Catalogue of BRITE-Constellation targets
Bibliographic record
Abstract
Context. The BRIght Target Explorer (BRITE) mission collects photometric time series in two passbands with the aim of investigating stellar structure and evolution. Since their launches in the years 2013 and 2014, the constellation of five BRITE nano-satellites has observed a total of more than 700 individual bright stars in 64 fields. Some targets have been observed multiple times. Thus, the total time base of the datasets acquired for those stars can be as long as nine years. Aims. Our aim is to provide a complete description of ready-to-use BRITE data, to show the scientific potential of the BRITE-Constellation data by identifying the most interesting targets, and to demonstrate how scientists can use these data in their research, and encourage them to do so. Methods. We applied a decorrelation process to the automatically reduced BRITE-Constellation data to correct for instrumental effects. We performed a statistical analysis of the light curves obtained for the 300 stars observed in the first 14 fields during the first ~2.5 yr of the mission. We also performed cross-identification with the International Variable Star Index. Results. We present the data obtained by the BRITE-Constellation mission in the first 14 fields it observed, from November 2013 to April 2016. We also describe the properties of the data for these fields and the 300 stars observed in them. Using these data, we detect variability in 64% of the presented sample of stars. Sixty-four stars, or 21.3% of the sample, have not yet been identified as variable in the literature and their data have not been analysed in detail. They can therefore provide valuable scientific material for further research. All data are made publicly available through the BRITE Public Data Archive and the Canadian Astronomy Data Centre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.022 |
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".