Quadratic shift-and-stack for Ground-Based Optical Detection of Faint Cislunar Objects

TL;DR

This paper introduces a quadratic shift-and-stack method that accounts for nonlinear motion in ground-based optical detection of faint cislunar objects, significantly improving detection sensitivity over traditional linear methods.

Contribution

The paper develops a theoretical criterion for maximum linear stacking duration and proposes a quadratic method to correct for angular acceleration, enhancing detection capabilities.

Findings

QSS improves SNR by up to 1 magnitude over linear stacking.

QSS maintains SNR improvement over longer integrations, up to 46 minutes.

QSS outperforms linear stacking by 31% in peak SNR for observational data.

Abstract

Detecting faint objects in cislunar space using ground-based optical telescopes is difficult because of their low brightness, strong lunar background, and complex, nonlinear apparent motion. Traditional shift-and-stack techniques based on linear motion assumption suffer signal trailing loss due to significant nonlinear motion during long integrations, thus producing a degraded signal-to-noise ratio (SNR). In this paper, we first derive a theoretical criterion based on the point spread function to determine the maximum applicable integration time for linear-motion stacking. We then propose a quadratic shift-and-stack (QSS) method to correct for the first-order nonlinear motion, namely the angular acceleration of cislunar targets. Simulations of typical cislunar orbits verify this theoretical criterion and show that the QSS method significantly improves SNR from stacking and can enhance the detection limit by up to 1 stellar magnitude compared with the linear-motion stacking method. Furthermore, tests using observational data of the cislunar object Tiandu-1 confirm that while linear stacking degrades after a 29-minute integration due to trajectory curvature, the QSS method achieves continuous SNR improvement over a 46-minute integration, outperforming the peak SNR of the linear method by 31%.