4th dimensional tracking: the GigaTracker of NA62 experiment.
Bibliographic record
Abstract
The GigaTracker is a lightweight hybrid silicon pixel detector built for the NA62 experiment at CERN, which aims at measuring the branching fraction of the ultra-rare kaon decay $K^+\rightarrow \pi^+\nu\bar{\nu}$ at the CERN SPS. The detector tracks charged particles in a 75 GeV/$c$ hadron beam with a flux reaching 1.3 MHz/mm$^2$. It consists of three stations, 61$\times$27 mm$^2$ each, which provide single-hit timing with 130 ps resolution. Each station is composed of a 200 $\mu$m thick planar silicon sensor, segmented in 300$\times$300 $\mu$m$^2$ pixels, bump-bonded to 2$\times$5 custom 100 $\mu$m thick ASIC, called TDCPix. Each TDCPix contains 40$\times$45 asynchronous pixels, and is instrumented with 360 pairs of time-to-digital converter channels with 100 ps bin. The three stations are installed in vacuum (about 10$^{-6}$ mbar) and cooled with liquid $\mathrm{C_6F_{14}}$ circulating through micro-channels etched inside silicon plates a few hundred microns thick. The total material budget is less than 0.5% $X_0$ per station. Detector description, operational experience and performance from the NA62 experimental run in 2016, at about 30% the nominal beam intensity, will be presented.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".