The Effectiveness of Mid IR / Far IR Blind, Wide Area, Spectral Surveys in Breaking the Confusion Limit
Bibliographic record
Abstract
Abstract Source confusion defines a practical depth to which to take large-area extragalactic surveys. 3D imaging spectrometers with positional as well as spectral information, however, potentially provide a means by which to use line emission to break the traditional confusion limit. In this paper we present the results of our investigation into the effectiveness of mid/far infrared, wide-area spectroscopic surveys in breaking the confusion limit. We use SAFARI, a FIR imaging Fourier Transform Spectrometer concept for the proposed JAXA-led SPICA mission, as a test case. We generate artificial skies representative of 100 SAFARI footprints and use a fully-automated redshift determination method to retrieve redshifts for both spatially and spectrally confused sources for bright-end and burst mode galaxy evolution models. We find we are able to retrieve accurate redshifts for 38/54% of the brightest spectrally confused sources, with continuum fluxes as much as an order of magnitude below the 120 $\mu$m photometric confusion limit. In addition we also recover accurate redshifts for 38/29% of the second brightest spectrally confused sources. Our results suggest that deep, spectral line surveys with SAFARI can break the traditional photometric confusion limit, and will also not only resolve, but provide redshifts for, a large number of previously inaccessible galaxies. To conclude we discuss some of the limitations of the technique, as well as further work.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".