Sensor Development and Testing for the NEWS-G Dark Matter Experiment
Bibliographic record
Abstract
The NEWS-G collaboration utilizes Spherical Proportional Counters (SPCs) in direct detection searches for low-mass dark matter. The detectors use gasses with a high voltage sensor in the centre to collect incoming electrons ionized from particle interactions in the gas. The experiment is currently running at SNOLAB and uses its low threshold requirements to search for Weakly Interacting Massive Particles (WIMPs) with sub-GeV mass. To support charge collection from greater distances within the SPC volume, the detector uses a multi-anode achinos sensor. This style of sensor uses a resistive support structure to hold the anodes in place and correct the electric field in the volume of the detector. Previous sensor versions used support structures made from Bakelite or were 3D printed with a Diamond-Like Carbon coating. I worked on further development of the achinos utilizing electrostatic discharge resin to directly 3D print the support structure, allowing for increased precision and production rates. This included adding internal guide tubes to support the wires, reducing pressure points that could cause damage to the wire. These structures were then tested with increasing voltages in an argon gas mixture while being observed for discharges. The sensors were further tested in 30cm diameter sphere to monitor the stability during data-taking runs, leading to design iterations to improve the stability of the sensors and increase the maximum usable voltage. Tests were conducted considering the resistive properties of alternative structure materials, including glass capillary tubes of varying thicknesses, but the resin was further developed for its easier applications to multi-anode structures. I also worked to implement new methods of attaching the metal anodes to insulated wire, including the use of silver conductive paste to strengthen the electrical connection. This was measured to have a resistance of about 10Ω, where previous methods had connections too weak to observe.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".