The TAOS Project: Statistical Analysis of Multi-Telescope Time Series Data

TL;DR

The TAOS project employs a novel statistical method to analyze multi-telescope time series data, effectively detecting rare occultation events caused by small Kuiper Belt Objects with high confidence.

Contribution

This paper introduces a new statistical analysis technique specifically designed for multi-telescope data to identify rare occultation events with robust false positive control.

Findings

Effective detection of small KBO occultations in large data sets

High false positive rejection rate achieved

Method successfully applied to TAOS data set

Abstract

The Taiwanese-American Occultation Survey (TAOS) monitors fields of up to ~1000 stars at 5 Hz simultaneously with four small telescopes to detect occultation events from small (~1 km) Kuiper Belt Objects (KBOs). The survey presents a number of challenges, in particular the fact that the occultation events we are searching for are extremely rare and are typically manifested as slight flux drops for only one or two consecutive time series measurements. We have developed a statistical analysis technique to search the multi-telescope data set for simultaneous flux drops which provides a robust false positive rejection and calculation of event significance. In this paper, we describe in detail this statistical technique and its application to the TAOS data set.