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26 April 2024 |
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LRP2020: Probing Diverse Phenomena through Data-Intensive Astronomy | | Mubdi Rahman
; Dustin Lang
; Renée Hložek
; Jo Bovy
; Laurence Perreault-Levasseur
; | | Date: |
4 Oct 2019 | | Abstract: | The era of data-intensive astronomy is being ushered in with the increasing
size and complexity of observational data across wavelength and time domains,
the development of algorithms to extract information from this complexity, and
the computational power to apply these algorithms to the growing repositories
of data. Data-intensive approaches are pushing the boundaries of nearly all
fields of astronomy, from exoplanet science to cosmology, and they are becoming
a critical modality for how we understand the universe. The success of these
approaches range from the discovery of rare or unexpected phenomena, to
characterizing processes that are now accessible with precision astrophysics
and a deep statistical understanding of the datasets, to developing algorithms
that maximize the science that can be extracted from any set of observations.
In this white paper, we propose a number of initiatives to maximize Canada’s
ability to compete in this data-intensive era. We propose joining international
collaborations and leveraging Canadian facilities for legacy data potential. We
propose continuing to build a more agile computing infrastructure that’s
responsive to the needs of tackling larger and more complex data, as well as
enabling quick prototyping and scaling of algorithms. We recognize that
developing the fundamental skills of the field will be critical for Canadian
astronomers, and discuss avenues through with the appropriate computational and
statistical training could occur. Finally, we note that the transition to
data-intensive techniques is not limited to astronomy, and we should coordinate
with other disciplines to develop and make use of best practises in methods,
infrastructure, and education. | | Source: | arXiv, 1910.2124 | | Services: | Forum | Review | PDF | Favorites | |
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