{"dataset":{"slug":"astronomy-machine-learning","title":"Machine Learning in Astronomy","description":"The computational layer of astronomy — the machine-learning methods, the astronomical applications (galaxy morphology, photometric redshifts, real-time alert classification), and the data-engineering workflows.","version":"1.0.0","lastGenerated":"2026-06-29","license":"CC BY-SA 4.0","entityCount":18,"sources":["nasa","noirlab"]},"entities":[{"id":"ml_method:anomaly-detection","name":"Anomaly Detection","type":"ml_method","domain":"science","description":"Finding the rare objects that do not fit any known pattern — the outliers that may be the next new class of transient or a genuinely unexpected discovery. As surveys outgrow human inspection, anomaly detection is how the strangest objects get noticed at all.","entryPath":"/astro-ml/anomaly-detection"},{"id":"ml_workflow:benchmark-datasets","name":"Benchmark Datasets","type":"ml_workflow","domain":"science","description":"Standard, shared datasets on which different methods are compared on equal footing — like the Galaxy Zoo morphology labels or the PLAsTiCC transient-classification challenge. They let the field measure real progress rather than incomparable claims.","entryPath":"/astro-ml/benchmark-datasets"},{"id":"ml_method:classification","name":"Classification","type":"ml_method","domain":"science","description":"Assigning objects to discrete categories from their measured features — star or galaxy, supernova type, real detection or artefact. It is the workhorse of survey astronomy, sorting the millions of objects a modern survey finds into kinds a human never could review one by one.","entryPath":"/astro-ml/classification"},{"id":"ml_method:clustering","name":"Clustering","type":"ml_method","domain":"science","description":"Grouping objects by similarity without being told the categories in advance — letting the structure in the data reveal itself. It is used to find natural classes of objects and to organise catalogues too large to label by hand.","entryPath":"/astro-ml/clustering"},{"id":"ml_workflow:feature-extraction","name":"Feature Extraction","type":"ml_workflow","domain":"science","description":"Turning raw data into the informative quantities a model uses — statistics of a light curve, cut-outs around a source, colours from photometry. Well-chosen features can make a simple model succeed; increasingly, representation learning discovers them automatically.","entryPath":"/astro-ml/feature-extraction"},{"id":"ml_method:foundation-models","name":"Foundation Models","type":"ml_method","domain":"science","description":"Large models pre-trained on broad data that can then be adapted to many specific tasks with little extra training. In astronomy they are an emerging approach to building a single model that understands images, spectra, or light curves across many problems at once.","entryPath":"/astro-ml/foundation-models"},{"id":"ml_application:galaxy-morphology-classification","name":"Galaxy Morphology Classification","type":"ml_application","domain":"science","description":"Sorting galaxies by their shape — spiral, elliptical, irregular, merging — from survey images. One of the earliest large-scale meetings of astronomy and machine learning, building on the labels gathered by citizen-science projects like Galaxy Zoo to train automatic classifiers for surveys too large to inspect by eye.","entryPath":"/astro-ml/galaxy-morphology-classification"},{"id":"ml_workflow:model-evaluation","name":"Model Evaluation","type":"ml_workflow","domain":"science","description":"Measuring honestly how well a model performs — its accuracy, completeness, and purity, whether its confidences are calibrated, and how it behaves on data unlike its training set. Careful evaluation is what separates a genuinely useful model from one that has merely memorised its examples.","entryPath":"/astro-ml/model-evaluation"},{"id":"ml_application:photometric-redshifts","name":"Photometric Redshifts","type":"ml_application","domain":"science","description":"Estimating how far away a galaxy is from its brightness in a few broad colour bands, without taking a full spectrum. It is far less precise than a spectroscopic redshift but can be done for the hundreds of millions of galaxies in an imaging survey, underpinning weak-lensing and large-scale-structure cosmology.","entryPath":"/astro-ml/photometric-redshifts"},{"id":"ml_application:real-time-alert-classification","name":"Real-Time Alert Classification","type":"ml_application","domain":"science","description":"Classifying the flood of alerts that a survey like Rubin issues — millions each night when something on the sky changes — quickly enough to catch the fleeting events worth following up. It is the problem the community alert brokers exist to solve.","entryPath":"/astro-ml/real-time-alert-classification"},{"id":"ml_method:regression","name":"Regression","type":"ml_method","domain":"science","description":"Predicting a continuous quantity from an object's features — a galaxy's redshift, a star's temperature or age — rather than a discrete label. Regression turns cheap, plentiful measurements into estimates of quantities that would otherwise need expensive follow-up.","entryPath":"/astro-ml/regression"},{"id":"ml_method:representation-learning","name":"Representation Learning","type":"ml_method","domain":"science","description":"Learning a compact, informative summary — an 'embedding' — of a complex object like an image or a light curve, so that similar objects sit near each other. Good representations make every downstream task, from classification to search, easier and more accurate.","entryPath":"/astro-ml/representation-learning"},{"id":"ml_method:self-supervised-learning","name":"Self-Supervised Learning","type":"ml_method","domain":"science","description":"Training a model on unlabelled data by inventing a task it can grade itself on — predicting a hidden part of an image, or telling two views of the same object apart. It is powerful in astronomy, where raw data is abundant but expert labels are scarce.","entryPath":"/astro-ml/self-supervised-learning"},{"id":"ml_application:source-extraction","name":"Source Extraction","type":"ml_application","domain":"science","description":"Finding and measuring the individual stars and galaxies in an astronomical image, and separating real sources from noise and artefacts — the first step of nearly every imaging pipeline. Machine-learning methods increasingly complement the classical algorithms, especially in crowded or blended fields.","entryPath":"/astro-ml/source-extraction"},{"id":"ml_application:strong-lens-finding","name":"Strong Lens Finding","type":"ml_application","domain":"science","description":"Searching survey images for the rare, distinctive arcs and rings of strong gravitational lensing — where a foreground mass bends the light of a background galaxy. The lenses are rare enough, and the images numerous enough, that automated finders are the only way to build large samples.","entryPath":"/astro-ml/strong-lens-finding"},{"id":"ml_application:supernova-classification","name":"Supernova Classification","type":"ml_application","domain":"science","description":"Deciding what kind of exploding star a transient is — often from its light curve alone, before or without a spectrum. Fast, automatic classification is essential when a survey finds thousands of supernovae a night and only a few can be followed up in detail.","entryPath":"/astro-ml/supernova-classification"},{"id":"ml_workflow:training-datasets","name":"Training Datasets","type":"ml_workflow","domain":"science","description":"The labelled examples a model learns from. Their size, coverage, and biases largely determine how well a model works and where it fails — a classifier only knows the kinds of object it was shown, and inherits any selection effects in how those examples were gathered.","entryPath":"/astro-ml/training-datasets"},{"id":"ml_application:transit-detection","name":"Transit Detection","type":"ml_application","domain":"science","description":"Picking the tiny, periodic dips of an exoplanet transit out of a noisy stellar light curve — and telling a real planet from the many kinds of false positive. Machine learning now helps sift the enormous light-curve archives of transit surveys for the faintest candidates.","entryPath":"/astro-ml/transit-detection"}]}