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Record W4310051351 · doi:10.1107/s1600577522010347

The PERCIVAL detector: first user experiments

2022· article· en· W4310051351 on OpenAlexaff
Jonathan Correa, Masoud Mehrjoo, Riccardo Battistelli, Felix Lehmkühler, A. Marras, C. Wunderer, T. Hirono, V. Felk, F. Kriváň, J. S. Lange, I. Shevyakov, Vahagn Vardanyan, M. Zimmer, Moritz Hoesch, Kai Bagschik, N. Guerrini, B. Marsh, I. Sedgwick, G. Cautero, L. Stebel, D. Giuressi, R.H. Menk, A.L. Greer, T. Nicholls, William F. Nichols, U. Pedersen, Polad M. Shikhaliev, N. Tartoni, H.J. Hyun, S. H. Kim, S. Y. Park, K. S. Kim, F. Orsini, F. J. Iguaz, Felix Büttner, Bastian Pfau, Elke Plönjes, Konstantin Kharitonov, Mabel Ruiz-Lopez, Rui Pan, Seung-gi Gang, Barbara Keitel, H. Graafsma

Bibliographic record

VenueJournal of Synchrotron Radiation · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsUniversity of Saskatchewan
FundersDeutsches Elektronen-SynchrotronHorizon 2020 Framework ProgrammeHelmholtz Association
KeywordsDetectorOpticsPtychographyHolographyPhysicsFrame ratePixelPhotonSpectroscopyDynamic rangeSpeckle patternSample (material)Frame (networking)Resolution (logic)Materials scienceDiffractionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The PERCIVAL detector is a CMOS imager designed for the soft X-ray regime at photon sources. Although still in its final development phase, it has recently seen its first user experiments: ptychography at a free-electron laser, holographic imaging at a storage ring and preliminary tests on X-ray photon correlation spectroscopy. The detector performed remarkably well in terms of spatial resolution achievable in the sample plane, owing to its small pixel size, large active area and very large dynamic range; but also in terms of its frame rate, which is significantly faster than traditional CCDs. In particular, it is the combination of these features which makes PERCIVAL an attractive option for soft X-ray science.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.265
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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