Background, calibration, and validation simulations for SBC dark matter and reactor CEvNS experiments
Bibliographic record
Abstract
The Scintillating Bubble Chamber (SBC) Collaboration plans to use liquid noble bubble chambers in rare event searches such as nuclear recoils from dark matter and neutrino scattering. Bubble chambers are particle detectors that use liquid above its boiling point in a "superheated" state in order to detect particle interactions. When a particle deposits energy inside the detector, the liquid boils, and a bubble forms. Liquid noble bubble chambers have unique potential as a scalable, low-background, low-energy nuclear recoil detector. Dark matter refers to yet undetected matter that is predicted to comprise most of the mass in the universe based on astronomical observations. SBC has plans to operate reactor neutrino experiments in the future. With the goal of being sensitive to 100 eV nuclear recoils, there is considerable potential for discovery for these experiments. SBC is constructing two 10-kg liquid argon bubble chambers, one to be deployed at Fermi National Accelerator Lab (Fermilab) and the other to be deployed in the SNOLAB underground facility (an expansion of the Sudbury Neutrino Observatory). The Fermilab chamber is a full-scale prototype designed for tests and calibrations, including a novel gamma nucleus elastic scattering calibration. The calibration is required to infer a dark matter sensitivity of the detector. The chamber at SNOLAB will conduct a dark matter search deep-underground with low cosmic radiation. This thesis focuses on the gamma-ray backgrounds and calibration for the SBC experiment. This includes simulations of inner shell electron ionization and nuclear recoils from gamma-rays to understand the detection of environmental radiation and predict the rate at which this radiation is detected. These inner shell ionization simulations helped to confirm a mechanism for electronic recoil backgrounds previously observed in PICO chambers and has implications for future experiments. I developed a high statistics method for simulating nuclear recoils from gamma-rays to inform the shielding design of SBC and design a calibration plan. A simulated calibration is performed to determine the best calibration sources to use and predict the accuracy that can be achieved with a realistic calibration campaign. SBC has a target of 20% or lower uncertainty on the detector threshold for a near-term dark matter search and a target of 5% or lower uncertainty on the detector threshold for a reactor neutrino experiment. The results from the simulated calibration suggest both targets are achievable and estimate the systematic uncertainties required to meet those targets.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".