Bibliographic record
Abstract
The Infrared Astronomical Satellite ( IRAS ) had a tremendous impact on many areas of modern astrophysics. In particular, it revealed the ubiquity of infrared cirrus that are a spectacular manifestation of the interstellar medium complexity but also an important foreground for observational cosmology. With the forthcoming Planck satellite there is a need for all-sky complementary data sets with arcminute resolution that can bring informations on specific foreground emissions that contaminate the cosmic microwave background radiation. With its ∼4' resolution matching perfectly the high-frequency bands of Planck , IRAS is a natural data set to study the variations of dust properties at all scales. But the latest version of the images delivered by the IRAS team (the ISSA plates) suffer from calibration, zero level, and striping problems that can preclude its use, especially at 12 and 25 μm. In this paper we present how we proceeded to solve each of these problems and enhance significantly the general quality of the ISSA plates in the four bands (12, 25, 60, and 100 μm). This new generation of IRAS images, called IRIS, benefits from a better zodiacal light subtraction, from a calibration and zero level compatible with DIRBE, and from a better destriping. At 100 μm the IRIS product is also a significant improvement from the Schlegel et al. maps. IRIS keeps the full ISSA resolution, it includes well-calibrated point sources, and the diffuse emission calibration at scales smaller than 1° was corrected for the variation of the IRAS detector responsivity with scale and brightness. The uncertainty on the IRIS calibration and zero level is dominated by the uncertainty on the DIRBE calibration and on the accuracy of the zodiacal light model.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.041 |
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".