Differentiating warm dark matter models through 21-cm line intensity mapping: A convolutional neural network approach-Reference-Cited by-同舟云学术

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Differentiating warm dark matter models through 21-cm line intensity mapping: A convolutional neural network approach

Published:2024-07-19 Issue:2 Volume:110 Page:
ISSN:2470-0010
Container-title:Physical Review D
language:en
Short-container-title:Phys. Rev. D

Author:

Murakami Koya¹^ORCID,Kadota Kenji²³^ORCID,Nishizawa Atsushi J.⁴¹¹^ORCID,Nagamine Kentaro⁵⁵⁶⁷^ORCID,Shimizu Ikkoh⁸

Affiliation:

1. Nagoya University

2. University of Chinese Academy of Sciences

3. International Centre for Theoretical Physics Asia-Pacific (ICTP-AP)

4. Gifu Shotoku Gakuen University

5. Osaka University

6. Kavli IPMU (WPI)

7. University of Nevada, Las Vegas

8. Shikoku Gakuin University

Funder

Nagoya University

Japan Science and Technology Agency

National Natural Science Foundation of China

Ministry of Education, Culture, Sports, Science and Technology

Japan Society for the Promotion of Science

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevD.110.023526/fulltext

Reference40 articles.

1. Warm dark matter as a solution to the small scale crisis: New constraints from high redshift Lyman-αforest data

2. How to constrain warm dark matter with the Lyman-α forest

3. The Lyman-α forest as a diagnostic of the nature of the dark matter

4. New constraints on warm dark matter from the Lyman- α forest power spectrum

5. The Murchison Widefield Array: The Square Kilometre Array Precursor at Low Radio Frequencies

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