# Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion

Source: [Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion](<https://www.sota2.com/research/paper/68fa358c1e209131ce8a2b17>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-24T21:19:27.476Z

Authors: Tamas Budavari, Alexander S. Szalay, Thomas J. Loredo

Published: 2016-11-10T03:16:38.000Z

## Abstract

Observational astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection with multi-epoch data, and describes an incremental strategy for separating real objects from artifacts in ongoing surveys, in situations where the single-epoch data are summaries of the full image data, such as single-epoch catalogs of flux and direction estimates for candidate sources. The basic idea is to produce low-threshold single-epoch catalogs, and use a probabilistic approach to accumulate catalog information across epochs; this is in contrast to more conventional strategies based on co-added or stacked image data across all epochs. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting there is no object, or one object, in a small image patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object (noise) hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a simplified Gaussian noise setting, comparing results based on single exposures and stacked exposures to results based on a series of single-epoch catalog summaries. Computing the detection probability based on catalog data amounts to generalized cross-matching: it is the product of a factor accounting for matching of the estimated fluxes of candidate sources, and a factor accounting for matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalog information can detect sources with only modest sacrifice in sensitivity and selectivity compared to stacking.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1611.03171>)

[PDF](<https://arxiv.org/pdf/1611.03171.pdf>)