Simulating the Spitzer mid-IR color-color diagrams
Bibliographic record
Abstract
We use a simple parameterization of the mid-IR spectra of a wide range of galaxy types in order to predict their distribution in the Infrared Array Camera (IRAC) 3.6um, 4.5um, 5.8um, and 8.0um and Multiband Photometer for Spitzer (MIPS) 24um color-color diagrams. We distinguish three basic spectral types by the energetically dominant component in the 3--12 micron regime: stellar-dominated; PAH-dominated; and continuum-dominated. We have used a Markov chain Monte Carlo (MCMC) approach to arrive at a more systematic and robust representation of the mid-IR spectra of galaxies than more traditional approaches. We find that IRAC color-color plots are well suited to distinguishing the above spectral types, while the addition of 24 micron data allows us to suggest practical 3-color cuts which preferentially select higher redshift sources of specific type. We compare our simulations with the color-color plot obtained by the Spitzer First Look Survey (FLS) and find reasonable agreement. Lastly, we discuss other applications as well as future directions for this work.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".