A Monte Carlo Approach to Evolution of the Far-Infrared Luminosity Function with BLAST
Bibliographic record
Abstract
We constrain the evolution of the rest-frame far-infrared (FIR) luminosity function out to high redshift, by combining several pieces of complementary information pro-vided by the deep Balloon-borne Large-Aperture Submillimeter Telescope surveys at 250, 350 and 500µm, as well as other FIR and millimetre data. Unlike most other phenomenological models, we characterise the uncertainties in our fitted parameters using Monte Carlo Markov Chains. We use a bivariate local luminosity function that depends only on FIR luminosity and 60-to-100µm colour, along with a single library of galaxy spectral energy distributions indexed by colour, and apply simple luminosity and density evolution. We use the surface density of sources, Cosmic Infrared Back-ground (CIB) measurements and redshift distributions of bright sources, for which identifications have been made, to constrain this model. The precise evolution of the FIR luminosity function across this crucial range has eluded studies at longer wave-lengths (e.g., using SCUBA and MAMBO) and at shorter wavelengths (e.g., with Spitzer), and should provide a key piece of information required for the study of
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".