BLIND DATE: USING PROPER MOTIONS TO DETERMINE THE AGES OF HISTORICAL IMAGES
Bibliographic record
Abstract
Astrometric calibration is based on patterns of cataloged stars and therefore effectively assumes a particular epoch, which can be substantially incorrect for historical images. With the known proper motions of stars we can "run back the clock" to an approximation of the night sky in any given year, and in principle the year that best fits stellar patterns in any given image is an estimate of the year in which that image was taken. In this paper, we use 47 scanned photographic images of M44 spanning the years 1910–1975 to demonstrate this technique. We use only the pixel information in each image; we use no prior information or metadata about image pointing, scale, orientation, or date. Blind Date returns date metadata for each input image. It also improves the astrometric calibration of the image because the final astrometric calibration is performed at the appropriate epoch. The accuracy and reliability of Blind Date are functions of image size, pointing, angular resolution, and depth; performance is related to the sum of proper-motion signal-to-noise ratios for catalog stars measured in the input image. All of the science-quality images and 90% of the low-quality images in our sample of photographic plate images of M44 have their dates reliably determined to within a decade, while 75% of our science-quality images have their dates reliably determined to within 3 years. This method has the potential to insert "lost" data into incomplete sets of time-domain imaging, improving constraints from historical data on transients, variable stars, stellar motions, and minor planet orbits.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".