THE DISTANCE OF THE FIRST OVERTONE RR LYRAE VARIABLES IN THE MACHO LARGE MAGELLANIC CLOUD DATABASE: A NEW METHOD TO CORRECT FOR THE EFFECTS OF CROWDING
Bibliographic record
Abstract
Previous studies have indicated that many of the RR Lyrae variables in the LMC have properties similar to those in the Galactic globular cluster M3. Assuming that the M3 RR Lyrae variables follow the same relationships among period, temperature, amplitude, and Fourier phase parameter ϕ 31 as their LMC counterparts, we have used the M3 ϕ 31 –log P relation to identify the M3-like unevolved first overtone RR Lyrae variables in 16 MACHO fields near the LMC bar. The temperatures of these variables were calculated from the M3 log P –log T eff relation so that the extinction could be derived for each star separately. Since blended stars have lower amplitudes for a given period, the period–amplitude relation should be a useful tool for determining which stars are affected by crowding. We find that the low-amplitude LMC RR1 stars are brighter than the stars that fit the M3 period–amplitude relation and we estimate that at least 40% of the stars are blended. Simulated data for three of the crowded stars illustrate that an unresolved companion with V ∼ 20.5 could account for the observed amplitude and magnitude. We derive a corrected mean apparent magnitude 〈 V 0 〉 = 19.01 ± 0.10 (extinction) ±0.02 (calibration) for the 51 uncrowded unevolved M3-like RR1 variables. Assuming that the unevolved RR1 variables in M3 have a mean absolute magnitude M V = 0.52 ± 0.02 leads to an LMC distance modulus μ = 18.49 ± 0.11.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".