An Automated Pipeline for Ultra-Violet Imaging Telescope (UVIT)
Bibliographic record
Abstract
We describe a versatile pipeline for processing the data collected by the Ultra-Violet Imaging Telescope (UVIT) on board Indian Multi-wavelength astronomical satellite AstroSat.The UVIT instrument carries out simultaneous astronomical imaging through selected filters / gratings in Far-Ultra-Violet (FUV), Near-Ultra-Violet & visible (VIS) bands of the targeted circular sky field (~ 0.5 deg dia). This pipeline converts the data (Level-1) emanating from UVIT in their raw primitive format supplemented by inputs from the spacecraft sub-systems into UV sky images (& slitless grating spectra) and associated products readily usable by astronomers (Level-2). The primary products include maps of Intensity (rate of photon arrival), error on Intensity and effective Exposure. The pipeline is open source, extensively user configurable with many selectable parameters and its execution is fully automated. The key ingredients of the pipeline includes - extraction of drift in pointing of the spacecraft, and disturbances in pointing due to internal movements; application of various corrections to measured position in the detector for each photon - e.g. differential pointing with respect to a reference frame for shift and add operation, systematic effects and artifacts in the optics of the telescopes and detectors, exposure tracking on the sky, alignment of sky products from multi-episode exposures to generate a consolidated set and astrometry. Detailed logs of operations and intermediate products for every processing stage are accessible via user selectable options. While large number of selectable parameters are available for the user, a well characterized standard default set is used for executing this pipeline at the Payload Operation Centre (POC) for UVIT and selected products are archived and disseminated by the Indian Space Research Organization (ISRO) through its ISSDC portal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.041 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".