The<i>Herschel</i>Multi-tiered Extragalactic Survey: HerMES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Herschel Multi-tiered Extragalactic Survey (HerMES) is a legacy programme designed to map a set of nested fields totalling ∼380 deg2. Fields range in size from 0.01 to ∼20 deg2, using the Herschel-Spectral and Photometric Imaging Receiver (SPIRE) (at 250, 350 and 500 μm) and the Herschel-Photodetector Array Camera and Spectrometer (PACS) (at 100 and 160 μm), with an additional wider component of 270 deg2 with SPIRE alone. These bands cover the peak of the redshifted thermal spectral energy distribution from interstellar dust and thus capture the reprocessed optical and ultraviolet radiation from star formation that has been absorbed by dust, and are critical for forming a complete multiwavelength understanding of galaxy formation and evolution. The survey will detect of the order of 100 000 galaxies at 5σ in some of the best-studied fields in the sky. Additionally, HerMES is closely coordinated with the PACS Evolutionary Probe survey. Making maximum use of the full spectrum of ancillary data, from radio to X-ray wavelengths, it is designed to facilitate redshift determination, rapidly identify unusual objects and understand the relationships between thermal emission from dust and other processes. Scientific questions HerMES will be used to answer include the total infrared emission of galaxies, the evolution of the luminosity function, the clustering properties of dusty galaxies and the properties of populations of galaxies which lie below the confusion limit through lensing and statistical techniques. This paper defines the survey observations and data products, outlines the primary scientific goals of the HerMES team, and reviews some of the early results.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it