Spectral stacking of radio-interferometric data
Bibliographic record
Abstract
Context. Mapping molecular line emission beyond the bright low- J CO transitions is still challenging in extragalactic studies, even with the latest generation of (sub-)millimetre interferometers, such as ALMA and NOEMA. Aims. We summarise and test a spectral stacking method that has been used in the literature to recover low-intensity molecular line emission, such as HCN(1−0), HCO + (1−0), and even fainter lines in external galaxies. The goal is to study the capabilities and limitations of the stacking technique when applied to imaged interferometric observations. Methods. The core idea of spectral stacking is to align spectra of the low S/N spectral lines to a known velocity field calculated from a higher S/N line expected to share the kinematics of the fainter line (e.g. CO(1−0) or 21 cm emission). Then these aligned spectra can be coherently averaged to produce potentially high S/N spectral stacks. Here we used imaged simulated interferometric and total power observations at different S/N levels, based on real CO observations. Results. For the combined interferometric and total power data, we find that the spectral stacking technique is capable of recovering the integrated intensities even at low S/N levels across most of the region where the high S/N prior is detected. However, when stacking interferometer-only data for low S/N emission, the stacks can miss up to 50% of the emission from the fainter line. Conclusions. A key result of this analysis is that the spectral stacking method is able to recover the true mean line intensities in low S/N cubes and to accurately measure the statistical significance of the recovered lines. To facilitate the application of this technique we provide a public Python package, called P Y S TACKER .
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".