A GALACTIC MOLECULAR CLOUD CLUMP CATALOG FROM HI-GAL DATA: METHOD AND INITIAL RESULTS COMPARISON WITH BGPS
Bibliographic record
Abstract
Abstract As the precursors to stellar clusters, it is imperative that we understand the distribution and physical properties of dense molecular gas clouds and clumps. Such a study has been done with the ground-based Bolocam Galactic Plane Survey (BGPS). Now the Herschel infrared GALactic plane survey (Hi-GAL) allows us to do the same with higher-quality data and complete coverage of the Galactic plane. We have made a pilot study comparing dense molecular gas clumps identified in Hi-GAL and BGPS, using six 2° × 2° regions centered at Galactic longitudes of , 30°, 41°, 50°, 202°, and 217°. We adopted the BGPS methodology for identifying clumps and estimating distances, leading to 6198 clumps being identified in our substudy, with 995 of those having well-constrained distances. These objects were evenly distributed with Galactic longitude, a consequence of Hi-GAL being source confusion limited. These clumps range in mass from 10−2 to 105 M ⊙ and have heliocentric distances of up to 16 kpc. When clumps found in both surveys are compared, we see that distances agree within 1 kpc and ratios of masses are of the order of unity. This serves as an external validation for BGPS and instills confidence as we move forward to cataloging the clumps from the entirety of Hi-GAL. In addition to the sources that were in common with BGPS, Hi-GAL found many additional sources, primarily due to the lack of atmospheric noise. We expect Hi-GAL to yield 2 × 105 clumps, with 20% having well-constrained distances, an order of magnitude above what was found in BGPS.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".