Studies on the temperature dependent drift velocity for the HELIX Drift Chamber Tracker
Bibliographic record
Abstract
HELIX (High Energy Light Isotope eXperiment) is a ballon experiment designed to measure abundance of cosmic ray isotopes from hydrogen to neon, with a particular interest in abundances of beryllium isotopes. HELIX aim to provide essential data to study the cosmic ray propagation in our galaxy. The Drift Chamber Tracker (DCT) in HELIX is a multi-wire gas drift chamber designed to measure the position of incident cosmic rays. It is located inside a magnet, bending the trajectory of incoming particles through 72-layers of tracking, enabling the measurement of the momentum of incoming particles.Before the data can be used for scientific analysis, the background must be taken out, and the instrument must be well calibrated. In order to exclude electronic noise from the DCT data it was necessary to determine the maximum drift velocity of each wire over a short period of time. To do this I developed an algorithm to be able to identify the maximum drift velocity for each wire over each dataset. This revealed a position dependence of the data within the detector. In order to attempt to characterize this dependence, so it can be accounted for in the calibration stage, I preformed a study on the temperature dependence of the drift velocities analyzing at data from different time periods over the course of the flight. To characterize this dependence I built a predicted heat map of the gas within the detector over the course of the flight. This involved calibrating the thermistors based on data collected when the experiment was on the ground. This helped confirm that the predicted temperature was reasonable and that the variation was temperature dependent. This algorithms and this study can now be applied to the full data set to develop a calibration method.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".