Characterization of the eLine ASICs in prototype detector systems for LCLS
Bibliographic record
Abstract
"eLine", a class of multichannel time-variant integrating front-end Application Specific Integrated Circuits (ASICs), has been completed at SLAC National Accelerator Laboratory for applications at the Linac Coherent Light Source (LCLS). The class, designed for pixelated sensors with column-parallel readout, is composed of two front-end ASICs: one designed for high-dynamic range applications (eLine10k) and one designed for ultra-low noise applications (eLine100). The first allows large input full-scale signals, on the order of 1048keV photons, with a resolution of half a photon FWHM; while the second provides low noise charge integration, up to a full-scale signal of 100 8keV photons, with an equivalent noise charge (ENC) of 55e- r.m.s. Three different prototype systems utilizing the ASICs are described. The first is a 32k-pixel X-ray Active Matrix Pixel Sensor (XAMPS) detector developed at Brookhaven National Laboratory (BNL) for the X-ray Pump Probe instrument (XPP) at LCLS. The XAMPS are monolithic detectors with fast-frame readout and large full-scale signal. In particular, they provide a full well capacity on the order of 1048keV photons per pixel and a resolution of half a photon FWHM. The second prototype, developed around eLine10k, is a beam finder with high dynamic range. The third prototype is developed around eLine100 to be used as detector in a spectrometer. Applications, test results and performance are discussed.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".