Exploring the keV-scale physics potential of CUORE
Bibliographic record
Abstract
We present the analysis techniques developed to explore the keV-scale energy region of the Cryogenic Underground Observatory for Rare Events (CUORE) experiment, based on more than 2 metric ton yr of data collected over five years. By prioritizing a stricter selection over a larger exposure, we are able to optimize data selection for thresholds at 10 keV and 3 keV with 691 kg yr and 11 kg yr of data, respectively. We study how the performance varies among the 988-detector array with different detector characteristics and data-taking conditions. We achieve an average baseline resolution of <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mn>2.54</a:mn> <a:mo>±</a:mo> <a:mn>0.14</a:mn> <a:mtext> </a:mtext> <a:mtext> </a:mtext> <a:mi>keV</a:mi> </a:math> FWHM and <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:mn>1.18</c:mn> <c:mo>±</c:mo> <c:mn>0.02</c:mn> <c:mtext> </c:mtext> <c:mtext> </c:mtext> <c:mi>keV</c:mi> </c:math> FWHM for the data selection at 10 keV and 3 keV, respectively. The analysis methods employed reduce the overall background by about an order of magnitude, reaching <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"> <e:mrow> <e:mn>2.06</e:mn> <e:mo>±</e:mo> <e:mn>0.05</e:mn> <e:mtext> </e:mtext> <e:mtext> </e:mtext> <e:mi>counts</e:mi> <e:mo>/</e:mo> <e:mo stretchy="false">(</e:mo> <e:mi>keV</e:mi> <e:mtext> </e:mtext> <e:mi>kg</e:mi> <e:mtext> </e:mtext> <e:mi>days</e:mi> <e:mo stretchy="false">)</e:mo> </e:mrow> </e:math> and <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" display="inline"> <i:mrow> <i:mn>16</i:mn> <i:mo>±</i:mo> <i:mn>2</i:mn> <i:mtext> </i:mtext> <i:mtext> </i:mtext> <i:mi>counts</i:mi> <i:mo>/</i:mo> <i:mo stretchy="false">(</i:mo> <i:mi>keV</i:mi> <i:mtext> </i:mtext> <i:mi>kg</i:mi> <i:mtext> </i:mtext> <i:mi>days</i:mi> <i:mo stretchy="false">)</i:mo> </i:mrow> </i:math> at the thresholds of 10 keV and 3 keV. We evaluate for the first time the near-threshold reconstruction efficiencies of the CUORE experiment, and find these to be <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mn>50</m:mn> <m:mo>±</m:mo> <m:mn>2</m:mn> <m:mo>%</m:mo> </m:math> and <o:math xmlns:o="http://www.w3.org/1998/Math/MathML" display="inline"> <o:mn>26</o:mn> <o:mo>±</o:mo> <o:mn>4</o:mn> <o:mo>%</o:mo> </o:math> at 10 keV and 3 keV, respectively. This analysis provides crucial insights into rare decay studies, new physics searches, and keV-scale background modeling with CUORE. We demonstrate that ton-scale cryogenic calorimeters can operate across a wide energy range, from keV to MeV, establishing their scalability as versatile detectors for rare event and dark matter physics. These findings also inform the optimization of future large mass cryogenic calorimeters to enhance the sensitivity to low-energy phenomena.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".