The HASHTAG Project: The First Submillimeter Images of the Andromeda Galaxy from the Ground
Bibliographic record
Abstract
Abstract Observing nearby galaxies with submillimeter telescopes on the ground has two major challenges. First, the brightness is significantly reduced at long submillimeter wavelengths compared to the brightness at the peak of the dust emission. Second, it is necessary to use a high-pass spatial filter to remove atmospheric noise on large angular scales, which has the unwelcome side effect of also removing the galaxy’s large-scale structure. We have developed a technique for producing high-resolution submillimeter images of galaxies of large angular size by using the telescope on the ground to determine the small-scale structure (the large Fourier components) and a space telescope (Herschel or Planck) to determine the large-scale structure (the small Fourier components). Using this technique, we are carrying out the HARP and SCUBA-2 High Resolution Terahertz Andromeda Galaxy Survey (HASHTAG), an international Large Program on the James Clerk Maxwell Telescope, with one aim being to produce the first high-fidelity high-resolution submillimeter images of Andromeda. In this paper, we describe the survey, the method we have developed for combining the space-based and ground-based data, and we present the first HASHTAG images of Andromeda at 450 and 850 μ m. We also have created a method to predict the CO( J = 3–2) line flux across M31, which contaminates the 850 μ m band. We find that while normally the contamination is below our sensitivity limit, it can be significant (up to 28%) in a few of the brightest regions of the 10 kpc ring. We therefore also provide images with the predicted line emission removed.
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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) | 0.003 | 0.002 |
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".