PhoPS: An automated photometric pipeline for survey-era astronomy
TL;DR
(Photometry and Astrometry of Point Sources), a fully automated Python-based pipeline for photometric reduction with integrated astrometric calibration, is introduced, supporting the homogeneous reduction of stellar and moving Solar System objects, independent of telescope aperture or detector type.
Abstract
Modern astronomical surveys generate large volumes of data and provide precise astrometric and photometric reference catalogues. Efficient use of these datasets requires automated, robust, and instrument-independent reduction pipelines. We introduce (Photometry and Astrometry of Point Sources), a fully automated Python-based pipeline for photometric reduction with integrated astrometric calibration. It supports the homogeneous reduction of stellar and moving Solar System objects, independent of telescope aperture or detector type. PhoPS PhoPS performs astrometric calibration by dynamically generating local Gaia Data Release 3 (DR3) reference index files propagated to the epoch of observation, avoiding the need for a pre-installed index collection. For photometry, it adopts a field-dependent calibration strategy in which the zero point (ZP) is modelled across the detector plane using Random Sample Consensus (RANSAC)-based linear regression, thereby accounting for spatial systematics such as vignetting and detector non-uniformities. The astrometric contribution was evaluated by comparing solutions obtained with epoch-propagated and non-propagated local Gaia DR3 index files. Using 141,672 matched measurements from 840 images obtained with the 1-m TUG100 telescope, the propagated solution improves the clipped N-weighted total root-mean-square (RMS) residual from 0.284 to 0.241 a 15.0% improvement. The photometric uncertainty model was validated using 203 reference stars and 92,980 measurements. The normalised residuals are centred close to zero. However, the residual width is magnitude dependent: the limited bright-star bin (10 łeq G < 13) shows excess scatter with σ_z=2.069, the intermediate bin (13 łeq G < 16) is closest to unit variance with σ_z=1.129, and the faintest bin (16 łeq G < 18) is conservative with σ_z=0.698. Thus, the validation reveals a magnitude-dependent uncertainty behaviour rather than a globally consistent error model. PhoPS provides an open-source, lightweight, cross-platform solution for astronomical image reduction. It is suited to asteroid light-curve analysis and stellar variability studies and can diagnose systematics related to telescope tracking, focus stability, and vignetting.