GOODS-<i>Herschel</i>: a population of 24 <i>μ</i>m dropout sources at<i>z</i> < 2
Bibliographic record
Abstract
Using extremely deep PACS 100- and 160 μm Herschel data from the GOODS-Herschel program, we identify 21 infrared bright galaxies previously missed in the deepest 24 μm surveys performed by Spitzer/MIPS. These MIPS dropouts are predominantly found in two redshift bins, centred at z ~ 0.4 and ~1.3. Their S100/S24 flux density ratios are similar to those of local (ultra-) luminous infrared galaxies (LIRGs and ULIRGs), whose silicate absorption features at 18 μm (at z ~ 0.4) and 9.7 μm (at z ~ 1.3) are shifted into the 24 μm MIPS band at these redshifts. The high-z sub-sample consists of 11 infrared luminous sources, accounting for ~2% of the whole GOODS-Herschel sample and putting strong upper limits on the fraction of LIRGs/ULIRGs at 1.0 < z < 1.7 that are missed by the 24 μm surveys. We find that a S100/S24 > 43 colour cut selects galaxies with a redshift distribution similar to that of the MIPS dropouts and when combined with a second colour cut, S16/S8 > 4, isolates sources at 1.0 < z < 1.7. We show that these sources have elevated specific star formation rates (sSFR) compared to main sequence galaxies at these redshifts and are likely to be compact starbursts with moderate/strong 9.7 μm silicate absorption features in their mid-IR spectra. Herschel data reveal that their infrared luminosities extrapolated from the 24 μm flux density are underestimated, on average, by a factor of ~3. These silicate break galaxies account for 16% (8%) of the ULIRG (LIRG) population in the GOODS fields, indicating a lower limit in their space density of 2.0 × 10-5 Mpc-3. Finally, we provide estimates of the fraction of z < 2 MIPS dropout sources as a function of the 24-, 100-, 160-, 250- and 350 μm sensitivity limits, and conclude that previous predictions of a population of silicate break galaxies missed by the major 24 μm extragalactic surveys have beenoverestimated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".