Capturing ultra-broadband complex-fields of arbitrary duration using a real-time spectrogram
Bibliographic record
Abstract
One of the most intuitive representations of a waveform is achieved through time-frequency analysis, which depicts how the frequency components of a wave evolve over time. Time-frequency representations, such as the spectrogram, are well-known for allowing full-field characterization of a signal in terms of amplitude and phase. However, present methods to capture the spectrogram of a waveform are only suited for either relatively slow ( THz bandwidth) waveforms of short duration. It remains very challenging to capture the time-frequency representation of broadband waves extending over long durations, as required for many important fields in science and technology. Here, we introduce a linear optics temporal imaging concept based on electro-optic time-lensing and dispersive propagation to map the 2D spectrogram as a 1D waveform along the temporal domain. This technique enables ultra-broadband spectrogram analysis without any gaps in the acquisition and with no inherent limitation on maximum signal duration. The spectrogram is captured at unmatched processing rates, up to 16 × 109 Fourier transforms per second (∼60 ps per spectral frame), using a single photodetector and in a fully self-referenced manner. Under certain conditions, we show how this method enables the single-shot full-field characterization of optical waveforms spanning multiple THz. The method is further showcased through accurate amplitude and phase recovery of high-speed complex-modulated optical telecommunication signals using direct intensity detection. This concept will enable the study of physical phenomena unreachable to date and disruptive advancements in high-speed communications, sensing, and information processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".