The WEAVE-TwiLight-Survey: expanding WEAVE’s reach to bright and low-surface-density targets with a novel observing mode
Bibliographic record
Abstract
ABSTRACT Current-day multi-object spectroscopic surveys are often limited in their ability to observe bright stars due to their low surface densities, resulting in increased observational overheads and reduced efficiency. Addressing this, we have developed a novel observing mode for WEAVE (William Herschel Telescope Enhanced Area Velocity Explorer) that enables efficient observations of low-surface-density target fields without incurring additional overheads from calibration exposures. As a pilot for the new mode, we introduce the WEAVE-TwiLight-Survey (WTLS), focusing on bright exoplanet-host stars and their immediate surroundings on the sky. High observational efficiency is achieved by superimposing multiple low-target-density fields and allocating the optical fibres in this configuration. We use a heuristic method to define fields relative to a central guide star, which serves as a reference for their superposition. Suitable guide fibres for each merged configuration are selected using a custom algorithm. Test observations have been carried out, demonstrating the feasibility of the new observing mode. We show that merged field configurations can be observed with WEAVE using the proposed method. The approach minimizes calibration times and opens twilight hours to WEAVE’s operational schedule. WTLS is built upon the new observing mode and sourced from the ESA PLATO long-duration-phase fields. This survey will result in a homogeneous catalogue of ∼6300 bright stars, including 62 known planet hosts, laying the groundwork for future elemental abundance studies tracing chemical patterns of planetary formation. This new observing mode (WEAVE-Tumble-Less) expands WEAVE’s capabilities to rarely used on-sky time and low-density field configurations without sacrificing efficiency.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".