Optimal linear estimation of binary star parameters

Daniel Burke; Nicholas Devaney; Szymon Gladysz; Harrisson H. Barrett; Meredith K. Whitaker; Luca Caucci

doi:10.1117/12.788973

10 July 2008 Optimal linear estimation of binary star parameters

Daniel Burke, Nicholas Devaney, Szymon Gladysz, Harrisson H. Barrett, Meredith K. Whitaker, Luca Caucci

Proceedings Volume 7015, Adaptive Optics Systems; 70152J (2008) https://doi.org/10.1117/12.788973
Event: SPIE Astronomical Telescopes + Instrumentation, 2008, Marseille, France

Abstract

We propose a new post-processing technique for the detection of faint companions and the estimation of their parameters from adaptive optics (AO) observations. We apply the optimal linear detector, which is the Hotelling observer, to perform detection, astrometry and photometry on real and simulated data. The real data was obtained from the AO system on the 3m Lick telescope¹. The Hotelling detector, which is a prewhitening matched filter, calculates the Hotelling test statistic which is then compared to a threshold. If the test statistic is greater than the threshold the algorithm decides that a companion is present. This decision is the main task performed by the Hotelling observer. After a detection is made the location and intensity of the companion which maximise this test statistic are taken as the estimated values. We compare the Hotelling approach with current detection algorithms widely used in astronomy. We discuss the use of the estimation receiver operating characteristic (EROC) curve in quantifying the performance of the algorithm with no prior estimate of the companion's location or intensity. The robustness of this technique to errors in point spread function (PSF) estimation is also investigated.

Citation Download Citation

Daniel Burke, Nicholas Devaney, Szymon Gladysz, Harrisson H. Barrett, Meredith K. Whitaker, and Luca Caucci "Optimal linear estimation of binary star parameters", Proc. SPIE 7015, Adaptive Optics Systems, 70152J (10 July 2008); https://doi.org/10.1117/12.788973

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