Unfolding the low-energy reactor neutrino flux from CEνNS data with a finite Dirac sum

@misc{chen2026unfolding,
  title = {{Unfolding the low-energy reactor neutrino flux from CE$ν$NS data with a finite Dirac sum}},
  author = {Muping Chen and Graciela Gelmini and Danny Marfatia and Koichiro Yasuda},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2608.16107},
  eprint = {2608.16107},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.16107},
}

TY  - UNPB
TI  - Unfolding the low-energy reactor neutrino flux from CEνNS data with a finite Dirac sum
AU  - Muping Chen
AU  - Graciela Gelmini
AU  - Danny Marfatia
AU  - Koichiro Yasuda
PY  - 2026
DA  - 2026/10/20
PB  - arXiv
DO  - 10.48550/arxiv.2608.16107
UR  - https://arxiv.org/abs/2608.16107
AB  - We present a novel method to analyze coherent elastic neutrino--nucleus scattering (CEνNS) data to extract the reactor antineutrino spectrum below the inverse beta decay threshold of 1.8 MeV, where it remains unmeasured. Adapting halo-independent analysis techniques developed for direct dark matter detection, we show how to obtain a best-fit and a pointwise confidence band for the integrated neutrino flux, without assuming a parametric form or smoothness prior for the spectrum. In our approach, which follows from convex geometry arguments, the differential neutrino rate is written as a linear combination of Dirac delta functions -- a finite Dirac sum (FDS) -- with a maximum number of terms determined by the number of data points. We apply our "FDS method" to mock CEνNS data for a low-threshold Ge detector and compare it with Tikhonov-regularized unfolding.
ER  -