View in EDS

Karabo: A versatile SKA observation simulation framework.

Saved in:
Bibliographic Details
Title: Karabo: A versatile SKA observation simulation framework.
Authors: Sharma, Rohit1,2 (AUTHOR) rsharma@iitk.ac.in, Felix, Simon2 (AUTHOR), Valle, Luis Fernando Machado Poletti3 (AUTHOR), Timmel, Vincenzo2 (AUTHOR), Gehrig, Lukas2 (AUTHOR), Wassmer, Andreas2 (AUTHOR), Studer, Jennifer3 (AUTHOR), Hitz, Pascal3 (AUTHOR), Schramka, Filip2 (AUTHOR), Bianco, Michele3,4 (AUTHOR), Crichton, Devin3 (AUTHOR), Spinelli, Marta5 (AUTHOR), Csillaghy, André2 (AUTHOR), Kögel, Stefan2 (AUTHOR), Réfrégier, Alexandre3 (AUTHOR)
Source: Astronomy & Computing. Jan2026, Vol. 54, pN.PAG-N.PAG. 1p.
Subject Terms: Radio astronomy, Simulation software, Space environment, Python programming language, Scientific observation, Software frameworks, Acquisition of data
Abstract: Karabo is a versatile Python-based software framework simplifying research with radio astronomy data. It bundles existing software packages into a coherent whole to improve the ease of use of its components. Karabo includes useful abstractions, like strategies to scale and parallelize typical workloads or science-specific Python modules. The framework includes functionality to access datasets and mock observations to study the Square Kilometre Array (SKA) instruments and their expected accuracy. SKA will address problems in a wide range of fields of astronomy. We demonstrate the application of Karabo relevant to some of the SKA science cases from HI intensity mapping, simulation of the radio surveys, radio source detection, the epoch of re-ionization and heliophysics. We discuss the capabilities, scalabilities and challenges of simulating large radio datasets in the context of SKA. [ABSTRACT FROM AUTHOR]
Database: Supplemental Index
Description
Abstract:Karabo is a versatile Python-based software framework simplifying research with radio astronomy data. It bundles existing software packages into a coherent whole to improve the ease of use of its components. Karabo includes useful abstractions, like strategies to scale and parallelize typical workloads or science-specific Python modules. The framework includes functionality to access datasets and mock observations to study the Square Kilometre Array (SKA) instruments and their expected accuracy. SKA will address problems in a wide range of fields of astronomy. We demonstrate the application of Karabo relevant to some of the SKA science cases from HI intensity mapping, simulation of the radio surveys, radio source detection, the epoch of re-ionization and heliophysics. We discuss the capabilities, scalabilities and challenges of simulating large radio datasets in the context of SKA. [ABSTRACT FROM AUTHOR]
ISSN:22131337
DOI:10.1016/j.ascom.2025.101004