NIRPipe: A Modular, Instrument-Agnostic Pipeline for the Reduction and Photometric Calibration of Dithered Near-Infrared Imaging

Registration ID: IJNRD_328922

Published ID: IJNRD2610150

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Radhika Gokani, Vishal Joshi

astronomical data reduction; image registration; astrometric calibration; software pipeline

Near-infrared (NIR) imaging in the J, H and K bands is almost universally acquired as a dithered sequence, because the sky background at these wavelengths is bright, spatially structured and variable on timescales of minutes, and so cannot be removed with a static calibration frame the way an optical bias or flat can. Reducing such data — grouping frames by telescope pointing, rejecting corrupted exposures, deriving and subtracting a time-appropriate sky model, solving the astrometry, and calibrating instrumental photometry against a reference catalogue — is a well-understood but fragmented process: most existing tools are written for a single camera, a single telescope, or a single survey, and porting them to new hardware is largely a manual exercise. This paper describes NIRPipe, an open, instrument-agnostic implementation of the full reduction-to-calibrated-catalogue chain, in which every piece of camera-specific knowledge — header keyword names, pixel scale, detector gain and read noise — is isolated in a small declarative configuration file rather than embedded in code. We describe the nine constituent processing stages, including two design choices we treat as central to the system's robustness: dither-position grouping is performed purely from image-domain registration rather than from telescope pointing metadata, and the astrometric solution is obtained by matching the pattern of detected stars against a reference catalogue rather than by refining a header-supplied pointing. We validate the complete pipeline against two simulated instruments with deliberately different plate scales, detector properties and header conventions, using datasets with fully known ground truth, and report quantitative results for frame-quality classification (96.7–100% agreement with injected defects), astrometric residuals (0.002–0.03 arcsec), and photometric calibration accuracy (0.01–0.05 mag scatter against the input catalogue). We close with a candid account of the system's current limitations — in particular a sensitivity of star-pattern matching to how the candidate reference catalogue is spatially filtered before matching — and the engineering lessons that shaped the final design.

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Radhika Gokani & Vishal Joshi (October-2026). NIRPipe: A Modular, Instrument-Agnostic Pipeline for the Reduction and Photometric Calibration of Dithered Near-Infrared Imaging. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(10), b457-b468. https://ijnrd.org/papers/IJNRD2610150.pdf

Volume 11 Issue 10, October-2026

Pages : b457-b468

Paper Reg. ID: IJNRD_328922

Published Paper Id: IJNRD2610150

Research Area: Other area not in list

Author Type: Indian Author

Country: Ahmedabad, Gujarat, India

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