AKARI and IRAS: From beam corrections to SEDs
Bibliographic record
Abstract
Abstract There is significant scientific value to be gained from combining AKARI fluxes with data at other far-infrared (IR) wavelengths from the Infrared Astronomical Satellite (IRAS) and Herschel missions. To be able to do this we must ensure that there are no systematic differences between the data sets that need to be corrected before the fluxes are compatible with each other. One such systematic effect identified in the Bright Source Catalog version 1 (BSCv1) data is the issue of beam corrections. We determine these for the BSC version 2 (BSCv2) data by correlating ratios of appropriate IRAS and AKARI bands with the difference in 2 Micron All Sky Survey (2MASS) J-band extended and point source magnitudes for sources cross-matched between the IRAS Faint Source Catalog (FSC), AKARI BSCv2 and 2MASS catalogs. We find significant correlations (p ≪ 10 −13) indicating that beam corrections are necessary in the 65 and 90 μm bands. We then use these corrected fluxes to supplement existing data in spectral energy distribution (SED) fits for ultraluminous infrared galaxies (ULIRGs) in the Herschel ULIRG Survey (HERUS). The addition of AKARI fluxes makes little difference to the results of simple (T, β) fits to the SEDs of these sources, though there is a general decrease in reduced χ2 values. The utility of the extra AKARI data, however, is in allowing physically more realistic SED models with more parameters to be fitted to the data. We also extend our analysis of beam correction issues in the AKARI data by examining the Herschel Reference Sample (HRS) galaxies, which have Herschel photometry from 100 to 500 μm and which are more spatially extended than the HERUS ULIRGs. 34 of the HRS sources have good Herschel SEDs and matching data from AKARI. This investigation finds that our simple 2MASS-based beam correction scheme is inadequate for these larger and more complex sources. There are also indications that additional beam corrections at 140 and 160 μm are needed for these sources, extended on scales >1′.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".