Gaia

[ Projects ]

• Source Classification Using Machine Learning
• Astrophysical Parameter Estimation using Machine Learning and Bayesian Analysis
• CU8 Validation

[ Copyright: Gaia ESA ]

Gaia is an ESA satellite project launched at Dec. 2013. It will map our Galaxy in 3D by measuring six astrometric parameters (i.e. two positions, two proper motions, parallax, and radial velocity) of one billion stars over the whole sky. Astrometric accuracy of Gaia for G type star is 5-16 μas for bright stars, 24 μas at V = 15 mag and 540 μas at V = 20 mag, which is the highest accuracy that has ever been achieved. Gaia simultaneously obtains low-resolution spectrum of each star using Blue and Red Photometer (BP and RP: 330-680nm and 640-1000nm, respectively). By the nature of Gaia, it will produce enormous amount of complex data. Analyzing Gaia data is thus a challenging and huge task requiring big-data experts and dedicated computing powers as well.

I have been a Gaia postdoctoral researcher at the Max-Planck Institute for Astronomy and also a member of the Data Processing and Analysis Consortium (DPAC) since July 2012, and I have worked on analyzing the Gaia data to classify sources, estimate astrophysical parameters, and verify science product (i.e. validation). I have been CU8 Validation Manager at MPIA since 2014 as well.

The following document is a small souvenir from the Gaia ESA :)

[ Gaia Certification ]

Go to this link to see me in "Gaia People" by ESA.