Searching for Light Dark Matter with a Spherical Proportional Counter
Bibliographic record
Abstract
The vast majority of mass in the universe is comprised of an unknown form of matter – Dark Matter. The NEWS-G collaboration are using a novel gaseous detector, the spherical propor- tional counter, in a search for Dark Matter particles with masses down to sub-GeV. Having set the first exclusion limit on the spin-independent DM-nucleon cross sections for a 0.5 GeV DM particle in 2017, NEWS-G is now turning its focus to it’s new detector, SNOGLOBE. The 140 cm-diameter spherical proportional counter was constructed and commissioned in France and has now been shipped to SNOLAB, Canada, for a direct DM search. \nFor SNOGLOBE to achieve it’s physics potential, several developments are required, in- cluding the understanding of the detector, the properties of gases, background suppression techniques and the physics potential of future experiments. Developments in the spherical proportional counter read-out technology are presented, which uses high-resistivity electrodes to improve stability and energy resolution. The multi-anode sensor, ACHINOS, enables the operation of larger detectors at higher pressures. A simulation framework for the spherical proportional counter has also been developed, which is an important tool for understanding how the detector operates. Another critical component to understanding the operation of the detector when looking for low-energy nuclear recoils induced by DM interactions is the ionisation quenching factor. Measurements of this in gases are scarce, and so a method to calculate this from measurements of the W-value has been developed and applied to several gases. The suppression of radioactive backgrounds is of paramount importance for future NEWS-G spherical proportional counters, and all rare-event search experiments. A method for producing highly radiopure copper is electroforming, which has been used to apply a layer to SNOGLOBE’s inner surface and suppress experimental backgrounds. The application of this technique to produce future NEWS-G detectors is discussed, along with their physics potential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".