Towards a multi‐element silicon drift detector system for fluorescence spectroscopy in the soft X‐ray regime
Bibliographic record
Abstract
In spite of the constant technological improvements in the field of detector development, X‐ray fluorescence (XRF) in the soft X‐ray regime remains a challenge. The low intrinsic fluorescence yield for energies below 2 keV indeed renders the applicability of low‐energy XRF still difficult. Here, we report on a new multi‐element multi‐tile detection system currently under development, designed to be integrated into a soft X‐ray microscopy end station. The system will be installed at the TwinMic beamline of Elettra synchrotron (Trieste, Italy) in order to increase the detected count rate by up to an order of magnitude. The new architecture is very versatile and can be adapted to any XRF experimental setup. Even though the first results of the previous version of such a multi‐element system were encouraging, several issues still needed to be addressed. The system described here represents a further step in the detector evolution. It is based on four trapezoidal‐shaped monolithic silicon drift detector tiles (matrices) with six hexagonal elements each equipped with a custom ultra‐low noise application‐specific integrated circuit readout. The whole signal processing chain has been improved leading to an overall increase in performances, namely, in terms of energy resolution and acquisition rates. The design and development of this new detection system will be described, and recent results obtained at the TwinMic beamline at Elettra will be presented. Future perspectives and improvements will also be discussed. Copyright © 2017 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".