1. Revised LOFAR upper limits on the 21-cm signal power spectrum at z ≈ 9.1 using machine learning and gaussian process regression.
- Author
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Acharya, Anshuman, Mertens, Florent, Ciardi, Benedetta, Ghara, Raghunath, Koopmans, Léon V E, and Zaroubi, Saleem
- Subjects
KRIGING ,POWER spectra ,MACHINE learning ,MIDDLE Ages ,STAR observations - Abstract
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 h (|${\approx} 10$| 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 |$\Delta ^2_{21} \ \lt\ (80)^2~\rm mK^2$| 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 |$\Delta ^2_{21} \ \lt\ (25)^2~\rm mK^2$| 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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