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astronomy-pipeline

sky survey alert pipeline

Rubin LSST

SystemTechniques · 8Primitives · 29 astronomy-pipeline Pub/sub fan-outdistributed · medium: Deliver one published message to every subscriber of a topic by sharing one encoded payload and giving each connection a bounded queue with an explicit slow-consumer policy.›Gradient-boosted decision trees (GB…machine-learning · medium: Add small regression trees one at a time, each fitted to the loss gradient; histogram splits over 256 bins make it the strongest tabular default.›Astrometric plate solvingmedia · medium: Match detected star patterns (quad hashes) to a catalog to solve the image WCSImage differencing psf matchingmedia · medium: Subtract a reference image after PSF-matching convolution (Alard-Lupton, ZOGY) to find transientsSource extractionmedia · medium: Detect and measure sources in images: background estimation, thresholding, deblending, photometryFft convolutionsignal-processing · medium: Overlap-add and uniformly partitioned convolution for long FIR filters and reverbColumnar storagestorage · medium: Store each column contiguously so analytic scans read only the columns they need, compress well and run as tight SIMD loops.›Healpix sky pixelizationgeometry · low: HEALPix and HTM equal-area hierarchical sphere pixelization for sky indexing and cross-matchingk-d treegeometry · low: A binary tree that splits k-dimensional points by one coordinate per level, giving fast nearest-neighbour and box queries on static low-dimensional data.›Robust statisticsstatistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.›Object storagecloud · low: S3-compatible blob storage at cents per GB-month with 11 nines durability; pick by egress price, request price and latency, not by storage price alone.›Image resampling filtersgraphics · low: Resize an image by convolving with a kernel (box, triangle, cubic, Lanczos) whose width scales with the shrink factor, done as two separable 1-D passes with precomputed weights.›SPSC ring bufferdata-structures · low: Bounded wait-free queue for exactly one producer and one consumer thread, built from an array and two indices with acquire/release ordering.›Topic matching triedata-structures · low: Trie over topic levels with + and # wildcards for subscription matchingBackpressuresystems · low: Bound every queue and make a full queue push back on the producer by blocking, dropping, rejecting or withholding credit.›Quantile sketchesdata-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histogramsLoss functionsmachine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.›Regularizationmachine-learning · low: Techniques that trade training fit for generalization: weight decay, dropout, early stopping, label smoothing and data mixing, with the exact update rules and defaults.›Map projectionsgeometry · low: Web Mercator, UTM and equal-area projections, datums and coordinate transformsRansacrobotics · low: Random sample consensus for robust model fitting with outliers (homographies, planes, poses)Image warping remapgraphics · low: Resampling with lookup maps: resize, lens undistortion, homographies, bilinear/bicubicLU and Cholesky factorizationnumerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).›Convolution image filtersgraphics · low: Separable 2D convolution kernels for blur, sharpen (unsharp mask) and edges with border handling+ 1 more techniques+ 13 more primitives