arXiv subjects

Ushasi Bhowmick

Publications and source records attributed to Ushasi Bhowmick.

3 recordsLinked to original sources

arXiv2026

Yuti: A General-purpose Transit Simulator for Arbitrary Shaped Objects Orbiting Stars

We present a versatile transit simulator (Yuti) aimed at generating light curves for arbitrarily shaped objects transiting stars. Utilizing a Monte Carlo algorithm, it accurately models the stellar flux blocked by these objects, producing precise light curves. The simulator adeptly handles realistic background stars, integrating effects such as tidal distortions and limb darkening, alongside the rotational dynamics of transiting objects of arbitrary geometries. We showcase its wide-ranging utility through successful simulations of light curves for single- and multiplanet systems, tidally distorted planets, eclipsing binaries, and exocomets. Additionally, our simulator can simulate light curves for hypothetical alien megastructures of any conceivable shape, providing avenues to identify interesting candidates for follow-up studies. We demonstrate applications of Yuti in modeling a Dyson swarm in construction, Dyson rings, and Dyson disks, discussing how tidally locked Dyson disks can be distinguished from planetary light curves.

astro-ph.EP↗

arXiv2026

Sutra : An integrated framework for identification and characterization of filaments in the interstellar medium

Observations of the interstellar medium (ISM) at Far-infrared(FIR) and sub-millimetre (sub-mm) wavelengths reveal a complex filamentary structure of dust and gas, which plays a pivotal role in both low and high mass star formation. Large scale identification and characterization of filaments is crucial to establish a link between the ISM and the star formation. We present Sutra, a machine learning based framework that unifies filament identification and beam-scale physical characterization within a single automated pipeline. The framework employs a U-Net architecture to perform supervised segmentation on column density maps and is trained on five nearby (<500pc) molecular clouds from the Herschel Gould Belt Survey (HGBS), using consensus skeletons constructed from the union of filaments identified by DisPerSE and getsf. Rather than reproducing broad intensity-based masks, Sutra predicts crest-likelihood maps focused on filament spines. Beyond identification, Sutra characterizes the filaments at the beam resolution by extracting radial profiles perpendicular to the crest and deriving local structural parameters. The framework provides a parameter-free, computationally efficient approach for consistent filaments identification and systematic investigation of their local properties and shows stable behaviour across varying background conditions in controlled synthetic tests. We demonstrate its application on selected regions from Aquila, Orion and Polaris molecular clouds, and compare the derived filament characteristics with those obtained using existing algorithms. Sutra robustly recovers filamentary structures consistent with cylindrical profiles, including in relatively low-intensity and low-contrast environments, making it well suited for both region-specific studies and large-scale statistical analyses of early-stage star formation and ISM structure.

astro-ph.GA↗

arXiv2026

Beyond Spherical geometry: Unraveling complex features of objects orbiting around stars from its transit light curve using deep learning

Characterizing the geometry of an object orbiting around a star from its transit light curve is a powerful tool to uncover various complex phenomena. This problem is inherently ill-posed, since similar or identical light curves can be produced by multiple different shapes. In this study, we investigate the extent to which the features of a shape can be embedded in a transit light curve. We generate a library of two-dimensional random shapes and simulate their transit light curves with light curve simulator, Yuti. Each shape is decomposed into a series of elliptical components expressed in the form of Fourier coefficients that adds increasingly diminishing perturbations to an ideal ellipse. We train deep neural networks to predict these Fourier coefficients directly from simulated light curves. Our results demonstrate that the neural network can successfully reconstruct the low-order ellipses, which describe overall shape, orientation and large-scale perturbations. For higher order ellipses the scale is successfully determined but the inference of eccentricity and orientation is limited, demonstrating the extent of shape information in the light curve. We explore the impact of non-convex shape features in reconstruction, and show its dependence on shape orientation. The level of reconstruction achieved by the neural network underscores the utility of using light curves as a means to extract geometric information from transiting systems.

astro-ph.EP↗