GBT/MUSTANG-2 900 resolution imaging of the SZ effect in MS0735.6+7421
Bibliographic record
Abstract
In the course of refactoring the code used to perform model fitting in Orlowski-Scherer et al. 2022, it was discovered that the beam size used was incorrect.MUSTANG-2 uses two concentric Gaussian beam profiles to represent the inner beam and the extended wings (e.g.Romero et al. 2020).For each of the Gaussians, the amplitude of the Gaussian and its full width at half maximum (FWHM) had been swapped.This resulted in incorrect smoothing of the map.We have completely rerun the analysis using the more accurate beam.There are no significant changes to our results.In general, the suppression factors, f , increase by about 1σ from the values quoted in Orlowski-Scherer et al. 2022, but remain consistent with either non-thermal pressure support or a mixture of extremely hot thermal and nonthermal support.In fact, our results are less consistent with purely thermal support, although we still cannot completely rule out pure thermal support.Additionally, we no longer find statistically significant support for an outer profile slope, β 1 , which differs from the X-ray-inferred value from Vantyghem et al. 2014 when not performing time ordered data (TOD) subtraction; this is a very minor change.In this erratum we include updated versions of all the tables and plots that were affected by this bug.In general, using this improved representation of the MUSTANG-2 beam has not changed the results of Orlowski-Scherer et al. (2022).The exact suppression factors have changed slightly, however, and as such we report them here.These new suppression factors should be used instead of those found in Orlowski-Scherer et al. (2022).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".