Using ALMA to resolve the nature of the early star-forming large-scale structure PLCK G073.4−57.5
Bibliographic record
Abstract
Galaxy clusters at high redshift are key targets for understanding matter assembly in the early Universe, yet they are challenging to locate. A sample of more than 2000 high-z candidate structures has been found using Planck’s all-sky submillimetre maps, and a sub-set of 234 have been followed up with Herschel-SPIRE, which showed that the emission can be attributed to large overdensities of dusty star-forming galaxies. As a next step, we need to resolve and characterise the individual galaxies giving rise to the emission seen by Planck and Herschel, and to find out whether they constitute the progenitors of present-day, massive galaxy clusters. Thus, we targeted the eight brightest Herschel-SPIRE sources in the centre of the Planck peak PLCK G073.4−57.5 using ALMA at 1.3 mm, and complemented these observations with multi-wavelength data from Spitzer-IRAC, CFHT-WIRCam in the J and Ks bands, and JCMT’s SCUBA-2 instrument. We detected a total of 18 millimetre galaxies brighter than 0.3 mJy within the 2.4 arcmin2 ALMA pointings, corresponding to an ALMA source density 8–30 times higher than average background estimates and larger than seen in typical “proto-cluster” fields. We were able to match all but one of the ALMA sources to a near infrared (NIR) counterpart. The four most significant SCUBA-2 sources are not included in the ALMA pointings, but we find an 8σ stacking detection of the ALMA sources in the SCUBA-2 map at 850 μm. We derive photometric redshifts, infrared (IR) luminosities, star-formation rates (SFRs), stellar masses (ℳ), dust temperatures, and dust masses for all of the ALMA galaxies. Photometric redshifts identify two groups each of five sources, concentrated around z ≃ 1.5 and 2.4. The two groups show two “red sequences”, that is similar near-IR [3.6] − [4.5] colours and different J − Ks colours. The majority of the ALMA-detected galaxies are on the SFR versus ℳ main sequence (MS), and half of the sample is more massive than the characteristic ℳ* at the corresponding redshift. We find that the z ≃ 1.5 group has total $ \mathrm{SFR}=840^{+120}_{-100}\,{M}_{\odot}\,\mathrm{yr}^{-1} $ and $ \mathcal{M}=5.8^{+1.7}_{-2.4}\,{\times}\,10^{11}\,{M}_{\odot} $, and that the z ≃ 2.4 group has $ \mathrm{SFR}=1020^{+310}_{-170}\,{M}_{\odot}\,\mathrm{yr}^{-1} $ and $ \mathcal{M}=4.2^{+1.5}_{-2.1}\,{\times}\,10^{11}\,{M}_{\odot} $, but the latter group is more scattered in stellar mass and around the MS. Serendipitous CO line detections in two of the galaxies appear to match their photometric redshifts at z = 1.54. We performed an analysis of star-formation efficiencies (SFEs) and CO- and mm-continuum-derived gas fractions of our ALMA sources, combined with a sample of 1 < z < 3 cluster and proto-cluster members, and observed trends in both quantities with respect to stellar masses and in comparison to field galaxies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".