Reference-less detection, astrometry, and photometry of faint companions with adaptive optics

TL;DR

This paper introduces a comprehensive, self-calibrating framework for detecting and measuring faint companions in adaptive optics images, outperforming traditional PSF fitting especially for very faint or close-in objects.

Contribution

The authors develop new algorithms that exploit statistical differences in intensity to improve detection, astrometry, and photometry without needing calibration stars.

Findings

Effective detection limits established for Lick Observatory data

Algorithms provide accurate measurements for faint, close companions

Self-calibrating methods improve observational efficiency

Abstract

We propose a complete framework for the detection, astrometry, and photometry of faint companions from a sequence of adaptive optics corrected short exposures. The algorithms exploit the difference in statistics between the on-axis and off-axis intensity. Using moderate-Strehl ratio data obtained with the natural guide star adaptive optics system on the Lick Observatory's 3-m Shane Telescope, we compare these methods to the standard approach of PSF fitting. We give detection limits for the Lick system, as well as a first guide to expected accuracy of differential photometry and astrometry with the new techniques. The proposed approach to detection offers a new way of determining dynamic range, while the new algorithms for differential photometry and astrometry yield accurate results for very faint and close-in companions where PSF fitting fails. All three proposed algorithms are self-calibrating, i.e. they do not require observation of a calibration star thus improving the observing efficiency.