Jennings, WD;
Watkinson, CA;
Abdalla, FB;
(2020)
Analysing the Epoch of Reionization with three-point correlation functions and machine learning techniques.
Monthly Notices of the Royal Astronomical Society
, 498
(3)
pp. 4518-4532.
10.1093/mnras/staa2598.
Preview |
Text
staa2598.pdf - Published Version Download (2MB) | Preview |
Abstract
Three-point and high-order clustering statistics of the high-redshift 21 cm signal contain valuable information about the Epoch of Reionization (EoR). We present 3PCF-FAST, an optimized code for estimating the three-point correlation function (3PCF) of 3D pixelized data such as the outputs from numerical and seminumerical simulations. After testing 3PCF-FAST on data with known analytical 3PCF, we use machine learning techniques to recover the mean bubble size and global ionization fraction from correlations in the outputs of the publicly available 21CMFAST code. We assume that foregrounds have been perfectly removed and negligible instrumental noise. Using ionization fraction data, our best multilayer perceptron (MLP) model recovers the mean bubble size with a median prediction error of around 10 per cent, or from the 21 cm differential brightness temperature with median prediction error of around 14 per cent. A further two MLP models recover the global ionization fraction with median prediction errors of around 4 per cent (using ionization fraction data) or around 16 per cent (using brightness temperature). Our results indicate that clustering in both the ionization fraction field and the brightness temperature field encode useful information about the progress of the EoR in a complementary way to other summary statistics. Using clustering would be particularly useful in regimes where high signal-to-noise ratio prevents direct measurement of bubble size statistics. We compare the quality of MLP models using the power spectrum, and find that using the 3PCF outperforms the power spectrum at predicting both global ionization fraction and mean bubble size.
| Type: | Article |
|---|---|
| Title: | Analysing the Epoch of Reionization with three-point correlation functions and machine learning techniques |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/staa2598 |
| Publisher version: | https://doi.org/10.1093/mnras/staa2598 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | methods: statistical, dark ages, reionization, first stars |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10117041 |
Archive Staff Only
 |
View Item |
