Pixel level decorrelation in service of the <i>Spitzer</i> microlens parallax survey
Bibliographic record
Abstract
ABSTRACT Microlens parallax measurements combining space-based and ground-based observatories can be used to study planetary demographics. In recent years, the Spitzer Space Telescope was used as a microlens parallax satellite. Meanwhile, Spitzer IRAC has been employed to study short-period exoplanets and their atmospheres. As these investigations require exquisite photometry, they motivated the development of numerous self-calibration techniques now widely used in the exoplanet atmosphere community. Specifically, pixel level decorrelation (PLD) was developed for starring-mode observations in uncrowded fields. We adapt and extend PLD to make it suitable for observations obtained as part of the Spitzer Microlens Parallax Campaign. We apply our method to two previously published microlensing events, OGLE-2017-BLG-1140 and OGLE-2015-BLG-0448, and compare its performance to the state-of-the-art pipeline used to analyses Spitzer microlensing observation. We find that our method yields photometry 1.5–6 times as precise as previously published. In addition to being useful for Spitzer, a similar approach could improve microlensing photometry with the forthcoming Nancy Grace Roman Space Telescope.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".