Estimate of the neutron fields in ATLAS based on ATLAS-MPX detectors data
Bibliographic record
Abstract
The ATLAS-MPX detectors are based on Medipix2 silicon devices designed by CERN for the detection of different types of radiation. These detectors are covered with converting layers of 6 LiF and polyethylene (PE) to increase their sensitivity to thermal and fast neutrons, respectively. These devices allow the measurement of the composition and spectroscopic characteristics of the radiation field in ATLAS, particularly of neutrons. These detectors can operate in low or high preset energy threshold mode. The signature of particles interacting in a ATLAS-MPX detector at low threshold are clusters of adjacent pixels with different size and form depending on their type, energy and incidence angle. The classification of particles into different categories can be done using the geometrical parameters of these clusters. The Medipix analysis framework (MAFalda) — based on the ROOT application — allows the recognition of particle tracks left in ATLAS-MPX devices located at various positions in the ATLAS detector and cavern. The pattern recognition obtained from the application of MAFalda was configured to distinguish the response of neutrons from other radiation. The neutron response at low threshold is characterized by clusters of adjoining pixels (heavy tracks and heavy blobs) left by protons and heavy ions resulting from neutron interactions in the converting layers of the ATLAS-MPX devices. The neutron detection efficiency of ATLAS-MPX devices has been determined by the exposure of two detectors of reference to radionuclide sources of neutrons ( 252 Cf and 241 AmBe). With these results, an estimate of the neutrons fields produced at the devices locations during ATLAS operation was done.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".