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Computational astrophysics, data science and AI/ML in astronomy: A perspective from Indian community

Authors: Prateek Sharma,Bhargav Vaidya,Yogesh Wadadekar,Jasjeet Bagla,Piyali Chatterjee,Shravan Hanasoge,Prayush Kumar,Dipanjan Mukherjee,Ninan Sajeeth Philip,Nishant Singh
Journal: Journal of Astrophysics and Astronomy
Publisher: Springer Science and Business Media LLC
Publish date: 2025-5-17
ISSN: 0973-7758 DOI: 10.1007/s12036-025-10049-9
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You say your stats on HPC/AI usage in Indian astronomy come from 29 respondents.
That’s a tiny sample size for a national community.
How can you generalize trends for the entire Indian A&A field from fewer than 30 people?
Did you check for sampling bias — e.g., mostly from a few institutes, or only those already using HPC?

You claim that access to advanced HPC leads to higher citation impact, citing Wang et al. 2018.
But that study was about XSEDE in the US.
Do you have any India-specific data to back this up for NSM or other national clusters?
Or are you just assuming the US model applies directly here?

You mention that global astrophysics codes are moving to GPU versions, and Indian researchers use them.
But in Section 3.1, most work listed seems CPU-based.
How many of the “community codes” you mention (PLUTO, Athena, etc.) are actually running efficiently on Indian GPU clusters?
Or is this a future wish, not current reality?

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