Detecting 40K geoneutrinos with LiquidO
Bibliographic record
Abstract
From 25% to 70% of Earth’s internal heat budget is deemed to be generated by the radioactive decay of the so-called heat-producing elements (i.e. U, Th and K). Potassium, the only semi-volatile element among them, seems to show from 10% to 30% of its expected chondritic abundance, making thus uncertain any heat balance estimation. Two theories stand on the possible fate of “missing K”: i) segregation of potassium into the core or ii) loss to space during planetary accretion. No experimental corroboration allows confirmation of these hypotheses yet. As a consequence, our knowledge of Earth’s internal heat budget and its thermal evolution has to rely on compositional models. Direct geoneutrino detection, however, permits to constrain, at least in part, Earth’s radiogenic heat production and its Urey ratio. Unfortunately, present state-of-the-art detection techniques based on Inverse Beta Decay (IBD) on free protons only permit the detection of geoneutrinos having energy above 1.8 MeV, leaving 40K-geoneutrinos (whose endpoint is at 1.3 MeV) impossible to detect. Detection via NC interactions such as elastic scattering has been proposed; however, solar and radioactivity backgrounds remain challenges limiting its feasibility. The novel LiquidO detection technique (Cabrera A. et al. - Neutrino Physics with an Opaque Detector - arXiv:1908.02859 - 2019) allows enabling for the first time the observation of 40K-geoneutrinos. LiquidO opaque detection medium allows for unprecedented particle identification and large loading capabilities for neutrino detection. A clear identification of single positrons event topology is possible upon CC interactions of geoneutrinos. This feature opens the door for the exploitation of loaded isotopes leading to new IBD interactions, making it thus possible to lower the minimum detectable antineutrino energy. A review of possible target candidates able to detect 40K-geoneutrinos will be presented here, together with their IBD cross-section and the corresponding expected signal for four different potential experimental sites. A few novels of possible isotope targets are presented here for the first time. The detection significance and the statistical uncertainty will then be discussed together with a possible methodology for the 40K-geoneutrino signal extraction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".