Sensitivity of nEXO to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:mmultiscripts> <mml:mrow> <mml:mi>Xe</mml:mi> </mml:mrow> <mml:mprescripts/> <mml:none/> <mml:mrow> <mml:mn>136</mml:mn> </mml:mrow> </mml:mmultiscripts> </mml:mrow> </mml:math> charged-current interactions: Background-free searches for solar neutrinos and fermionic dark matter
Bibliographic record
Abstract
We study the sensitivity of nEXO to solar neutrino charged-current interactions, ${\ensuremath{\nu}}_{e}+^{136}\mathrm{Xe}\ensuremath{\rightarrow}\phantom{\rule{0ex}{0ex}}{^{136}\mathrm{Cs}}^{*}+{e}^{\ensuremath{-}}$, as well as analogous interactions predicted by models of fermionic dark matter. Due to the recently observed low-lying isomeric states of $^{136}\mathrm{Cs}$, these interactions will create a time-delayed coincident signal observable in the scintillation channel. Here we develop a detailed Monte Carlo simulation of scintillation emission, propagation, and detection in the nEXO detector to model these signals under different assumptions about the timing resolution of the photosensor readout. We show this correlated signal can be used to achieve background discrimination on the order of ${10}^{\ensuremath{-}9}$, enabling nEXO to make background-free measurements of solar neutrinos above the reaction threshold of 0.668 MeV. We project that nEXO could measure the flux of neutrinos from the carbon-nitrogen-oxygen cycle with a statistical uncertainty of 25%, thus contributing a novel and competitive measurement toward addressing the solar metallicity problem. Additionally, nEXO could measure the mean energy of the $^{7}\mathrm{Be}$ neutrinos with a precision of $\ensuremath{\sigma}\ensuremath{\le}1.5\text{ }\text{ }\mathrm{keV}$ and could determine the survival probability of $^{7}\mathrm{Be}$ and pep solar ${\ensuremath{\nu}}_{e}$ with precision comparable to the state of the art. These quantities are sensitive to the Sun's core temperature and to nonstandard neutrino interactions, respectively. Furthermore, the strong background suppression would allow nEXO to search for charged-current interactions of fermionic dark matter in the mass range ${m}_{\ensuremath{\chi}}=0.668--7\text{ }\text{ }\mathrm{MeV}$ with a sensitivity up to three orders of magnitude better than current limits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".