Astronomy

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 136 papers with code (395 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Self-Normalizing Neural Networks
  8 Jun 2017 13 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.
• Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
  30 Aug 2019 5 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  We provide sample code in Python and R as well as examples of applications to photometric redshift estimation and likelihood-free cosmological inference via CDE.
• Prediction-Powered Inference
  23 Jan 2023 3 repositories listed Syntology ran 12 of 16 samples · 4 unverified · 3 pointer-only (licence)
  Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system.
• Scaling Language Models: Methods, Analysis & Insights from Training Gopher
  8 Dec 2021 3 repositories listed
  Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
• Extracting the main trend in a dataset: the Sequencer algorithm
  24 Jun 2020 3 repositories listed
  However, some are challenging to detect as they may be expressed in complex manners.
• Deep-Learnt Classification of Light Curves
  19 Sep 2017 3 repositories listed
  As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and techniques to classify light curves.
• Deep Convolutional Denoising of Low-Light Images
  6 Jan 2017 3 repositories listed
  Poisson distribution is used for modeling noise in photon-limited imaging.
• Using matrix-product states for time-series machine learning
  20 Dec 2024 2 repositories listed
  Here, we develop and apply an MPS-based algorithm, MPSTime, for learning a joint probability distribution underlying an observed time-series dataset, and show how it can be used to tackle important time-series ML…
• Light Curve Classification with DistClassiPy: a new distance-based classifier
  18 Mar 2024 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  While tree-based models (e.
• Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources
  22 Jan 2024 2 repositories listed
  We provide a catalog of probabilistic classes for 8, 756 sources, comprising a total of 14, 507 detections, and demonstrate the success of the method at identifying emission from young stellar objects, as well as…
• Rethinking data-driven point spread function modeling with a differentiable optical model
  9 Mar 2022 2 repositories listed
  We change the data-driven modeling space from the pixels to the wavefront by adding a differentiable optical forward model into the modeling framework.
• DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging
  1 Dec 2019 2 repositories listed
  We propose a recurrent neural-network for real-time reconstruction of acoustic camera spherical maps.
• Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
  13 Sep 2019 2 repositories listed Syntology ran 0 of 10 samples · 10 unverified
  Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe 𝒪(100)s of transient GW events per year.
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
  18 Apr 2019 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to…
• Efficient Optimization of Echo State Networks for Time Series Datasets
  12 Mar 2019 2 repositories listed
  Echo State Networks (ESNs) are recurrent neural networks that only train their output layer, thereby precluding the need to backpropagate gradients through time, which leads to significant computational gains.
• Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph
  20 Apr 2018 2 repositories listed
  Large datasets represented by multidimensional data point clouds often possess non-trivial distributions with branching trajectories and excluded regions, with the recent single-cell transcriptomic studies of developing…
• Astronomical image reconstruction with convolutional neural networks
  14 Dec 2016 2 repositories listed
  State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem.
• Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
  19 Jun 2025 1 repository listed
  Observational astronomy relies on visual feature identification to detect critical astrophysical phenomena.
• Statistical Machine Learning for Astronomy -- A Textbook
  13 Jun 2025 1 repository listed
  The treatment builds toward modern techniques including neural networks through a solid foundation in classical methods and their theoretical underpinnings.
• Emulating compact binary population synthesis simulations with robust uncertainty quantification and model comparison: Bayesian normalizing flows
  6 Jun 2025 1 repository listed
  Population synthesis simulations of compact binary coalescences~(CBCs) play a crucial role in extracting astrophysical insights from an ensemble of gravitational wave~(GW) observations.
• Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
  5 Jun 2025 1 repository listed
  Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more.
• RGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification
  25 May 2025 1 repository listed
  We introduce a novel machine learning dataset tailored for the classification of bent radio active galactic nuclei (AGN) in astronomical observations.
• Anomaly detection in radio galaxy data with trainable COSFIRE filters
  24 May 2025 1 repository listed
  By characterizing normal patterns and detecting deviations, this semi-supervised methodology overcomes the need for anomalous examples in the training set, a major limitation of traditional supervised methods.
• AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
  6 May 2025 1 repository listed
  Anomaly detection in large datasets is essential in fields such as astronomy and computer vision; however, supervised methods typically require extensive anomaly labelling, which is often impractical.
• Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes
  16 Apr 2025 1 repository listed
  Radio Frequency Interference (RFI) is a known growing challenge for radio astronomy, intensified by increasing observatory sensitivity and prevalence of orbital RFI sources.
• When Astronomy Meets AI: Manazel For Crescent Visibility Prediction in Morocco
  27 Mar 2025 1 repository listed
  The accurate determination of the beginning of each Hijri month is essential for religious, cultural, and administrative purposes.
• A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations
  21 Mar 2025 1 repository listed
  The search for exoplanets is an active field in astronomy, with direct imaging as one of the most challenging methods due to faint exoplanet signals buried within stronger residual starlight.
• Scalable Cosmic AI Inference using Cloud Serverless Computing with FMI
  8 Jan 2025 1 repository listed
  Large-scale astronomical image data processing and prediction is essential for astronomers, providing crucial insights into celestial objects, the universe's history, and its evolution.
• IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
  5 Jan 2025 1 repository listed
  Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy.
• Energy and polarization based on-line interference mitigation in radio interferometry
  19 Dec 2024 1 repository listed
  We combine statistics based on the energy as well as the polarization alignment of the correlated signal to develop an on-line RFI mitigation scheme that can be applied to a data stream produced by the correlator in…

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.