Timing offset calibration of CZTI instrument aboard ASTROSAT
Bibliographic record
Abstract
Aim: Both the radio and the high-energy emission mechanism in pulsars is not yet properly understood. A multiwavelength study is likely to help better understand of such processes. ASTROSAT, the first Indian space-based observatory, has five instruments aboard that cover the electromagnetic spectrum from infra-red (1300 Å) to hard X-ray (380 keV). The instrument relevant to our study is the Cadmium Zinc Telluride Imager (CZTI). CZTI is a hard X-ray telescope functional over an energy range of 20–380 keV. We aim to estimate the timing offset introduced in the data acquisition pipeline of the instrument, which will help in time alignment of high energy time-series with those from two other ground based observatories, viz. the Giant Meterwave Radio Telescope (GMRT) and the Ooty Radio Telescope (ORT). Method: PSR B0531+21 is a well studied bright pulsar with closely aligned radio and hard X-ray pulse profiles. We used simultaneous observations of this pulsar with the ASTROSAT, the ORT, and the GMRT. As the pulsar resides in a very turbulent environment and shows significant timing noise, it was specially observed using the ORT with almost daily cadence to obtain good timing solutions. We also supplemented the ORT data with archival Fermi data for estimation of timing noise. We obtained the phase connected timing solution of the pulsar by estimating its dispersion measure variations and the timing noise. The timing offset of ASTROSAT instruments was estimated from fits to pulse arrival time data at the ASTROSAT and the radio observatories. Results: We estimate the relative offset of ASTROSAT-CZTI with respect to GMRT to be −4716 ± 50 μs. The corresponding offset with the ORT was −29639 ± 50 μs and Fermi-LAT was −5368 ± 56 μs respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".