Herschel reveals a Tdust-unbiased selection of z~ 2 ultraluminous infrared galaxies
Bibliographic record
Abstract
Using Herschel Photodetector Array Camera (PACS) and Spectral and Photometric Imaging Receiver (SPIRE) observations of Lockman Hole-North and Great Observatories Origins Deep Survey-North (GOODS-N) as part of the Herschel Multi-tiered Extragalactic Survey (HerMES) project, we explore the far-infrared (IR) properties of a sample of mid-IR-selected starburst-dominated ultraluminous infrared galaxies (ULIRGs) at z~ 2. The selection of the sample is based on the detection of the stellar bump that appears in the spectral energy distribution of star-forming galaxies at 1.6 μm. We derive robust estimates of infrared luminosities (LIR) and dust temperatures (Td) of the population and find that while the luminosities in our sample span less than an order of magnitude (12.24 ≤ log(LIR/L·) ≤ 12.94), they cover a wide range of dust temperatures (25 ≤Td≤ 62 K). Galaxies in our sample range from those that are as cold as high-z submillimetre galaxies (SMGs) to those that are as warm as optically faint radio galaxies (OFRGs) and local ULIRGs. Nevertheless, our sample has median Td= 42.3 K, filling the gap between SMGs and OFRGs, bridging the two populations. We demonstrate that a significant fraction of our sample would be missed from ground-based (sub)mm surveys (850-1200 μm), showing that the latter introduce a bias towards the detection of colder sources. We conclude that Herschel observations confirm the existence of high-z ULIRGs warmer than SMGs, show that the mid-IR selection of high-z ULIRGs is not Td dependent, reveal a large dispersion in Td of high-z ULIRGs and provide the means to characterize the bulk of the ULIRG population, free from selection biases introduced by ground-based (sub)mm surveys. © 2010 The Authors. Journal compilation © 2010 RAS.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".