arXiv subjects

Shivam Kumaran

Publications and source records attributed to Shivam Kumaran.

4 recordsLinked to original sources

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↗

arXiv2026

Explainable machine learning classification of \textit{Chandra} X-ray sources: SHAP analysis of multi-wavelength features

Extensive astronomical surveys, like those conducted with the {\em Chandra} X-ray Observatory, detect hundreds of thousands of unidentified cosmic sources. Machine learning (ML) methods offer an efficient, probabilistic approach to classify them, which can be useful for making discoveries and conducting deeper studies. In earlier work, we applied the LightGBM (ML model) to classify 277,069 {\em Chandra} point sources into eight categories: active galactic nuclei (AGN), X-ray emitting stars, young stellar objects (YSO), high-mass X-ray binaries, low-mass X-ray binaries, ultraluminous X-ray sources, cataclysmic variables, and pulsars. In this work, we present the classification table of 54,770 robustly classified sources (over $3σ$ confidence), including 14,066 sources at $>4σ$ significance. To ensure classification reliability and gain a deeper insight, we investigate the multiwavelength feature relationships learned by the LightGBM model, focusing on AGNs, Stars, and YSOs. We employ Explainable Artificial Intelligence (XAI) techniques, specifically, SHapley Additive exPlanations (SHAP), to quantify the contribution of individual features and their interactions to the predicted classification probabilities. Among other things, we find infrared-optical and X-ray decision boundaries for separating AGN/Stars, and infrared-X-ray boundaries for YSOs. These results are crucial for estimating object classes even with limited multiwavelength data. This study represents one of the earliest applications of XAI to large-scale astronomical datasets, demonstrating ML models' potential for uncovering physically meaningful patterns in data in addition to classification. Finally, our publicly available, extensive, and interactive catalogue will be helpful to explore the contributions of features and their combinations in greater detail in the future.

astro-ph.IM↗

arXiv2024

Probing properties of nearly two-hundred new active galactic nuclei

We present a comprehensive analysis of the X-ray spectral properties of 198 newly identified active galactic nuclei (AGNs), leveraging archival data from the {\it Chandra} X-ray Observatory. All these AGNs exhibit a powerlaw spectral signature spanning a broad energy range of $0.5-7.0$ keV, characterized by the photon index ($Γ$) values ranging from $0.3^{+0.16}_{-0.14}$ to $2.54^{+0.14}_{-0.13}$. Particularly, 76 of these AGNs display discernible levels of intrinsic absorption, after considering the Galactic absorption. The column densities associated with this local absorption ($n_{\rm H}^{\rm local}$) are within a range of $\sim 10^{19} - 10^{22}\ {\rm cm^{-2}}$. We study the cosmological evolution of AGNs using the variation of $n_{\rm H}^{\rm local}$ and $Γ$ with their estimated redshift. The intrinsic spectral signature did not reveal any significant cosmological evolution; however, a deficit of hard sources at high redshift is possibly intrinsic. Our sample covers several decades of broadband intrinsic luminosity ($L_{\rm B}^{\rm intr}$) ranging from $4.59^{+0.41}_{-0.41} \times 10^{42}$ to $2.4^{+0.12}_{-0.12} \times 10^{46}\, {\rm erg~s}^{-1}$ with peak at 1.84 redshift. We also investigate the hardness-luminosity diagram (HLD) to further probe the AGNs. We conduct a sanity check by applying our findings to known AGNs, and the results are consistent with our observations.

astro-ph.HE↗

arXiv2023

Automated classification of Chandra X-ray point sources using machine learning methods

A large number of unidentified sources found by astronomical surveys and other observations necessitate the use of an automated classification technique based on machine learning methods. The aim of this paper is to find a suitable automated classifier to identify the point X-ray sources in the Chandra Source Catalogue (CSC) 2.0 in the categories of active galactic nuclei (AGN), X-ray emitting stars, young stellar objects (YSOs), high-mass X-ray binaries (HMXBs), low-mass X-ray binaries (LMXBs), ultra luminous X-ray sources (ULXs), cataclysmic variables (CVs), and pulsars. The catalogue consists of approx 3,17,000 sources, out of which we select 2,77,069 point sources based on the quality flags available in CSC 2.0. In order to identify unknown sources of CSC 2.0, we use multi-wavelength features, such as magnitudes in optical/UV bands from Gaia-EDR3, SDSS and GALEX, and magnitudes in IR bands from 2MASS, WISE and MIPS-Spitzer, in addition to X-ray features (flux and variability) from CSC 2.0. We find the Light Gradient Boosted Machine, an advanced decision tree-based machine learning classification algorithm, suitable for our purpose and achieve $93\%$ precision, $93\%$ recall score and 0.91 Mathew's Correlation coefficient score. With the trained classifier, we identified 54,770 (14,066) sources with more than $3σ$ (4$σ$) confidence, out of which there are 32,600 (8,574) AGNs, 16,148 (5,166) stars, 5,184 (208) YSOs, 439 (46) HMXBs, 197 (71) LMXBs, 50 (0) ULXs, 89 (1) CVs, and 63 (0) pulsars. This method can also be useful for identifying sources of other catalogues reliably.

astro-ph.HE↗