Reference : prose: A Python framework for modular astronomical images processing
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Space science, astronomy & astrophysics
http://hdl.handle.net/2268/264929
prose: A Python framework for modular astronomical images processing
English
Garcia, Lionel mailto [Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Exotic >]
Timmermans, Mathilde mailto [Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Exotic >]
Pozuelos, Francisco J. [Astrobiology Research Unit, University of Liége, Allée du 6 Août 19C, B-4000 Liège, Belgium; STAR Institute, University of Liége, Allée du 6 Août 19C, B-4000 Liège, Belgium]
Ducrot, Elsa [Commissariat à l'Energie Atomique (Saclay) - CEA > > > >]
Gillon, Michaël mailto [Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Exotic >]
Delrez, Laetitia mailto [Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Exotic >]
Wells, Robert D. [Center for Space and Habitability, Gesellsschaftstrasse 6, 3012 Bern, Switzerland]
Jehin, Emmanuel mailto [Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Origines Cosmologiques et Astrophysiques (OrCa) >]
1-Oct-2021
Monthly Notices of the Royal Astronomical Society
Oxford University Press
Yes
International
0035-8711
1365-2966
Oxford
United Kingdom
[en] data analysis ; photometry ; astronomical instrumentation ; planetary systems ; Astrophysics - Instrumentation and Methods for Astrophysics ; Astrophysics - Earth and Planetary Astrophysics
[en] To reduce and analyse astronomical images, astronomers can rely on a wide range of libraries providing low-level implementations of legacy algorithms. However, combining these routines into robust and functional pipelines requires a major effort which often ends up in instrument-specific and poorly maintainable tools, yielding products that suffer from a low-level of reproducibility and portability. In this context, we present prose, a Python framework to build modular and maintainable image processing pipelines. Built for astronomy, it is instrument-agnostic and allows the construction of pipelines using a wide range of building blocks, pre-implemented or user-defined. With this architecture, our package provides basic tools to deal with common tasks such as automatic reduction and photometric extraction. To demonstrate its potential, we use its default photometric pipeline to process 26 TESS candidates follow-up observations and compare their products to the ones obtained with AstroImageJ, the reference software for such endeavors. We show that prose produces light curves with lower white and red noise while requiring less user interactions and offering richer functionalities for reporting.
http://hdl.handle.net/2268/264929
https://ui.adsabs.harvard.edu/abs/2021MNRAS.tmp.2859G

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