Ultra Violet Imaging Telescope (UVIT) on ASTROSAT
Bibliographic record
Abstract
The Ultra Violet Imaging Telescope on ASTROSAT Satellite mission is a suite of Far Ultra Violet (FUV: 130 - 180 nm), Near Ultra Violet (NUV: 200 - 300 nm) and Visible band (VIS: 320-550nm) imagers. ASTROSAT is the multiwavelength mission of ISRO. UVIT will image the sky simultaneously in three channels with a field of view diameter of ̃ 28 arcminutes and an angular resolution < 1.8". Two identical co-Aligned telescopes (T1, T2) of Ritchey-Chretien configuration (Primary mirror of ̃375 mm diameter) collect the celestial radiation and feed the detector systems via a selectable filter on a filter wheel mechanism; gratings are available in the filter wheels of FUV and NUV channels for slitless low-resolution spectroscopy. The photon-counting detector system for each of the 3 channels is generically identical. one of the telescopes images in the FUV channel, while the other images in NUV and VIS channels via a beamsplitter. Images from the VIS channel are principally used for measuring drift, used in construction of images on the ground by shift and add, and to reconstruct absolute aspect of the images. Adequate baffling has been provided for reducing the scattered background from the Sun, earth albedo and other bright objects. The one-Time opening mechanical cover on each telescope also works as a Sun-shield after deployment. We will present the overall (mechanical, optical and electrical) design of the payload. © 2012 SPIE.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.036 |
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".