Spectra and Polarisation In Cutouts of Extragalactic sources from RACS Second Data Release (SPICE-RACS-DR2)
Bibliographic record
Abstract
Spectra and Polarisation In Cutouts of Extragalactic sources from RACS (SPICE-RACS) is a project to derive an all-Southern-sky, linear polarisation map from the Rapid ASKAP Continuum Survey (RACS). Through this project, we will derive the following products: • Cutout image cubes in Stokes I, Q, and U around every source detected in total intensity as a function of frequency • Polarisation spectra as a function of frequency and Faraday depth for every component detected in total intensity • A catalogue summarising the polarisation properties, including rotation measure (RM), for every component. • Catalogues that provide the source and component positions in total intensity. Note that the total intensity catalogues should be treated as a reference for source positions in the images, and approximate source properties. These are not processed to the same quality standard as the forthcoming RACS-low3 catalogue (Galvin et al. in prep). This collection is laid out as follows: / ├── catalogues/ │ └── ... ├── cubelets/ │ ├── {FIELD_NAME}_{SBID}_cubelets.tar.gz │ └── ... ├── skymaps/ │ ├── spice-racs.dr2.{column name}_{interpolation}.fits │ └── ... └── spectra/ ├── {FIELD_NAME}_{SBID}_spectra.tar.gz └── ... Catalogues: - RACS-low3_INITIAL_components.fits - RACS-low3_INITIAL_sources.fits - spice-racs.dr2.fits.gz Key identifier columns: The following columns can be used to match across the catalogues, and are also used to structure the data below. • Scheduling Block: 'sbid' in the spice-racs catalogue, and 'SBID' in the source and component catalogues. • Field or Tile: 'tile_id' in the spice-racs catalogue, and 'Field_ID' in the source and component catalogues. • PyBDSF Gaussian component: 'cat_id' in the spice-racs catalogue, and 'Gaussian_ID' component catalogue. • PyBDSF Source: 'source_id' in the spice-racs catalogue, and 'Source_ID' in the source and component catalogues. Basic subsets: From the SPICE-RACS DR2 paper, S4.1: "Our catalogue contains a row for every source detected in total intensity. We must therefore stress that not every reported row will correspond to a component with a reliable detection of polarised flux. Rather, we have retained all possible values so that users in the community can make their own cuts on the catalogue to satisfy their own needs and balance of completeness vs. correctness. To assist users of the catalogue, we have again defined some basic subsets that should be reasonable, although conservative, for most science cases. These are as follows: • goodI: Where the stokesI_fit_flag and channel_flag columns are False. • goodRM: goodI, and where leakage_flag and snr_flag are False." Cubelets: {tile_id}_{sbid}/ ├── {source_id stem} │ ├── {source_id}.SB{sbid}.cutout.{image type}.restored.{Stokes}.{tile_id}.contcube.linmos.{ion type}.fits │ └── ... └── ... • 'image type' can be 'image' or 'weight' • 'Stokes' can be 'i', 'q', or 'u' • ion type can be 'ion' or left out of the file name Spectra: {tile_id}_{sbid}/ ├── {cat_id stem}/ │ ├── {cat_id}.SB{sbid}.{spectra type}.fits │ └── ... └── ... • 'spectra type' can be 'freq', 'fdf', or 'rmsf' Note that a 'stem' for cat_id or source_id e.g. "XYZ_123" will be "XYZ" Sky maps: These are catalogue data interpolated onto sky using either nearest-neighbour (nn) or inverse-distance weighting (idw). All files are HEALPix images with Nside=512. All files are named following: spice-racs.dr2.{column name}_{interpolation}.fits Where 'column name' is the column from the catalogue being interpolated, and interpolation is either 'nn' or 'idw' as above.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.027 |
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".