Bibliographic Details
| Title: |
Revised LOFAR upper limits on the 21-cm signal power spectrum at $\mathbf{z\approx9.1}$ using Machine Learning and Gaussian Process Regression |
| Authors: |
Acharya, Anshuman, Mertens, Florent, Ciardi, Benedetta, Ghara, Raghunath, Koopmans, Léon V. E., Zaroubi, Saleem |
| Publication Year: |
2024 |
| Collection: |
Astrophysics |
| Subject Terms: |
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies |
| More Details: |
The use of Gaussian Process Regression (GPR) for foregrounds mitigation in data collected by the LOw-Frequency ARray (LOFAR) to measure the high-redshift 21-cm signal power spectrum has been shown to have issues of signal loss when the 21-cm signal covariance is misestimated. To address this problem, we have recently introduced covariance kernels obtained by using a Machine Learning based Variational Auto-Encoder (VAE) algorithm in combination with simulations of the 21-cm signal. In this work, we apply this framework to 141 hours ($\approx 10$ nights) of LOFAR data at $z \approx 9.1$, and report revised upper limits of the 21-cm signal power spectrum. Overall, we agree with past results reporting a 2-$\sigma$ upper limit of $\Delta^2_{21} < (80)^2~\rm mK^2$ at $k = 0.075~h~\rm Mpc^{-1}$. Further, the VAE-based kernel has a smaller correlation with the systematic excess noise, and the overall GPR-based approach is shown to be a good model for the data. Assuming an accurate bias correction for the excess noise, we report a 2-$\sigma$ upper limit of $\Delta^2_{21} < (25)^2~\rm mK^2$ at $k = 0.075~h~\rm Mpc^{-1}$. However, we still caution to take the more conservative approach to jointly report the upper limits of the excess noise and the 21-cm signal components.
Comment: 5 pages, 3 figures, 2 tables. Accepted for publication in the Monthly Notices of the Royal Astronomical Society (MNRAS) Letters |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2408.10051 |
| Accession Number: |
edsarx.2408.10051 |
| Database: |
arXiv |
