Jul 2026· Astronomy & Astrophysics· 1 citation· 5 references
Physics
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.
Publicly-available photometry pipelines make astronomical data reduction accessible to non-experts, reduce the margin for human error, and enable reproducible reduction. In many cases, bespoke reduction software is written on a per-instrument basis; this results in rigid pipelines that cannot be straightforwardly applied to data from other instruments. To alleviate this problem, we present phoptic, an open source photometry pipeline written in Python. phoptic began as a dedicated pipeline for the the OPtical TIming CAMera (OPTICAM), a triple-camera system mounted on the 2.1 m telescope at the Observatorio Astronómico Nacional in San Pedro Mártir, México. However, phoptic now serves as a generic photometry pipeline with a simple interface to reduce data from other instruments. At its core, phoptic leverages the astropy Python package, and affiliated packages thereof, to provide a flexible, modern, and interoperable reduction pipeline. In particular, phoptic uses photutils for background estimation, source detection, and performing aperture photometry. Additionally, phoptic implements optimal photometry, improving the signal-to-noise ratio over aperture photometry by up to ~10 per cent. We describe phoptic’s functionality, discuss its default behaviour, and demonstrate its flexible interface by reducing data from the HiPERCAM, MEXMAN, OPTICAM, and ULTRACAM instruments. We also review the performance of phoptic, and show that it is highly scalable on multi-core CPUs.
Z. Irving, N. C. Segura, D. Altamirano et al.· RAS Techniques and Instrumen...· 1 citation
High-resolution spacecraft images provide important astrometric constraints for orbit refinement, but measurements of resolved bodies are often limited by labor-intensive control-point selection and the difficulty of achieving consistent reductions over large image archives. We present an automated shape-model-based astrometric pipeline for Phobos and apply it to Mars Express Super Resolution Channel (SRC) images. For each exposure, a synthetic image is rendered from a high-resolution 3D shape model under the nominal spacecraft-target-Sun geometry. Feature correspondences between the observed and synthetic images are established using SuperPoint and SuperGlue, followed by RANSAC filtering. The matched synthetic-image keypoints are then associated with surface points through ray-shape intersection. The geometric adjustment fixes the adopted body orientation, spacecraft state, and corrected camera pointing and estimates only two effective plane-of-sky position offsets using the exact perspective-projection model. These offsets are used to derive the center-of-figure position of Phobos. We first test the method on an image set previously analysed with a control-point approach and obtain comparable astrometric performance. We then extend the analysis to a larger SRC dataset spanning 2007-2025 and obtain 1113 successful measurements. Relative to the JPL MAR099 ephemeris, the resulting observed-minus-computed residuals have mean values of 0.186 km in $\alpha \times cos(\delta)$ and 0.053 km in $\delta$, with corresponding standard deviations of 0.609 km and 0.583 km. These results demonstrate that the proposed pipeline provides a practical approach to large-scale, homogeneous astrometric reduction of archival spacecraft images of Phobos, with potential application to other resolved bodies.
Wangxin Lai, Qing-Feng Zhang, Rui Zhang et al.· 0 citations
Accurate radio astrometry is essential for reliable cross-identification of sources across wavelengths, precision localisation of transient events, and the construction of stable all-sky reference catalogues. In this work we extend our astrometric correction framework for the Rapid ASKAP Continuum Survey (RACS) to its mid- and high-frequency epochs (RACS-Mid1 and RACS-High1), building on our previous corrections to the low-frequency surveys. Using a hierarchical crossmatching strategy with high-precision external catalogues, we remove large-scale systematic positional errors that were present in the uncorrected data and significantly reduce the residual scatter across the sky. After correction, the median positional offsets are effectively eliminated, and the 68\% confidence interval of the mean residuals of RACS source positions averaged over $~\sim1$~sq.deg. regions is reduced from $\gtrsim 0.4''$ to $\lesssim 0.18''$ over most of the survey area for both epochs. Independent validation against multiple external radio astrometric references confirms that individual corrected RACS source positions are accurate to a 1-$\sigma$ confidence level of $\sim 0.25''$ across the bulk of the sky, with slightly degraded performance within the Galactic plane. While motivated primarily by the need for improved localisation of ASKAP fast radio bursts, these corrections also benefit a wide range of science applications, including transient identification, multiwavelength host association, and studies of Galactic and extragalactic radio populations. Together with our previous work, this establishes RACS as the highest precision arcsecond-resolution all-sky astrometric reference in the southern hemisphere at decimetre wavelengths.
A. Jaini, A. Deller, Yuanming Wang et al.· 1 citation
Accurate satellite tracking requires up-to-date Two-Line Elements (TLEs), as outdated data can lead to significant positioning errors. While ground-based optical telescopes are highly accessible, generating TLEs from their data is complicated by the fundamental lack of direct range measurements. This paper presents an automated end-to-end pipeline designed to overcome this limitation by proposing a robust method to estimate the range from prior TLE. The pipeline can then generate the updated TLEs by calculating new satellite state vectors using the estimated range. The pipeline consists of star and satellite detection, astrometric calibration, and orbit determination. For star and satellite detection, the key component of the pipeline is a robust deep learning-based detection model. To achieve this, we benchmarked models such as Deformable DETR, RF-DETR, and YOLOv12 against traditional image processing methods with 3105 images in FITS (Flexible Image Transport System) format. RF-DETR yields the highest F1 score (0.93) and precision (0.97) at an 8-pixel threshold. The detected star coordinates resulting from using RF-DETR, the best-performing model, were fed into Astrometry.net for precise astrometric calibration to determine the satellite celestial coordinates. The range required for orbit determination was estimated by extracting the prior TLE and propagating it to the observation epoch via SGP4. The satellite state vectors were then calculated using TLE-constrained orbit determination approach using the estimated range, followed by an inverse SGP4 optimization to recover the mean orbital elements. The generated TLEs were validated against public TLEs from Space-Track.org. To evaluate this pipeline, updated TLEs were generated specifically for medium Earth orbit (MEO) and geostationary Earth orbit (GEO) targets. The results demonstrate that the proposed method yields high accuracy for GEO satellites, achieving a mean motion difference of 0.0030 rev/day and a 24-hour ground-track position error of 0.83 degrees. In comparison, MEO satellites achieve a mean motion difference of 0.0126 rev/day and an error of 5.27 degrees. These results suggest that the proposed pipeline provides a robust foundation for automated orbit determination with clear potential for further refinement.
Kamin Kanchanapradit, K. Noysena, R. Lipikorn· IEEE Access· 0 citations
The surge in publicly available photometric alerts from wide-field surveys requires automated tools for real-time transient classification. We present NMMA--Astro-COLIBRI, an on-demand Bayesian classification service that couples the Nuclear-physics and Multi-Messenger Astrophysics (NMMA) inference framework to the Astro-COLIBRI real-time multi-messenger platform. After the detection of an optical transient, if photometry is available, it is quality-filtered. The filtered photometry is fitted by nested sampling against a user-selected model from a library of eleven supernova templates; results are delivered to every user within minutes. Applying two or more models on the same optical transient, the service reports the corresponding log Bayes factors as a quantitative ranking of competing subtypes. We demonstrate the workflow on SN 2021ugl (ZTF21abotose), a Type IIb supernova initially mistaken for a kilonova candidate by automated real-time pipelines, comparing competing supernova and kilonova models. In an early-time configuration using only the first ~ 6 days of photometry in two bands (ZTF g and r), so ten days before spectroscopic confirmation, the empirical Type IIb template recovers the correct classification, favored over both the kilonova template and the kilonova-mimicking shock-cooling model. In the full 47-day, three-band baseline, it again achieves the highest evidence over every competing supernova and kilonova template. These results highlight the importance of a comprehensive supernova template library for kilonova discrimination in the multi-survey era.
R. W. Kiendrebeogo, Bernardo Cornejo Avila, S. Bisero et al.· 0 citations
Obtaining accurate fluxes of faint sources from the ground in near- and mid-infrared wavelengths is challenging because of the rapidly changing atmospheric absorption. A common limitation is the lack of a nearby spectro-photometric calibrator. We present the STellar Absolute Reference Spectroscopic Flux Library (starsflux), an all-sky catalogue of calibrator spectra spanning 0.3–30 μm and comprising 64, 484 stars; the target list is based on the Mid-infrared stellar Diameters and Fluxes compilation Catalogue (MDFC). starsflux combines Gaia DR3 stellar parameters with multi-band photometry from space- and ground-based surveys and synthetic NewEra PHOENIX atmosphere models. We fit each observed stellar SED with an interpolated model spectrum, an estimated diameter and estimated extinction to produce a flux-calibrated spectrum. The photometric diameter can be used for accurate calibration of interferometric observations. We validate the catalogue in three ways. First, starsflux angular radii agree closely with independent Gaia DR3 radii, with median |ΔR|/RGaia, DR3 ≃ 4.8%. Second, The integrated L-band (2.8–4.2 μm) fluxes for 12 stars in common with the Cohen infrared standard agree with the Cohen values with a mean absolute percentage difference of 4.7% ± 2.6%. Finally we compare the starsflux near-UV/visible/near-IR spectra with the Gaia DR3 BP/RP spectra. The absolute spectrophotometric fluxes agree to approximately 3%. The spectra in FITS format are available at https://home.strw.leidenuniv.nl/ gamez/ and will be submitted to VizieR.
V. G. Rosas, M. Hogerheijde, R. Boekel et al.· Monthly notices of the Royal...· 0 citations