{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-galaxy-properties-from-21-cm","title":"Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination","arxiv_id":"2107.00018","date":"2021-06-30","proceeding":null,"authors":["David Prelogović","Andrei Mesinger","Steven Murray","Giuseppe Fiameni","Nicolas Gillet"],"abstract":"Imaging the cosmic 21 cm signal will map out the first billion years of our Universe. The resulting 3D lightcone (LC) will encode the properties of the unseen first galaxies and physical cosmology. Here, we build on previous work using neural networks (NNs) to infer astrophysical parameters directly from 21 cm LC images. We introduce recurrent neural networks (RNNs), capable of efficiently characterizing the evolution along the redshift axis of 21 cm LC images. Using a large database of simulated cosmic 21 cm LCs, we compare the relative performance in parameter estimation of different network architectures. These including two types of RNNs, which differ in their complexity, as well as a more traditional convolutional neural network (CNN). For the ideal case of no instrumental effects, our simplest and easiest to train RNN performs the best, with a mean squared parameter estimation error (MSE) that is lower by a factor of $\\ge 2$ compared with the other architectures studied here, and a factor of $\\ge 8$ lower than the previously-studied CNN. We also corrupt the cosmic signal by adding noise expected from a 1000 h integration with the Square Kilometre Array, as well as excising a foreground-contaminated 'horizon wedge'. Parameter prediction errors increase when the NNs are trained on these contaminated LC images, though recovery is still good even in the most pessimistic case (with $R^2 \\ge 0.5-0.95$). However, we find no notable differences in performance between network architectures on the contaminated images. We argue this is due to the size of our data set, highlighting the need for larger data sets and/or better data augmentation in order to maximize the potential of NNs in 21 cm parameter estimation.","url_abs":"https://arxiv.org/abs/2107.00018v2","url_pdf":"https://arxiv.org/pdf/2107.00018v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"machine-learning-galaxy-properties-from-21-cm","repo_url":"https://github.com/dprelogo/21cmRNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2107.00018","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00018"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dprelogo/21cmRNN","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"8278a0b80035c6b9","entry":"R2","repo":"dprelogo/21cmRNN","repo_kind":"official","path":"rnn21cm/utilities.py","file_url":"https://github.com/dprelogo/21cmRNN/blob/HEAD/rnn21cm/utilities.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8278a0b80035c6b9"}},{"code_sha256_prefix":"9a90d9e87fefe580","entry":"R2_final","repo":"dprelogo/21cmRNN","repo_kind":"official","path":"rnn21cm/utilities.py","file_url":"https://github.com/dprelogo/21cmRNN/blob/HEAD/rnn21cm/utilities.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9a90d9e87fefe580"}},{"code_sha256_prefix":"321619a70d6cd74b","entry":"R2_numpy","repo":"dprelogo/21cmRNN","repo_kind":"official","path":"rnn21cm/utilities.py","file_url":"https://github.com/dprelogo/21cmRNN/blob/HEAD/rnn21cm/utilities.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"321619a70d6cd74b"}},{"code_sha256_prefix":"dd91f5492e648eda","entry":"apply_uv_coverage","repo":"dprelogo/21cmRNN","repo_kind":"official","path":"rnn21cm/database.py","file_url":"https://github.com/dprelogo/21cmRNN/blob/HEAD/rnn21cm/database.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"dd91f5492e648eda"}},{"code_sha256_prefix":"67fac7a215bfa269","entry":"leakyrelu","repo":"dprelogo/21cmRNN","repo_kind":"official","path":"rnn21cm/hyperparameters.py","file_url":"https://github.com/dprelogo/21cmRNN/blob/HEAD/rnn21cm/hyperparameters.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"67fac7a215bfa269"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}