Considerations for a next-generation Great Observatory class space-based interferometer for far-infrared astronomy
Bibliographic record
Abstract
Astronomy at far-infrared (far-IR) wavelengths is essential to our understanding of the evolution of the cosmos, from the star formation history of galaxies to how the ice distribution affects the formation of extrasolar planetary systems.The Hubble Space Telescope, James Webb Space Telescope, and the Atacama Large Millimeter Array have already produced ground-breaking astronomical observations with high angular resolution spanning the visible to sub-millimetre wavelength regimes.However, this presents a gap in the far-IR, from roughly 30-400 µm, where ground-based observations are largely intractable due to the opacity of Earth's atmosphere.Indeed, no telescope, observatory, or interferometry array has ever achieved sub-arcsecond angular resolution over this wavelength range.A space-based solution is needed.However, a space-based far-IR telescope capable of subarcsecond angular resolution and high sensitivity, at a cost comparable to the largest space missions to date, presents unique physical, practical, and engineering challenges.In this paper, we envisage what a far-IR Great Observatory class mission might look like in the context of the already-studied Origins Space Telescope (OST) and the Space Infrared Interferometric Telescope (SPIRIT).We begin with a historical reflection of far-IR missions, including OST and the recommendations by the Astro2020 Decadal Survey for a de-scoped mission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.014 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".