Research Interests of Yogesh Wadadekar
Those wanting to do a student project with me are requested to read the student FAQ. My research work covers three broad areas - the evolution of normal (non-AGN, sometimes star-forming) galaxies, the radio properties of galaxies (primarily AGN) and software and technology development, with a major focus on AI/ML techniques.
Galaxy Evolution
- Bulges of galaxies: spirals and ellipticals and lenticulars (ApJ Letter, longer MNRAS paper). A related paper explored bar fraction in lenticulars as a function of luminosity and environment. We reported on systematic differences in the star formation history of bright and faint lenticular galaxies. My short review article summarises some of our early work in this area. The dependence of pseudo bulge fraction on environment, increased occurence of S0s in high density environments,age bimodality in the central region of pseudo bulges in lenticulars, why classical bulges are more common in lenticulars, and a spectral study of pseudobulges in lenticulars using SALT formed the subject of recent papers.
- Star-formation in and around galaxies: I used the WFPC2 parallels of the Hubble Ultra Deep Field to study the properties of star-forming galaxies at z < 2 and Lyman break galaxies at higher redshifts (AAS Poster,AJ paper). We also investigated the importance of morphological K correction in UV bright galaxies. For blue cloud galaxies with 0 < z < 1, we studied the radio-FIR correlation. We also studied the dependence of star formation on the morphology and environment in massive galaxies in the local Universe. Outlying H-alpha emitters and the discovery of a giant hydrogen ring formed the subjects of separate studies. We identified and studied a rare population of star-forming S0 galaxies in SDSS-MaNGA. We studied the structure and kinematics of star-forming elliptical galaxies with SDSS-MaNGA. I was involved with a project that discovered low-metallicity warm plasma in a galaxy overdensity environment with HST/COS.
- Low-surface brightness features in galaxies: We serenditipitously discovered a galaxy with the longest known tidal tail
- High redshift galaxies: We discovered the highest redshift spiral galaxy known using JWST data.
Radio Properties of Galaxies
- Radio continuum emission from AGN: For my doctoral thesis, I studied radio emission from Quasars. A companion paper discussed radio emission from Seyfert galaxies. My thesis was titled Optical studies of VLA FIRST Survey radio sources PDF. We also made GMRT follow up observations of an AGN cum luminous, type IIn supernova. We also studied Kiloparsec-scale radio emission in Seyfert and LINER galaxies. As a follow-up work, we discovered 4 extremely rare double lobed radio AGN hosted by spiral galaxies. We also discovered a relic, giant radio galaxy at z ~1.3 and presented a composite optical spectrum of Seyfert 1 galaxies. We discovered another giant radio galaxy at z~0.57 and some interesting double-double radio galaxies. Multi-frequency characterisation of remnant radio galaxies in the Lockman Hole field was the topic of another work. An overview paper on remnants, discovery of new remnants in the XMM-LSS field, remnants of small angular sizes followed.
- Radio continuum emission from galaxy clusters: We have developed an upper limit calculator to obtain upper limits on extended cluster radio haloes. We used this software on GMRT archival observations to detect and characterise radio haloes in 39 galaxy clusters.
- High redshift radio galaxies: We use deep radio observations with the GMRT to identify high redshift radio sources using the steep spectrum technique. By combining radio data with deep optical and infrared data we can characterise steep spectrum sources. We found an interesting relic, high redshift giant radio galaxy and another high redshift giant radio galaxy. We have also studied the nature of Infrared Faint Radio Sources (IFRS) and dust obscured radio-AGN using our GMRT data.
- Radio continuum observations of nearby spirals: We studied the radio continuum emission of 6 nearby spirals to put constraints on the generation, diffusion and energy losses of cosmic ray electrons at scales of ~1 kpc.
Software and Technology Development
- Square Kilometer Array: Since 2010, I have been part of a group at NCRA working on SKA related activities. My work mainly focussed on the Monitoring and Control System for the proposed SKA telescope. A paper on our work was presented at the URSI General Assembly 2011. Early work done by our group is described in a series of documents (zip file) prepared for the concept design review held in Nov. 2011 in Pune and in a SPIE paper. A current overview was published in 2023. Many of the technologies we developed for the SKA were implemented in a next generation monitoring and control system for the GMRT. For the S20 policy webinar on Astroinformatics and Recommendations for global cooperation, I led a panel on Large projects in Astronomy in the Indian context. I am a member of the continuum science group of SKA-India. In 2016, we wrote a paper summarising our science interests.
- SKA Regional Centre: As a member of the SKA Regional Centre Steering Committee (SRCSC), I coordinate efforts to build a regional centre in India. I have contributed to a number of documents that are being developed to flesh out the ideas detailing a global network of SKA Regional Centres called SRCNet, including an overview paper.
- Astronomy software development: During 1997-1999 I worked on the development of a bulge disk decomposition code called fitgal. I worked on Pymorph a Python based software for automated galaxy morphology estimation. At STScI, I was involved in data processing for the Archival Pure Parallels Project (APPP). Our data processing techniques are decribed in detail in a paper. We attempted to use these data for a measurement of the weak lensing cosmic shear (AAS Poster). I was also involved in efforts to develop a telescope scheduler program for the GMRT. Since 2008, I have been involved with various aspects of the NCRA Archive and Proposal Management System - NAPS
- Machine learning methods in astronomy: I worked on the problem of star galaxy classification using the the Difference boosting neural network (DBNN). We used the DBNN to classify unresolved photometric detections in the SDSS DR7 into stars, galaxies and quasars. I also proposed the use of a new approach to photometric redshift estimation. We used deep learning to predict the star formation properties and the bulge-to-total luminosity ratio of galaxies. We used deep learning for morphological classification of radio galaxies. I contributed to the paper on Computational Astrophysics, Data Science and AI/ML in Astronomy, a chapter of the ASI Vision Document
More Links to my work
Detailed Curriculum Vitae
Publications on Google Scholar
Publications
on ADS
Publications on arXiv.org
Media Coverage of my work