ColdPress: Efficient Quantile-Based Compression of Photometric Redshift PDFs

TL;DR

ColdPress is a Python tool that efficiently compresses photometric redshift PDFs using quantiles, achieving high accuracy with significantly reduced computational cost compared to previous methods.

Contribution

It introduces a novel quantile-based compression method for photometric redshift PDFs that is faster and equally accurate as existing sparse-basis approaches.

Findings

Achieves comparable reconstruction accuracy to sparse-basis methods.

Reduces computational cost by approximately 7000 times.

Provides a practical, open-source implementation for real-world data.

Abstract

ColdPress is a Python module that compresses photometric redshift probability distribution functions (PDFs) by encoding quantiles of their cumulative distribution. For a fixed packet size (the default is 80 bytes per PDF), ColdPress attains a reconstruction accuracy comparable to the sparse-basis representation method implemented in the pdf_storage module of Carrasco-Kind & Brunner (2014), yet reduces the computational cost by a factor of ~7000. I describe the implementation and quantify its compression speed and reconstruction accuracy in comparison to pdf_storage for real-life PDFs from two different photometric redshift codes. ColdPress is free software, available at https://github.com/ahc-photoz/coldpress-project.