A new method for finding nearby white dwarfs exoplanets and detecting biosignatures
Bibliographic record
Abstract
ABSTRACT We demonstrate that the James Webb Space Telescope (JWST) can detect infrared (IR) excess from the blended light spectral energy distribution of spatially unresolved terrestrial exoplanets orbiting nearby white dwarfs. We find that JWST is capable of detecting warm (habitable-zone; Teq = 287 K) Earths or super-Earths and hot (400–1000 K) Mercury analogues in the blended light spectrum around the nearest 15 isolated white dwarfs with 10 h of integration per target using MIRI’s medium-resolution spectrograph (MRS). Further, these observations constrain the presence of a CO2-dominated atmosphere on these planets. The technique is nearly insensitive to system inclination, and thus observation of even a small sample of white dwarfs could place strong limits on the occurrence rates of warm terrestrial exoplanets around white dwarfs in the solar neighbourhood. We find that JWST can also detect exceptionally cold (100–150 K) Jupiter-sized exoplanets via MIRI broad-band imaging at $\lambda = 21\, \mathrm{\mu m}$ for the 34 nearest (<13 pc) solitary white dwarfs with 2 h of integration time per target. Using IR excess to detect thermal variations with orbital phase or spectral absorption features within the atmosphere, both of which are possible with long-baseline MRS observations, would confirm candidates as actual exoplanets. Assuming an Earth-like atmospheric composition, we find that the detection of the biosignature pair O3+CH4 is possible for all habitable-zone Earths (within 6.5 pc; six white dwarf systems) or super-Earths (within 10 pc; 17 systems) orbiting white dwarfs with only 5–36 h of integration using MIRI’s low-resolution spectrometer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".