The Role of Machine Learning in the Next Decade of Cosmology
Роль машинного обучения в следующем десятилетии космологии
2019-02-26
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astronomycosmologydata-driven cosmological discoveryinterdisciplinary researchmachine learning
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Abstract (AI)
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results. ML could transform our field, but this transformation will require the astronomy community to both foster and promote interdisciplinary research endeavors.
Key Findings
1
Machine learning has substantially improved cosmologists’ ability to interpret astronomical data in recent years.
2
Realizing ML’s transformative potential in cosmology requires the astronomy community to foster and promote interdisciplinary research.
3
The coming decade is expected to create new opportunities for data-driven discoveries in cosmology.
4
Wider adoption of machine-learning methods will introduce challenges in methodology and in interpreting their results.
Research Object
machine learning applications in cosmology
Research Subject
the opportunities, challenges, and transformative role of machine learning for interpreting cosmological data and enabling data-driven discovery
Publication Details
Publication Date
2019-02-26
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