Deep learning approach for identification of HII regions during reionization in 21-cm observations -- III. image recovery
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915469073580032 |
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| author | Bianco, Michele Giri, Sambit. K. Sharma, Rohit Chen, Tianyue Krishna, Shreyam Parth Finlay, Chris Nistane, Viraj Denzel, Philipp De Santis, Massimo Ghorbel, Hatem |
| author_facet | Bianco, Michele Giri, Sambit. K. Sharma, Rohit Chen, Tianyue Krishna, Shreyam Parth Finlay, Chris Nistane, Viraj Denzel, Philipp De Santis, Massimo Ghorbel, Hatem |
| contents | The low-frequency component of the upcoming Square Kilometre Array Observatory (SKA-Low) will be sensitive enough to construct 3D tomographic images of the 21-cm signal distribution during reionisation. However, foreground contamination poses challenges for detecting this signal, and image recovery will heavily rely on effective mitigation methods. We introduce \texttt{SERENEt}, a deep-learning framework designed to recover the 21-cm signal from SKA-Low's foreground-contaminated observations, enabling the detection of ionised (HII) and neutral (HI) regions during reionisation. \texttt{SERENEt} can recover the signal distribution with an average accuracy of 75 per cent at the early stages ($\overline{x}_\mathrm{HI}\simeq0.9$) and up to 90 per cent at the late stages of reionisation ($\overline{x}_\mathrm{HI}\simeq0.1$). Conversely, HI region detection starts at 92 per cent accuracy, decreasing to 73 per cent as reionisation progresses. Beyond improving image recovery, \texttt{SERENEt} provides cylindrical power spectra with an average accuracy exceeding 93 per cent throughout the reionisation period. We tested \texttt{SERENEt} on a 10-degree field-of-view simulation, consistently achieving better and more stable results when prior maps were provided. Notably, including prior information about HII region locations improved 21-cm signal recovery by approximately 10 per cent. This capability was demonstrated by supplying \texttt{SERENEt} with ionising source distribution measurements, showing that high-redshift galaxy surveys of similar observation fields can optimise foreground mitigation and enhance 21-cm image construction. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_16814 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep learning approach for identification of HII regions during reionization in 21-cm observations -- III. image recovery Bianco, Michele Giri, Sambit. K. Sharma, Rohit Chen, Tianyue Krishna, Shreyam Parth Finlay, Chris Nistane, Viraj Denzel, Philipp De Santis, Massimo Ghorbel, Hatem Cosmology and Nongalactic Astrophysics The low-frequency component of the upcoming Square Kilometre Array Observatory (SKA-Low) will be sensitive enough to construct 3D tomographic images of the 21-cm signal distribution during reionisation. However, foreground contamination poses challenges for detecting this signal, and image recovery will heavily rely on effective mitigation methods. We introduce \texttt{SERENEt}, a deep-learning framework designed to recover the 21-cm signal from SKA-Low's foreground-contaminated observations, enabling the detection of ionised (HII) and neutral (HI) regions during reionisation. \texttt{SERENEt} can recover the signal distribution with an average accuracy of 75 per cent at the early stages ($\overline{x}_\mathrm{HI}\simeq0.9$) and up to 90 per cent at the late stages of reionisation ($\overline{x}_\mathrm{HI}\simeq0.1$). Conversely, HI region detection starts at 92 per cent accuracy, decreasing to 73 per cent as reionisation progresses. Beyond improving image recovery, \texttt{SERENEt} provides cylindrical power spectra with an average accuracy exceeding 93 per cent throughout the reionisation period. We tested \texttt{SERENEt} on a 10-degree field-of-view simulation, consistently achieving better and more stable results when prior maps were provided. Notably, including prior information about HII region locations improved 21-cm signal recovery by approximately 10 per cent. This capability was demonstrated by supplying \texttt{SERENEt} with ionising source distribution measurements, showing that high-redshift galaxy surveys of similar observation fields can optimise foreground mitigation and enhance 21-cm image construction. |
| title | Deep learning approach for identification of HII regions during reionization in 21-cm observations -- III. image recovery |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2408.16814 |