The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James Webb Space Telescope (JWST) have made information about distant objects more easily accessible, resulting in extensive amounts of valuable data. As part of this work-in-progress study, we are working to create an atmospheric absorption spectrum prediction model for exoplanets. The eventual model will be based on both collected observational spectra and synthetic spectral data generated by the ROCKE-3D general circulation model (GCM) developed by the climate modeling program at NASA's Goddard Institute for Space Studies (GISS). In this initial study, spline curves are used to describe the bin heights of simulated atmospheric absorption spectra as a function of one of the values of the planetary parameters. Bayesian Adaptive Exploration is then employed to identify areas of the planetary parameter space for which more data are needed to improve the model. The resulting system will be used as a forward model so that planetary parameters can be inferred given a planet's atmospheric absorption spectrum. This work is expected to contribute to a better understanding of exoplanetary properties and general exoplanet climates and habitability.

New Delhi: Utilising an Artificial Intelligence-based algorithm, Indian Astronomers have devised a new approach for identifying potentially habitable planets with a high probability. Since time immemorial, humanity has been looking at the cosmos and believing that other inhabited worlds are out there. Current estimates are that the number of planets in our Galaxy alone run into billions, possibly a number greater than the number of stars itself. The question that naturally arises is whether there are other life-harboring planets and if there is a way to predict which exoplanet can potentially harbour life? Of India, along with astronomers from BITS Pilani, Goa campus have devised a new approach -- an anomaly detection method -- by which they can identify potentially habitable ones with a high probability.