{"url":"/task/astronomy","name":"Astronomy","slug":"astronomy","description_markdown":"Astronomy is the study of everything in the universe beyond Earth’s atmosphere. That includes objects we can see with our naked eyes, like the Sun, the Moon, the planets, and the stars. It also contains objects we can only see with telescopes or other instruments, like faraway galaxies and tiny particles. And it even includes questions about things we can't see, like dark matter and energy.","categories":[{"name":"Miscellaneous","url":"/area/miscellaneous"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":395,"papers_with_code":136,"benchmarks":1,"benchmark_tables_in_archive":1,"benchmark_tables_shown":1,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":4,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/astronomy-on-big-bench","slug":"astronomy-on-big-bench","dataset":"BIG-bench","dataset_url":"/dataset/big-bench","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Gopher-280B (few-shot, k=5)","paper_title":"Scaling Language Models: Methods, Analysis & Insights from Training Gopher","paper_url":"/paper/scaling-language-models-methods-analysis-1","paper_date":"2021-12-08","arxiv_id":"2112.11446","code_links":[{"title":"allenai/dolma","url":"https://github.com/allenai/dolma"},{"title":"rvlopes/gloria","url":"https://github.com/rvlopes/gloria"},{"title":"bramiozo/PubScience","url":"https://github.com/bramiozo/PubScience"}],"syntology":null}}],"datasets":[{"url":"/dataset/big-bench","name":"BIG-bench","full_name":"Beyond the Imitation Game Benchmark","num_papers_in_archive":349},{"url":"/dataset/plasticc","name":"PLAsTiCC","full_name":"Photometric LSST Astronomical Time-Series Classification Challenge","num_papers_in_archive":10},{"url":"/dataset/manga","name":"MaNGA","full_name":"Mapping Nearby Galaxies at APO","num_papers_in_archive":2},{"url":"/dataset/rgz-emu-semantic-taxonomy","name":"RGZ EMU: Semantic Taxonomy","full_name":"Radio Galaxy Zoo EMU: Towards a Semantic Radio Galaxy Morphology Taxonomy","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":136,"tagged_in_all":395,"items":[{"url":"/paper/self-normalizing-neural-networks","title":"Self-Normalizing Neural Networks","date":"2017-06-08","arxiv_id":"1706.02515","repositories_listed":13,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/conditional-density-estimation-tools-in","title":"Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference","date":"2019-08-30","arxiv_id":"1908.11523","repositories_listed":5,"syntology":{"n":6,"n_ran":0,"n_unverified":6,"n_pointer_only":0}},{"url":"/paper/prediction-powered-inference","title":"Prediction-Powered Inference","date":"2023-01-23","arxiv_id":"2301.09633","repositories_listed":3,"syntology":{"n":16,"n_ran":12,"n_unverified":4,"n_pointer_only":3}},{"url":"/paper/scaling-language-models-methods-analysis-1","title":"Scaling Language Models: Methods, Analysis & Insights from Training Gopher","date":"2021-12-08","arxiv_id":"2112.11446","repositories_listed":3,"syntology":null},{"url":"/paper/extracting-the-main-trend-in-a-dataset-the","title":"Extracting the main trend in a dataset: the Sequencer algorithm","date":"2020-06-24","arxiv_id":"2006.13948","repositories_listed":3,"syntology":null},{"url":"/paper/deep-learnt-classification-of-light-curves","title":"Deep-Learnt Classification of Light Curves","date":"2017-09-19","arxiv_id":"1709.06257","repositories_listed":3,"syntology":null},{"url":"/paper/deep-convolutional-denoising-of-low-light","title":"Deep Convolutional Denoising of Low-Light Images","date":"2017-01-06","arxiv_id":"1701.01687","repositories_listed":3,"syntology":null},{"url":"/paper/using-matrix-product-states-for-time-series","title":"Using matrix-product states for time-series machine learning","date":"2024-12-20","arxiv_id":"2412.15826","repositories_listed":2,"syntology":null},{"url":"/paper/light-curve-classification-with-distclassipy","title":"Light Curve Classification with DistClassiPy: a new distance-based classifier","date":"2024-03-18","arxiv_id":"2403.12120","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/unsupervised-machine-learning-for-the","title":"Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources","date":"2024-01-22","arxiv_id":"2401.12203","repositories_listed":2,"syntology":null},{"url":"/paper/rethinking-data-driven-point-spread-function","title":"Rethinking data-driven point spread function modeling with a differentiable optical model","date":"2022-03-09","arxiv_id":"2203.04908","repositories_listed":2,"syntology":null},{"url":"/paper/deepwave-a-recurrent-neural-network-for-real","title":"DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging","date":"2019-12-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/bayesian-parameter-estimation-using","title":"Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy","date":"2019-09-13","arxiv_id":"1909.06296","repositories_listed":2,"syntology":{"n":10,"n_ran":0,"n_unverified":10,"n_pointer_only":0}},{"url":"/paper/convolutional-neural-networks-a-magic-bullet","title":"Convolutional neural networks: a magic bullet for gravitational-wave detection?","date":"2019-04-18","arxiv_id":"1904.08693","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/efficient-optimization-of-echo-state-networks","title":"Efficient Optimization of Echo State Networks for Time Series Datasets","date":"2019-03-12","arxiv_id":"1903.05071","repositories_listed":2,"syntology":null},{"url":"/paper/robust-and-scalable-learning-of-complex","title":"Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph","date":"2018-04-20","arxiv_id":"1804.07580","repositories_listed":2,"syntology":null},{"url":"/paper/astronomical-image-reconstruction-with","title":"Astronomical image reconstruction with convolutional neural networks","date":"2016-12-14","arxiv_id":"1612.04526","repositories_listed":2,"syntology":null},{"url":"/paper/can-ai-dream-of-unseen-galaxies-conditional","title":"Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation","date":"2025-06-19","arxiv_id":"2506.16233","repositories_listed":1,"syntology":null},{"url":"/paper/statistical-machine-learning-for-astronomy-a","title":"Statistical Machine Learning for Astronomy -- A Textbook","date":"2025-06-13","arxiv_id":"2506.12230","repositories_listed":1,"syntology":null},{"url":"/paper/emulating-compact-binary-population-synthesis","title":"Emulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows","date":"2025-06-06","arxiv_id":"2506.05657","repositories_listed":1,"syntology":null},{"url":"/paper/2506-04553","title":"Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices","date":"2025-06-05","arxiv_id":"2506.04553","repositories_listed":1,"syntology":null},{"url":"/paper/rgc-bent-a-novel-dataset-for-bent-radio","title":"RGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification","date":"2025-05-25","arxiv_id":"2505.19249","repositories_listed":1,"syntology":null},{"url":"/paper/anomaly-detection-in-radio-galaxy-data-with","title":"Anomaly detection in radio galaxy data with trainable COSFIRE filters","date":"2025-05-24","arxiv_id":"2505.18643","repositories_listed":1,"syntology":null},{"url":"/paper/anomalymatch-discovering-rare-objects-of","title":"AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning","date":"2025-05-06","arxiv_id":"2505.03509","repositories_listed":1,"syntology":null},{"url":"/paper/polarisation-inclusive-spiking-neural","title":"Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes","date":"2025-04-16","arxiv_id":"2504.11720","repositories_listed":1,"syntology":null},{"url":"/paper/when-astronomy-meets-ai-manazel-for-crescent","title":"When Astronomy Meets AI: Manazel For Crescent Visibility Prediction in Morocco","date":"2025-03-27","arxiv_id":"2503.21634","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-statistical-model-of-star-speckles-for","title":"A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations","date":"2025-03-21","arxiv_id":"2503.17117","repositories_listed":1,"syntology":null},{"url":"/paper/scalable-cosmic-ai-inference-using-cloud","title":"Scalable Cosmic AI Inference using Cloud Serverless Computing with FMI","date":"2025-01-08","arxiv_id":"2501.06249","repositories_listed":1,"syntology":null},{"url":"/paper/iris-a-bayesian-approach-for-image","title":"IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors","date":"2025-01-05","arxiv_id":"2501.02473","repositories_listed":1,"syntology":null},{"url":"/paper/energy-and-polarization-based-on-line","title":"Energy and polarization based on-line interference mitigation in radio interferometry","date":"2024-12-19","arxiv_id":"2412.14775","repositories_listed":1,"syntology":null}],"syntology_records":6,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}