Speaker:
Dr
Cristiano Sabiu
(University of Seoul)
- Started writing paper
- 1. Introduction
- a. 21cm - EoR -
- b. Fuzzy Dark Matter / Axion
- 2. Modeling
- a. 21cmFAST -
- i. Pots of 21cm Temperature / Snapshots / Lightcones
- ii. Nuisance (astrophysical) parameters
- b. AxionCAMB
- 3. Rionization History
- Referee Report
- Editor : "I have received the reviewer's report on your above submission to The Astrophysical Journal, and is appended below. As you will see, the reviewer thinks that your manuscript is interesting and that it will merit publication once you have addressed the issues raised in the report."
- Reviewer : This paper introduces a new way to probe the ultra light axion mass with future 21cm observations using convolution neural networks. I think this is an interesting idea, but many important aspects are lacking in this work and should be explored in order for it to be relevant for future observations.
- 1. The authors vary some of the astrophysical parameters but set the cosmology to be fixed in their simulations. They should explore varying the cosmological parameters to explore these degeneracies with the axion mass. They should also include the neutrino mass in their simulations.
- 2. The effect of the non-linear density field should be included (and Fig. 1 should be updated correspondingly).
- 3. Foreground contamination should be added (with its amplitude varied) in the simulations. In sum, I believe that this article should not be published in its current form. The presentation of the machine learning technique is interesting, but it still needs work to be used as a practical tool for cosmology.
