Large solid angle and high detection efficiency multi-element silicon drift detectors (SDD) for synchrotron based x-ray spectroscopy
Bibliographic record
Abstract
Third and fourth generation light sources have revolutionized the research in many scientific and technological disciplines. New scientific challenges impose the construction of cutting-edge performance machines and experimental stations. In this context, off-the-shelf detection systems severely constrain the achievable results. These reasons motivated the ReDSoX research project, aiming to explore new solutions related to energy resolving imagers based on Silicon Drift Detectors (SDD), which are among the most employed acquisition devices in X-ray fluorescence spectroscopy. The main goal of the project is to develop novel versatile detection systems able to cover a large photoemission solid angle, being easily adaptable to the needs of different X-ray spectroscopy beamlines and ready to cope with high photon count-rates in order to exploit all the power of new light sources. Research efforts yielded two detector systems, dedicated to different experimental needs. The first system is composed of 32 SDD elements arranged on 4 monolithic sensors and covers a total non-collimated area of 1230 mm2. Such device is optimized for detecting low-energy photons in the 200 – 4000 eV energy range. The second detector consists of a matrix of 64 SDD elements disposed on 8 monolithic arrays covering an overall non-collimated active area of 576 mm2 and operating over an energy range between 4 and 30 keV. Both systems are highly integrated and can either be operated as an apparent large area single detector or as a multi-element detector, collecting information separately from each single element in order to enable spatially and angularly resolved advanced studies. The performances of the two detector systems have been studied at the TwinMic and XAFS beamlines (Elettra Sincrotrone Trieste, Italy), respectively. Recent results obtained during these measurements are presented and discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".