ULTRASAT: A Wide-field Time-domain UV Space Telescope
Bibliographic record
Abstract
Abstract The Ultraviolet Transient Astronomy Satellite (ULTRASAT) is scheduled to be launched to geostationary orbit in 2027. It will carry a telescope with an unprecedentedly large field of view (204 deg2) and near-ultraviolet (NUV; 230–290 nm) sensitivity (22.5 mag, 5σ, at 900 s). ULTRASAT will conduct the first wide-field survey of transient and variable NUV sources and will revolutionize our ability to study the hot transient Universe. It will explore a new parameter space in energy and timescale (months-long light curves with minutes cadence), with an extragalactic volume accessible for the discovery of transient sources that is >300 times larger than that of the Galaxy Evolution Explorer (GALEX) and comparable to that of the Vera Rubin Observatory’s Legacy Survey of Space and Time. ULTRASAT data will be transmitted to the ground in real time, and transient alerts will be distributed to the community in <15 minutes, enabling vigorous ground-based follow up of ULTRASAT sources. ULTRASAT will also provide an all-sky NUV image to >23.5 AB mag, over 10 times deeper than the GALEX map. Two key science goals of ULTRASAT are the study of mergers of binaries involving neutron stars, and supernovae. With a large fraction (>50%) of the sky instantaneously accessible, fast (minutes) slewing capability, and a field of view that covers the error ellipses expected from gravitational-wave (GW) detectors beyond 2026, ULTRASAT will rapidly detect the electromagnetic emission following binary neutron star/neutron star–black hole mergers identified by GW detectors, and will provide continuous NUV light curves of the events. ULTRASAT will provide early (hour) detection and continuous high-cadence (minutes) NUV light curves for hundreds of core-collapse supernovae, including for rarer supernova progenitor types.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".