Initial On-Sky Performance Testing of the Single-Photon Imager for Nanosecond Astrophysics (SPINA) System
Bibliographic record
Abstract
This work presents an initial on-sky performance measurement of the Single-Photon Imager for Nanosecond Astro-physics (SPINA) system, part of our Ultra-Fast Astronomy (UFA) program. We developed the SPINA system based on the position-sensitive silicon photomultiplier (PS-SiPM) detector to record both temporal and spatial information of detected photons. The initial on-sky testing of the SPINA system was conducted on UT 2022 Jul 10, with the Nazarbayev University Transient Telescope at the Assy-Turgen Astrophysical Observatory (NUTTelA-TAO), studied stars with a wide range of brightness and a dark region of the sky without starsm, affected by atmospheric conditions; a background noise level of 1914 counts per second (cps) within this resolution element; and crosstalk probability of ~ 0.18 near the detector’s center while reaching ~ 0.5 at the edges. We derived a 5σ sensitivity of 17.45 Gaia-BP magnitude in a 1s exposure with no atmospheric extinction. Based on a false alarm rate of once per 100 nights, The SPINA system provides a transient sensitivity of 14.06 mag on a 10ms window and a 15 P.E. detection threshold forsub- μstime scale, limited by crosstalk. In addition, the SPINA system proved its capability to detect rapid alterations in the stellar profile: a variation of ±1.8% in the stellar profile FWHM under 20msexposure and ±5% change under 2msexposures, as well as capturing stellar light curves on themsand μsscales.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".