ALMA SPECTROSCOPIC SURVEY IN THE HUBBLE ULTRA DEEP FIELD: SURVEY DESCRIPTION
Bibliographic record
Abstract
ABSTRACT We present the rationale for and the observational description of ASPECS: the ALMA SPECtroscopic Survey in the Hubble Ultra-Deep Field (UDF), the cosmological deep field that has the deepest multi-wavelength data available. Our overarching goal is to obtain an unbiased census of molecular gas and dust continuum emission in high-redshift ( z > 0.5) galaxies. The ∼1′ region covered within the UDF was chosen to overlap with the deepest available imaging from the Hubble Space Telescope . Our ALMA observations consist of full frequency scans in band 3 (84–115 GHz) and band 6 (212–272 GHz) at approximately uniform line sensitivity ( 2 × 10 9 K km s −1 pc 2 ), and continuum noise levels of 3.8 μ Jy beam −1 and 12.7 μ Jy beam −1 , respectively. The molecular surveys cover the different rotational transitions of the CO molecule, leading to essentially full redshift coverage. The [C ii ] emission line is also covered at redshifts . We present a customized algorithm to identify line candidates in the molecular line scans and quantify our ability to recover artificial sources from our data. Based on whether multiple CO lines are detected, and whether optical spectroscopic redshifts as well as optical counterparts exist, we constrain the most likely line identification. We report 10 (11) CO line candidates in the 3 mm (1 mm) band, and our statistical analysis shows that <4 of these (in each band) are likely spurious. Less than one-third of the total CO flux in the low- J CO line candidates are from sources that are not associated with an optical/NIR counterpart. We also present continuum maps of both the band 3 and band 6 observations. The data presented here form the basis of a number of dedicated studies that are presented in subsequent papers.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.006 | 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".