Katherine Blundell OBE

Abstract:

Accurate modelling of the effective point spread function (ePSF) is essential for high-precision photometry and astrometry, particularly in undersampled imaging regimes. In this work, we build on a well-established ePSF modelling framework and its commonly used open-source Python implementation and demonstrate that several simple but effective modifications to existing ePSF modelling routines can significantly improve model accuracy. We use synthetic ePSFs to generate simulated data sets of stellar images, allowing us to evaluate the accuracy of ePSF models and determine the scale of the pixel-phase errors in resulting flux and position measurements. We systematically investigate how specific modelling choices affect ePSF accuracy, and evaluate the influence of oversampling, interpolation, gridpoint estimation, smoothing, star-sample distribution and dithering on photometric precision. We apply our refined ePSF modelling routine to images from the Global Jet Watch observatories, demonstrating its improved ability to recover an accurate ePSF for real astronomical images. Our findings highlight the importance of tailoring the modelling approach to the specific characteristics of the instrument and detector, as well as to the nature of the available imaging data used to construct the ePSF model. These results provide practical guidance for optimising ePSF construction, thereby improving the reliability of photometric and astrometric measurements.

Abstract:

The observed orbits of emission-line stars may be affected by systematics owing to their broad emission lines being formed in complex and extended environments. This is problematic when orbital parameter probability distributions are estimated assuming radial-velocity data are solely comprised of Keplerian motion plus Gaussian white noise, leading to overconfident and inaccurate orbital solutions, with implications for the inferred dynamical masses and hence evolutionary models. We present a framework in which these systems can be meaningfully analysed. We synthesize benchmark data sets, each with a different and challenging noise formulation, for testing the performance of different algorithms. We make these data sets freely available with the aim of making model validation an easy and standardized practice in this field. Next, we develop an application of Gaussian processes to model the radial-velocity systematics of emission-line binaries, named marginalized GP⁠. We benchmark this algorithm, along with current standardized algorithms, on the synthetic data sets and find our marginalized GP algorithm performs significantly better than the standard algorithms for data contaminated by systematics. Finally, we apply the marginalized GP algorithm to four prototypical emission-line binaries: Eta Carinae, GG Carinae, WR 140, and WR 133. We find systematics to be present in several of these case studies; and consequently, the predicted orbital parameter distributions, and dynamical masses, are modified from those previously determined.