Galaxy Redshifts from Discrete Optimization of Correlation Functions
Abstract
We propose a new method of constraining the redshifts of individual extragalactic sources based on celestial coordinates and their ensemble statistics. Techniques from integer linear programming are utilized to optimize simultaneously for the angular two-point cross- and autocorrelation functions. Our novel formalism introduced here not only transforms the otherwise hopelessly expensive, brute-force combinatorial search into a linear system with integer constraints but also is readily implementable in off-the-shelf solvers. We adopt Gurobi, a commercial optimization solver, and use Python to build the cost function dynamically. The preliminary results on simulated data show potential for future applications to sky surveys by complementing and enhancing photometric redshift estimators. Our approach is the first application of integer linear programming to astronomical analysis.
Cite
@article{arxiv.1604.00652,
title = {Galaxy Redshifts from Discrete Optimization of Correlation Functions},
author = {Benjamin C. G. Lee and Tamás Budavári and Amitabh Basu and Mubdi Rahman},
journal= {arXiv preprint arXiv:1604.00652},
year = {2016}
}
Comments
10 pages with 3 figures, accepted for publication in The Astronomical Journal on 08/04/16