DustPedia: Multiwavelength photometry and imagery of 875 nearby galaxies in 42 ultraviolet-microwave bands
Bibliographic record
Abstract
Aims. The DustPedia project is capitalising on the legacy of the Herschel Space Observatory, using cutting-edge modelling techniques to study dust in the 875 DustPedia galaxies – representing the vast majority of extended galaxies within 3000 km s -1 that were observed by Herschel . This work requires a database of multiwavelength imagery and photometry that greatly exceeds the scope (in terms of wavelength coverage and number of galaxies) of any previous local-Universe survey. Methods. We constructed a database containing our own custom Herschel reductions, along with standardised archival observations from GALEX, SDSS, DSS, 2MASS, WISE, Spitzer , and Planck . Using these data, we performed consistent aperture-matched photometry, which we combined with external supplementary photometry from IRAS and Planck . Results. We present our multiwavelength imagery and photometry across 42 UV-microwave bands for the 875 DustPedia galaxies. Our aperture-matched photometry, combined with the external supplementary photometry, represents a total of 21 857 photometric measurements. A typical DustPedia galaxy has multiwavelength photometry spanning 25 bands. We also present the Comprehensive & Adaptable Aperture Photometry Routine (CAAPR), the pipeline we developed to carry out our aperture-matched photometry. CAAPR is designed to produce consistent photometry for the enormous range of galaxy and observation types in our data. In particular, CAAPR is able to determine robust cross-compatible uncertainties, thanks to a novel method for reliably extrapolating the aperture noise for observations that cover a very limited amount of background. Our rich database of imagery and photometry is being made available to the community.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".