An Open Source, Iterative Dual-Tree Wavelet Background Subtraction Method Extended from Automated Diffraction Pattern Analysis to Optical Spectroscopy
Bibliographic record
Abstract
Background subtraction is a general problem in spectroscopy often addressed with application-specific techniques, or methods that introduce a variety of implementation barriers such as having to specify peak-free regions of the spectrum. An iterative dual-tree complex wavelet transform-based background subtraction method (DTCWT-IA) was recently developed for the analysis of ultrafast electron diffraction patterns. The method was designed to require minimal user intervention, to support streamlined analysis of many diffraction patterns with complex overlapping peaks and time-varying backgrounds, and is implemented in an open-source computer program. We examined the performance of DTCWT-IA for the analysis of spectra acquired by a range of optical spectroscopies including ultraviolet-visible spectroscopy (UV-Vis), X-ray photoelectron spectroscopy (XPS), and surface-enhanced Raman spectroscopy (SERS). A key benefit of the method is that the user need not specify regions of the spectrum where no peaks are expected to occur. SER spectra were used to investigate the robustness of DTCWT-IA to signal-to-noise levels in the spectrum and to user operation, specifically to two of the algorithm parameter settings: decomposition level and iteration number. The single, general DTCWT-IA implementation performs well in comparison to the different conventional approaches to background subtraction for UV-Vis, XPS, and SERS, while requiring minimal input. The method thus holds the same potential for optical spectroscopy as for ultrafast electron diffraction, namely streamlined analysis of spectra with complex distributions of peaks and varying signal levels, thus supporting real-time spectral analysis or the analysis of data acquired from different sources.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".