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Record W2339940431 · doi:10.1107/s1600577516000114

First experimental feasibility study of VIPIC: a custom-made detector for X-ray speckle measurements

2016· article· en· W2339940431 on OpenAlexaff
Abdul K. Rumaiz, D. P. Siddons, G. Deptuch, P. Maj, A. Kuczewski, G. Carini, Suresh Narayanan, Eric M. Đufresne, Alec Sandy, Robert S. Bradford, Andrei Fluerasu, Mark Sutton

Bibliographic record

VenueJournal of Synchrotron Radiation · 2016
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcGill University
FundersFermilabArgonne National LaboratoryOffice of ScienceU.S. Department of Energy
KeywordsDetectorMicrosecondChipPixelOpticsPhoton countingPhysicsSpectroscopyPhotonSpeckle patternMaterials scienceOptoelectronicsComputer science

Abstract

fetched live from OpenAlex

The Vertically Integrated Photon Imaging Chip (VIPIC) was custom-designed for X-ray photon correlation spectroscopy, an application in which occupancy per pixel is low but high time resolution is needed. VIPIC operates in a sparsified streaming mode in which each detected photon is immediately read out as a time- and position-stamped event. This event stream can be fed directly to an autocorrelation engine or accumulated to form a conventional image. The detector only delivers non-zero data (sparsified readout), greatly reducing the communications overhead typical of conventional frame-oriented detectors such as charge-coupled devices or conventional hybrid pixel detectors. This feature allows continuous acquisition of data with timescales from microseconds to hours. In this work VIPIC has been used to measure X-ray photon correlation spectroscopy data on polystyrene latex nano-colliodal suspensions in glycerol and on colloidal suspensions of silica spheres in water. Relaxation times of the nano-colloids have been measured for different temperatures. These results demonstrate that VIPIC can operate continuously in the microsecond time frame, while at the same time probing longer timescales.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.279

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 studies0.0000.000
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 head0.060
GPT teacher head0.364
Teacher spread0.304 · 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 designBench or experimental
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

Citations12
Published2016
Admission routes1
Has abstractyes

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