DAXA: Traversing the X-ray desert by Democratising Archival X-ray Astronomy
David J. Turner, Jessica E. Pilling, Megan Donahue, Paul A. Giles,, Kathy Romer, Agrim Gupta, Toby Wallage, Ray Wang

TL;DR
DAXA is an open-source Python tool that simplifies access to and processing of archival X-ray data from multiple telescopes, enabling broader use and multi-mission studies in X-ray astronomy.
Contribution
It introduces a unified, user-friendly Python interface for accessing and processing archival X-ray data from various telescopes, facilitating non-specialists and experts alike.
Findings
Enables easier access to X-ray archives for diverse users.
Supports creation of multi-mission datasets for large sample studies.
Simplifies dataset management and updates.
Abstract
We introduce a new, open-source, Python module for the acquisition and processing of archival data from many X-ray telescopes - Democratising Archival X-ray Astronomy (hereafter referred to as DAXA). Our software is built to increase access to, and use of, large archives of X-ray astronomy data; providing a unified, easy-to-use, Python interface to the disparate archives and processing tools. We provide this interface for the majority of X-ray telescopes launched within the last 30 years. This module enables much greater access to X-ray data for non-specialists, while preserving low-level control of processing for X-ray experts. It is useful for identifying relevant observations of a single object of interest but it excels at creating multi-mission datasets for serendipitous or targeted studies of large samples of X-ray emitting objects. The management and organization of datasets is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsResearch Data Management Practices
