Updated data processing and analysis methods for the AstroSat UltraViolet Imaging Telescope (UVIT)
Bibliographic record
Abstract
The data processing methods for AstroSat’s UVIT instrument and the software implementations that have been developed over the past few years will be presented. The instrument calibration is discussed in other work. Source magnitude extraction is calibrated with respect to a curve of growth analysis (COG) where the COG calibration is described in Ref. 2. UVIT images require corrections for geometric distortion, flat-field illumination, and spacecraft drift, which is carried out with the UVIT-customized software package CCDLAB. A description of the usage of the CCDLAB Pipeline for UVIT data reduction from Level 1 raw data to completed science images was presented in Ref. 4. The astrometry has been improved recently by incorporating the Gaia DR2 catalog and developing a new algorithm for coordinate matching. CCDLAB was upgraded in 2020 to produce exposure maps for the entire field of view. New methods for source extraction for crowded fields have now been added to the CCDLAB Pipeline. Other new updates will be discussed.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.039 |
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".