Kirill Grishin


I have finished my MSc at Moscow State University this summer and I plan to start my PhD in October.




Observing, calibrating and reducing near-infrared imaging mosaics
Kirill Grishin

Coadding and mosaicking of astronomical imaging datasets allow us to investigate low-surface brightness features of extended objects such as galaxies, nebulae, comets etc. However, such observations in near-infrared bands require specific dithering patterns to tackle imperfections of NIR detectors and the reduced images should have a sufficiently high calibration quality (flat fielding, background subtraction) to be co-added and combined. Here we present a complete workflow for obtaining imaging mosaics with the MMT and Magellan Infrared Spectrograph (MMIRS) currently operated at the 6.5-m MMT in Arizona and open-source tools developed additionally to an existing pipeline for preparation and data reduction of mosaic observations. We describe pre-observing actions, such as design of dithering patterns and mosaic layouts and post-processing steps to perform absolute astrometric and photometric calibration, and also generate HiPS maps to display final product in Aladin / Aladin Lite. We developed a Python module that constructs dithering patterns, which can include several nearby fields to minimize overheads needed to move telescope between the fields. Post-processing tools for reduced images were developed using dedicated Python libraries and third-party software. They allow us to identify sources on images using either Photutils library procedures or SExtractor output, perform photometric measurements, match the list of sources with the reference catalogs. In our packages we use Pan-STARRS DR2 and GAIA DR2 catalogs for astrometric calibration procedure and 2MASS and UKIDSS catalogs for photometric zeropoint determination. To minimize storage space needed for reference catalogs instead of using whole datasets locally we have implemented "on-the-fly" generation of the required catalog subsets using wrappers of VO services implemented in PyVO. We have applied these tools to the imaging data obtained with MMIRS in J, H and Ks bands for a Coma cluster including its central dense regions with large galaxies, which make "standard" sky subtraction technique used in NIR imaging inaccurate. However, the results of our data processing do not display any significant sky subtraction artifacts. Our approach can be recommended for use for imaging surveys at large telescopes, which need to cover large areas of the sky including those done with instruments, which use multi-chip mosaic detectors.

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