Attenuated Alpha Backgrounds in the DEAP-3600 Dark Matter Search Experiment
Bibliographic record
Abstract
DEAP-3600 is a dark matter experiment using 3.3 tonnes of liquid argon as a scintillation target to directly detect Weakly Interacting Massive Particles (WIMPs), a dark matter candidate.Mitigating background sources is crucial to dark matter searches.A large background model contribution comes from attenuated alphas originating from 210 Po decays within the acrylic vessel surfaces.Alphas from decays within the acrylic inner vessel and from the acrylic neck flowguide are analyzed.The activity of the inner vessel is separated into surface and bulk components, and determined to be 0.22 ± 0.02 mBq/m 2 and 3.68 ± 0.06 mBq.An event rate of 53.5 +30 -4.6 µHz is found for alphas originating from the neck flowguide.An optimized event selection is obtained, making use of machine-learning algorithms to reject neck flowguide alphas and maximize WIMP sensitivity.In 802 live-days of DEAP-3600 data, the expected upper limit on the spin-independent WIMP-nucleon interaction cross-section is 1.9×10 -45 cm 2 (90% C.L.) for a 100 GeV/c 2 WIMP mass.D&D nights and mid-day Catan sessions kept me alive during this entire process, and this thesis would likely sound like the words of a madman without all three of you.Most importantly, I want to thank my parents.I am incredibly lucky to have such supportive and caring parents, who were there for me through this whole process.You taught me how to put my full effort into my work, never give up, and be proud of my accomplishments.That knowledge is the most important lesson I have learned in my life.I will always be grateful for your unconditional love.And to Shawna.It is incredible to me that you can somehow manage your own Masters, a full time job, and all of my nonsense, but you do, and I am grateful every day that you are with me.You are the rock in my life, and the first person I go to when I am excited or nervous.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".