How dark the sky: the JWST backgrounds
Bibliographic record
Abstract
We describe the sources of stray light and thermal background that affect JWST observations, report actual backgrounds as measured from commissioning and early-science observations, compare these background levels to prelaunch predictions, estimate the impact of the backgrounds on science performance, and explore how the backgrounds probe the achieved configuration of the deployed observatory. We find that for almost all applications, the observatory is limited by the irreducible astrophysical backgrounds, rather than scattered stray light and thermal self-emission, for all wavelengths lambda < 12.5 micron, thus meeting the level 1 requirement. This result was not assured given the open architecture and thermal challenges of JWST, and it is the result of meticulous attention to stray light and thermal issues in the design, construction, integration, and test phases. From background considerations alone, JWST will require less integration time in the near-infrared compared to a system that just met the stray-light requirements; as such, JWST will be even more powerful than expected for deep imaging at 1-5 micron. In the mid-infrared, the measured thermal backgrounds closely match prelaunch predictions. The background near 10 micron is slightly higher than predicted before launch, but the impact on observations is mitigated by the excellent throughput of MIRI, such that instrument sensitivity will be as good as expected prelaunch. These measured background levels are fully compatible with JWST's science goals and the Cycle 1 science program currently underway.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".