What Is the Nature of Little Red Dots and what Is Not, MIRI SMILES Edition
Bibliographic record
Abstract
Abstract We study 31 little red dots (LRD) detected by JADES/NIRCam and covered by the SMILES/MIRI survey, of which ∼70% are detected in the two bluest MIRI bands and 40% in redder MIRI filters. The median/quartiles redshifts are z = 6.9 5.9 7.7 (55% spectroscopic). The spectral slopes flatten in the rest-frame near-infrared, consistent with a 1.6 μ m stellar bump but bluer than direct pure emission from active galactic nuclei (AGN) tori. The apparent dominance of stellar emission at these wavelengths for many LRDs expedites stellar mass estimation: the median/quartiles are log M ⋆ / M ⊙ = 9.4 9.1 9.7 . The number density of LRDs is 10 −4.0±0.1 Mpc −3 , accounting for 14% ± 3% of the global population of galaxies with similar redshifts and masses. The rest-frame near-/mid-infrared (2–4 μ m) spectral slope reveals significant amounts of warm dust (bolometric attenuation ∼3–4 mag). Our spectral energy distribution modeling implies the presence of <0.4 kpc diameter knots, heated by either dust-enshrouded OB stars or an AGN producing a similar radiation field, obscured by A ( V ) > 10 mag. We find a wide variety in the nature of LRDs. However, the best-fitting models for many of them correspond to extremely intense and compact starburst galaxies with mass-weighted ages 5–10 Myr, very efficient in producing dust, with their global energy output dominated by the direct (in the flat rest-frame ultraviolet and optical spectral range) and dust-recycled emission from OB stars with some contribution from an obscured AGN (in the infrared).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".