SuperSpec: development towards a full-scale filter bank
Bibliographic record
Abstract
SuperSpec is a new spectrometer-on-a-chip technology for submm/mm-wave spectroscopy. SuperSpec stands out from other direct-detection submm spectrometer technologies in that the detectors are coupled to a series of resonant filters along a single microwave feedline instead of using dispersive optics. SuperSpec makes use of kinetic inductance detectors (KIDs) to detect radiation in this filter bank. The small profile of this design makes SuperSpec a natural choice to produce a multi-object spectrometer for tomographic mapping or galaxy redshift surveys. We have recently fabricated a device that is a 50 channel subset of a full 280 channel filter bank, which would cover the 190 - 310 GHz range at R = 275. Analysis of the data from this device informs us of the potential design modifications to enable a high-yield background-limited SuperSpec spectrometer. The results indicate that this subset filter bank can scale up to a full filter bank with only a few collisions in readout space and less than 20% variation in responsivity for the detectors. Additionally, the characterization of this and other prototype devices suggests that the noise performance is limited by generation-recombination noise. Finally, we find that the detectors are sufficiently sensitive for ground-based spectroscopy at R = 100, appropriate for tomographic mapping experiments. Further modifications are required to reach the background limit for R = 400, ideal for spectroscopy of individual galaxies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".