تقرير
Machine Learning and Cosmology
| العنوان: | Machine Learning and Cosmology |
|---|---|
| المؤلفون: | Dvorkin, Cora, Mishra-Sharma, Siddharth, Nord, Brian, Villar, V. Ashley, Avestruz, Camille, Bechtol, Keith, Ćiprijanović, Aleksandra, Connolly, Andrew J., Garrison, Lehman H., Narayan, Gautham, Villaescusa-Navarro, Francisco |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Astrophysics High Energy Physics - Phenomenology Statistics |
| مصطلحات موضوعية: | High Energy Physics - Phenomenology, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educational pathways have emerged. Despite rapid progress, substantial potential at the intersection of cosmology and machine learning remains untapped. In this white paper, we summarize current and ongoing developments relating to the application of machine learning within cosmology and provide a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities. Comment: Contribution to Snowmass 2021. 32 pages |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2203.08056 |
| رقم الأكسشن: | edsarx.2203.08056 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |