RESEARCH
Machine Learning-Driven Insights into Cloud
Condensation Nuclei Formation in Gaseous
Exoplanet Atmospheres
Clouds on exoplanets are hypothesized to account for the absence of expected molecular or atomic absorption features in optical and near-infrared spectra obtained from both space-based observations from telescopes (CHEOPS and JWST) and ground based telescopes (VLT). These clouds form through the condensation of thermally stable materials onto cloud condensation nuclei (CCN) via gas-surface reactions, playing a crucial role in shaping the observed atmospheric properties. In rocky exoplanets, CCN are supplied by processes such as sandstorms, combustion, and volcanic eruptions. However, gaseous exoplanets lack direct sources of CCN. Instead, CCN form through nucleation with a bottom up approach, where small molecules, undergo a series of chemical reactions to form larger molecular clusters. These clusters grow until they reach a size sufficient to undergo a phase transition from gas to solid, ultimately forming CCN. Previous studies have explored nucleation using various theories, including Classical Nucleation Theory, kinetic nucleation networks, modified Classical Nucleation Theory, and non-classical nucleation theory. All these approaches require thermochemical data for the nucleating species. While experimental studies have provided insights, limitations in replicating substellar atmospheric conditions, such as extreme temperatures and pressures, hinder their applicability. Quantum mechanical methods have been employed to address these challenges by optimizing cluster geometries and calculating thermochemical properties. However, these computationally expensive methods can take weeks to months for big clusters. In this project, we utilize machine learning (ML) models to predict the geometric and thermochemical properties of large molecular clusters. By combining in-house molecular cluster data with data from the literature, we train ML models to make accurate predictions of these properties. The ultimate goal of this approach is to identify clusters capable of undergoing a phase transition from the gas phase to the solid phase, serving as cloud condensation nuclei (CCN) for cloud formation in gaseous exoplanets.
RV Analysis of HD 10647.
Planetary System Signals in Debris Disc Around Nearby Stars
The search for exoplanets has become an area of increasing interest in astrophysics in recent years. Among various methods, the radial velocity (RV) technique has proven to be one of the most successful techniques for detecting exoplanets. A star with a planet will move in its own small orbit in response to the planet's gravity. This leads to variations in the speed with which the star moves toward or away from Earth, i.e., the variations are in the radial velocity of the star with respect to Earth. The radial velocity can be deduced from the displacement in the parent star's spectral lines due to the Doppler effect. The radial-velocity method measures these variations to confirm the presence of the planet using the binary mass function. However, we can only calculate the Msini value using this technique, and in order to calculate a mass value close to the true mass of the planet, we considered the debris disc as our subject of study. As debris discs and exoplanets are formed from the same protoplanetary disc, we consider the exoplanet orbit and debris disc to be coplanar and calculate the disc-aligned planet mass.
To initiate the process, we first identify stars with debris discs by employing a variety of sources (ALMA and Spitzer observations). Subsequently, we determine the inclination angles of these debris discs through a combination of comprehensive literature searches and a novel geometrical approach. For our RV analysis,, we used high-resolution spectroscopic data from HARPS, HIRES, HAMILTON, CORALIE, and APF. We then used DACE, Radvel, and Exostriker for the RV analysis. We first calculate the priors using a Keplerian fit and then perform the Markov Chain Monte Carlo method to calculate the orbital parameters of the planets. In summary, our study successfully updates the orbital parameters of exoplanets around HD 10647, HD 115617, GJ 581, HD 22049, HD 69830, and HD 142091. Furthermore, we used the inclination angles of the debris discs to determine the disc-aligned planet mass. Notably, we also introduce previously unidentified exoplanets orbiting HD 207129 and a long-term activity signal around HD 202628. Our contribution extends beyond individual exoplanet identification. For more detailed information, please refer to the publication section and access the paper for the preprint link.
Open Star Cluster
Open clusters are a type of star cluster composed of up to a few hundred to thousand stars that originate from a single massive molecular cloud. These stars are loosely connected through weak gravitational forces and are more diffusive than globular clusters, which are tightly bound by gravity and contain hundreds of thousands to millions of stars. Open clusters may disperse their component stars after a few million years due to gravitational interactions with other objects in their environment, such as giant molecular clouds. Open clusters are mainly found in new star-forming spiral and irregular galaxies rather than in elliptical galaxies with no new stars forming, as they are formed from the gas and dust in star-forming regions. Understanding the structure and evolution of open clusters is critical in understanding the properties of the Milky Way galaxy. To generate reliable research, existing open-cluster inventories collect all the necessary data to determine standardized open-cluster parameters. The Gaia DR3 catalogue is an essential resource for calculating the fundamental parameters of open clusters. The Gaia mission is a space observatory launched by the European Space Agency in 2013 to create a three-dimensional map of the Milky Way galaxy and study its star's properties and movements. The 2MASS data is another valuable resource for calculating interstellar reddening in open clusters. The 2MASS survey collected data in three near-infrared bands, making them ideal for studying distant and obscured objects such as star-forming regions, active galactic nuclei, and dust-obscured galaxies. To understand the properties of open star clusters better, we used the data sets from the Gaia DR3 catalogue to calculate the fundamental parameters of Berkeley 76 and the 2MASS data to estimate its interstellar reddening. These data sets help us determine the standardized parameters of Berkeley 76 and are critical in understanding the age, mass, and spatial distribution of it.
TESS lightcurve data HD 202628.
Rotational modulation stands as a fundamental technique extensively employed in the analysis of photometric data obtained from telescopes. It serves as a potent tool for elucidating the rotation periods of stars, thereby enhancing our comprehension of their dynamics and surface characteristics. With the advent of the Transiting Exoplanet Survey Satellite (TESS), which boasts unparalleled capabilities in surveying vast expanses of the sky and discerning subtle variations in luminosity, our grasp of stellar rotation has undergone a paradigm shift across diverse stellar categories. This technique leverages the intrinsic variability in a star's luminosity resulting from its rotation. As a star rotates, its surface features such as spots, faculae, and other phenomena come into view and subsequently recede from sight, inducing fluctuations in the observed flux. By meticulously monitoring these fluctuations over time, astronomers can glean invaluable insights into a star's rotation period and surface dynamics. In our analytical approach, we meticulously extracted TESS data using the SPOC pipeline. Employing the Lomb-Scargle periodogram, a robust statistical tool, we scrutinized the light curve—depicting brightness variations over time—to pinpoint the dominant periodic signal. Subsequently, through phase-folding of the TESS light curve, we discerned whether the identified dip resonated with a rotation period signal. Rigorous validation procedures were then implemented to confirm the presence of a rotational period signal, ensuring the reliability and accuracy of our findings.
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Calculation of Rotational Periods Using TESS Photometric Data Through the Rotational Modulation Technique
Variable Stars
Variable stars are fascinating astronomical objects that change in brightness over time. These stars vary in brightness due to a number of factors, including pulsations, eclipses, and eruptions. Some variable stars can vary in brightness by just a few hundredths of a magnitude, while others can vary by several magnitudes, making them some of the most extreme objects in the universe. One of the most well-known types of variable stars is the Cepheid variable. These stars have a regular pulsation period that is directly related to their intrinsic brightness, making them important for measuring distances in the universe. By observing the pulsation period of a Cepheid variable, astronomers can determine its intrinsic brightness and use this information to calculate its distance from Earth. This technique, known as the cosmic distance ladder, has been used to measure distances to galaxies billions of light years away. Another type of variable star is the eclipsing binary. These stars are actually two stars that orbit around a common center of mass. As they orbit, one star can pass in front of the other, causing a temporary decrease in brightness as seen from Earth. By studying the light curve of an eclipsing binary, astronomers can determine the size, mass, and other properties of the stars in the system. Variable stars can also provide important clues about the evolution of stars. For example, as a star ages, it can become unstable and begin to pulsate, leading to changes in its brightness. By studying the properties of pulsating stars, astronomers can better understand the internal structure and evolution of stars. Overall, variable stars are important objects for astronomers to study, as they provide insights into the properties and evolution of stars, as well as valuable tools for measuring distances in the universe.
