Bibliographic record
Abstract
Next generation particle physics experiments are requiring charged particle detection in 4 dimensions at the micron and picosecond scale. Achieving timing resolution better than 100ps with position resolution better than 100 micron is a major challenge. We are proposing to a concept taking advantage of the high timing resolution capabilities of single photon avalanche diodes (SPAD), developed for single photon, i.e. single charged carrier detection. The main drawback of SPADs compared to other technologies for charged particle detection is the inability to separate the signals from a single carrier, thermally produced in particular, from the signals produced by a charged particle generating more than 100 ionization charge carriers. We are proposing a concept for enhancing the number of SPADs that avalanche at the same time per charged particle such that thermal noise can be effectively rejected. This concept relies on allowing a fraction of the charge carrier to diffuse in the silicon hence reaching neighboring SPADs. We assess the signal and noise including the light emission process in avalanches that can mimic the multi-SPAD signal of a charge particle. We investigate this concept for the detection of Minimum Ionizing Particle that go through the all detector and keV scale electrons that stop very close to the surface. We show that this concept is compelling providing that the average number of photons emitted per avalanche is sufficiently low. Such concept could then be implemented as a modified configuration of back-side illuminated SPAD arrays.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".