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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2410.11954 |
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| _version_ | 1866916440500600832 |
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| author | Turner, David J. Pilling, Jessica E. Donahue, Megan Giles, Paul A. Romer, Kathy Gupta, Agrim Wallage, Toby Wang, Ray |
| author_facet | Turner, David J. Pilling, Jessica E. Donahue, Megan Giles, Paul A. Romer, Kathy Gupta, Agrim Wallage, Toby Wang, Ray |
| contents | 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 also made easier; DAXA archives can be version controlled and updated if new data become available. Once relevant observations are identified, the raw data can be downloaded (and optionally processed) through DAXA, or pre-processed event lists, images, and exposure maps can be downloaded if they are available. X-ray observations are perfectly suited to serendipitous discoveries and archival analyses, and with a decade-long `X-ray desert' potentially on the horizon archival data will take on even greater importance; enhanced access to those archives will be vital to the continuation of X-ray astronomy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11954 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DAXA: Traversing the X-ray desert by Democratising Archival X-ray Astronomy Turner, David J. Pilling, Jessica E. Donahue, Megan Giles, Paul A. Romer, Kathy Gupta, Agrim Wallage, Toby Wang, Ray Instrumentation and Methods for Astrophysics 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 also made easier; DAXA archives can be version controlled and updated if new data become available. Once relevant observations are identified, the raw data can be downloaded (and optionally processed) through DAXA, or pre-processed event lists, images, and exposure maps can be downloaded if they are available. X-ray observations are perfectly suited to serendipitous discoveries and archival analyses, and with a decade-long `X-ray desert' potentially on the horizon archival data will take on even greater importance; enhanced access to those archives will be vital to the continuation of X-ray astronomy. |
| title | DAXA: Traversing the X-ray desert by Democratising Archival X-ray Astronomy |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2410.11954 |