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
Radio properties of galaxies
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
My most frequented astrolinks.
Curriculum vitae.
My publications: Google Scholar
ADS
Media Coverage of my work
arXiv.org
yogeshw \at/ iitbombay.org