The science case for a far-infrared interferometer in the era of JWST and ALMA
Bibliographic record
Abstract
A space-based far-infrared interferometer could work synergistically with the James Webb Space Telescope (JWST) and the Atacama Large Millimeter Array (ALMA) to revolutionize our understanding of the astrophysical processes leading to the formation of habitable planets and the co-evolution of galaxies and their central supermassive black holes. Key to these advances are measurements of water in its frozen and gaseous states, observations of astronomical objects in the spectral range where most of their light is emitted, and access to critical diagnostic spectral lines, all of which point to the need for a far-infrared observatory in space. The objects of interest – circumstellar disks and distant galaxies – typically appear in the sky at sub-arcsecond scales, which rendered all but a few of them unresolvable with the successful and now-defunct 3.5-m Herschel Space Observatory, the largest far-infrared telescope flown to date. A far-infrared interferometer with maximum baseline length in the tens of meters would match the angular resolution of JWST at 10x longer wavelengths and observe water ice and water-vapor emission, which ALMA can barely do through the Earth’s atmosphere. Such a facility was conceived and studied two decades ago. Here we revisit the science case for a space-based far-infrared interferometer in the era of JWST and ALMA and summarize the measurement capabilities that will enable the interferometer to achieve a set of compelling scientific objectives. Common to all the science themes we consider is a need for sub-arcsecond image resolution.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.023 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.015 |
| 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".