Eureka!: An End-to-End Pipeline for JWST Time-SeriesObservations
Bibliographic record
Abstract
Eureka! is a data reduction and analysis pipeline for exoplanet time-series observations, with a particular focus on James Webb Space Telescope (JWST, Gardner et al., 2006) data.JWST was launched on December 25, 2021 and over the next 1-2 decades will pursue four main science themes: Early Universe, Galaxies Over Time, Star Lifecycle, and Other Worlds.Our focus is on providing the astronomy community with an open source tool for the reduction and analysis of time-series observations of exoplanets in pursuit of the fourth of these themes, Other Worlds.The goal of Eureka! is to provide an end-to-end pipeline that starts with raw, uncalibrated FITS files and ultimately yields precise exoplanet transmission and/or emission spectra.The pipeline has a modular structure with six stages, and each stage uses a "Eureka!Control File" (ECF; these files use the .ecffile extension) to allow for easy control of the pipeline's behavior.Stage 5 also uses a "Eureka!Parameter File" (EPF; these files use the .epffile extension) to control the fitted parameters.We provide template ECFs for the MIRI (Rieke et al., 2015), NIRCam (Horner & Rieke, 2004), NIRISS (Maszkiewicz, 2017), and NIRSpec (Bagnasco et al., 2007) instruments on JWST and the WFC3 instrument (Kimble et al., 2008) on the Hubble Space Telescope (HST, Bahcall, 1986).These templates give users a good starting point for their analyses, but Eureka! is not intended to be used as a black box tool, and users should expect to fine-tune some settings for each observation in order to achieve optimal results.At each stage, the pipeline creates intermediate figures and outputs that allow users to compare Eureka!'s performance using different parameter settings or to compare Eureka! with an independent pipeline.The ECF used to run each stage is also copied into the output folder from each stage to enhance reproducibility.Finally, while Eureka! has been optimized for exoplanet observations (especially the latter stages of the code), much of the core functionality could also be repurposed for JWST time-series observations in other research domains thanks to Eureka!'s modularity.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.095 | 0.127 |
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".