The photon clean method: an event-based approach to analyzing X-ray spectra
Bibliographic record
Abstract
The Photon Clean Method (PCM) is an inverse Monte-Carlo method of spectral fitting that differs from traditional fitting routines found in spectral modeling packages by fitting event lists as opposed to binned spectra. The model spectrum is represented in event form as well. Thus, using this method it is possible to fit data of higher dimensionality than can be fit using binned spectra and standard routines based on Chi-Square statistics, such as event-mode data from electron beam ion traps or satellite observations that are tagged, for example, as a function of time, position, or energy. To demonstrate some of the power of the PCM and aid in its development, we have implemented a simplified one-dimensional version of the PCM algorithm (PCM1D). Using our implementation, which is a command-line program intended for public release, we have performed tests on simulated and observed Chandra ACIS CCD data, and present two examples, one on Cassiopeia A and another on a simulated multitemperature plasma in collisional ionization equilibrium. PACS No.: 52.65.Pp
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".