An accurate centroid algorithm for PSF reconstruction

TL;DR

This paper introduces a Fourier space Phase Fitting (FPF) method for more accurate centroid estimation in PSF reconstruction, outperforming traditional polynomial fitting especially in anisotropic conditions.

Contribution

The paper presents a novel FPF centroiding method that improves accuracy in PSF reconstruction by effectively handling anisotropy and noise, validated through simulations and real data.

Findings

FPF reduces centroid scatter by half compared to polynomial fitting.

FPF performs consistently better across different SNR levels.

Anisotropy significantly affects centroid accuracy in PSF reconstruction.

Abstract

In this work, we present a novel centroiding method based on Fourier space Phase Fitting(FPF) for Point Spread Function(PSF) reconstruction. We generate two sets of simulations to test our method. The first set is generated by GalSim with elliptical Moffat profile and strong anisotropy which shifts the center of the PSF. The second set of simulation is drawn from CFHT i band stellar imaging data. We find non-negligible anisotropy from CFHT stellar images, which leads to $\sim$0.08 scatter in unit of pixels using polynomial fitting method Vakili and Hogg (2016). And we apply FPF method to estimate the centroid in real space, this scatter reduces to $\sim$0.04 in SNR=200 CFHT like sample. In low SNR (50 and 100) CFHT like samples, the background noise dominates the shifting of the centroid, therefore the scatter estimated from different methods are similar. We compare polynomial fitting and FPF using GalSim simulation with optical anisotropy. We find that in all SNR$\sim$50, 100 and 200) samples, FPF performs better than polynomial fitting by a factor of $\sim$3. In general, we suggest that in real observations there are anisotropy which shift the centroid, and FPF method is a better way to accurately locate it.