPRESSO instrument at the Very Large Telescope of the European Southern Observatory for the rapidly rotating and pulsating massive star Zeta Ophiuchi. Leveraging excellent temporal coverage, our analysis demonstrates that pulsation-induced variability leads to peak-to-peak scatter as large as 88 km/s in the observed macroturbulent velocity time series. We also demonstrate that time-averaged macroturbulent velocities are spectral line specific and can exceed 100 km/s . Furthermore, the macroturbulent velocities from shorter integration times are systematically lower than those derived from stacked spectra mimicking longer exposure times typically needed for fainter stars. These results highlight the role of pulsations in driving variable macroturbulence in massive stars, while also pointing to a potential bias in spectroscopic estimates of macroturbulence for fainter massive stars.

astro-ph.SR↗