Low Background Neutron Counters for HALO-1KT
Bibliographic record
Abstract
The last supernova near our galaxy was in 1987. HALO-1kT will be a low background galactic Supernova detector that uses 1kT of lead as the target for supernova neutrinos and helium-3 neutron counters to detect neutrons produced from the neutrino-lead interactions. The neutrons are then effectively captured by He-3 and converted to electrical signals by the proportional counters. As with many experiments that want to detect particles coming from space, HALO- 1kT needs to have low ”backgrounds”. The term backgrounds refers to any ambient particles, or noise, which are unrelated to the supernova signal that the counters could pick up. Some backgrounds cannot be controlled, like the amount of neutrons in the lab at any given time, but there are backgrounds that can be minimized, like choosing the lowest background materials possible for building the counters. As HALO-1kT is planned to have 4.3 km of helium-3 counters, they need to have as low backgrounds as possible. My research is testing the protype proportional counters to make sure their backgrounds are low enough to avoid regular false positives. The first way of testing them was to take the 4 counters underground at SNOLAB to collect 3-4 months of data as well as a 2-day calibration run to see what the base background rate is. After that two of counters were attached to electrostatic counters and counted for two months to determine the background of the wall material. Initial background rates show the prototype counters have 50x too much background. The ongoing research will help narrow down which part of the counters those backgrounds are coming from so that the materials used to make the actual counters will be cleaner allowing the counters to meet the background goals.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".