Data-Collection for the Sloan Digital Sky Survey: a Network-Flow Heuristic

TL;DR

This paper presents a heuristic algorithm based on network flow and Lagrangian relaxation to optimize the snapshot scheduling for the Sloan Digital Sky Survey, reducing the total number of snapshots needed.

Contribution

It introduces a novel heuristic algorithm for snapshot scheduling that significantly improves efficiency over naive methods in large sky surveys.

Findings

Algorithm achieves within 5-15% of the lower bound

Outperforms uniform coverage by 10-20% in efficiency

Effective for large-scale astronomical surveys

Abstract

The goal of the Sloan Digital Sky Survey is ``to map in detail one-quarter of the entire sky, determining the positions and absolute brightnesses of more than 100 million celestial objects''. The survey will be performed by taking ``snapshots'' through a large telescope. Each snapshot can capture up to 600 objects from a small circle of the sky. This paper describes the design and implementation of the algorithm that is being used to determine the snapshots so as to minimize their number. The problem is NP-hard in general; the algorithm described is a heuristic, based on Lagriangian-relaxation and min-cost network flow. It gets within 5-15% of a naive lower bound, whereas using a ``uniform'' cover only gets within 25-35%.