High-precision ground-based photometry of exoplanets
Bibliographic record
Abstract
High-precision photometry of transiting exoplanet systems has contributed significantly to our understanding of the properties of their atmospheres. The best targets are the bright exoplanet systems, for which the high number of photons allow very high signal-to-noise ratios. Most of the current instruments are not optimised for these high-precision measurements, either they have a large read-out overhead to reduce the readnoise and/or their field-of-view is limited, preventing simultaneous observations of both the target and a reference star. Recently we have proposed a new wide-field imager for the Observatoir de Mont-Megantic optimised for these bright systems (PI: Jayawardhana). The instruments has a dual beam design and a field-of-view of 17' by 17'. The cameras have a read-out time of 2 seconds, significantly reducing read-out overheads. Over the past years we have obtained significant experience with how to reach the high precision required for the characterisation of exoplanet atmospheres. Based on our experience we provide the following advice:
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
High-precision photometry technique for exoplanets; an astronomy methods and instrumentation contribution.
This studies exoplanet observation technology and photometry, not research practice.
Domain astronomy methods paper on exoplanet photometry instrumentation and observing practice.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".