Efficient blind search: Optimal power of detection under computational cost constraints

TL;DR

This paper introduces a hierarchical, dynamic programming-based blind search method that significantly reduces computational costs in large hypothesis testing, demonstrated on gamma-ray pulsar searches.

Contribution

A novel hierarchical search strategy using multiple resolution levels and dynamic programming to optimize detection power under computational constraints.

Findings

Reduces computational cost by over 1000 times compared to naive search.

Nearly matches the detection power of naive search.

Effective in gamma-ray pulsar detection scenarios.

Abstract

Some astronomy projects require a blind search through a vast number of hypotheses to detect objects of interest. The number of hypotheses to test can be in the billions. A naive blind search over every single hypothesis would be far too costly computationally. We propose a hierarchical scheme for blind search, using various "resolution" levels. At lower resolution levels, "regions" of interest in the search space are singled out with a low computational cost. These regions are refined at intermediate resolution levels and only the most promising candidates are finally tested at the original fine resolution. The optimal search strategy is found by dynamic programming. We demonstrate the procedure for pulsar search from satellite gamma-ray observations and show that the power of the naive blind search can almost be matched with the hierarchical scheme while reducing the computational burden by more than three orders of magnitude.