Comparison of nonlinear filtering techniques for photonic systems with blackbody radiation
Bibliographic record
Abstract
This work explores a theoretical solution for noise reduction in photonic systems using blackbody radiators. Traditionally, signal noise can be reduced by increasing the integration time during signal acquisition. However, increasing the integration time during signal acquisition will reduce the acquisition speed of the signal. By developing and applying a filter using a model based on the theoretical equations for blackbody radiation, the noise of the signal can be reduced without increasing integration time. In this work, three filters, extended Kalman filter, unscented Kalman filter (UKF), and extended sliding innovation filter (ESIF), are compared for blackbody photonic systems. The filters are tested on a simulated signal from five scenarios, each simulating different experimental conditions. In particular, the nonlinear filters, UKF and ESIF, showed a significant reduction of noise from the simulated signal in each scenario. The results show great promise for photonic systems using blackbody radiators that require post-process for noise reduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".