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Diffuse supernova neutrino background with up-to-date star formation rate measurements and long-term multidimensional supernova simulations
High Energy Astrophysical Phenomena · Submitted 23 Oct 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In this fiducial calculation, we could see a $3\sigma$ detection by $\sim2030$ with SK-Gd and a $5\sigma$ detection by $\sim2035$ with a joint SK-Gd/JUNO analysis, but background reduction remains crucial.
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The abstract, sentence by sentence
The sensitivity of current and future neutrino detectors like Super-Kamiokande (SK), JUNO, Hyper-Kamiokande (HK), and DUNE is expected to allow for the detection of the diffuse supernova neutrino background (DSNB). However, the DSNB model ingredients like the core-collapse supernova (CCSN) rate, neutrino emission spectra, and the fraction of failed supernovae are not precisely known. We quantify the uncertainty on each of these ingredients by (i) compiling a large database of recent star formation rate density measurements, (ii) combining neutrino emission from long-term axisymmetric CCSNe simulations and strategies for estimating the emission from the protoneutron star cooling phase, and (iii) assuming different models of failed supernovae. Finally, we calculate the fluxes and event rates at multiple experiments and perform a simplified statistical estimate of the time required to significantly detect the DSNB at SK with the gadolinium upgrade and JUNO. Our fiducial model predicts a flux of $5.1\pm0.4^{+0.0+0.5}_{-2.0-2.7}\,{\rm cm^2~s^{-1}}$ at SK employing Gd-tagging, or $3.6\pm0.3^{+0.0+0.8}_{-1.6-1.9}$ events per year, where the errors represent our uncertainty from star formation rate density measurements, uncertainty in neutrino emission, and uncertainty in the failed-supernova scenario. In this fiducial calculation, we could see a $3\sigma$ detection by $\sim2030$ with SK-Gd and a $5\sigma$ detection by $\sim2035$ with a joint SK-Gd/JUNO analysis, but background reduction remains crucial.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The sensitivity of current and future neutrino detectors like Super-Kamiokande (SK), JUNO, Hyper-Kamiokande (HK), and DUNE is expected to allow for the detection of the diffuse supernova neutrino background (DSNB). | background 1.00 | 0.07 | 0.04 | hedged possibility 0.95 |
| s1 | However, the DSNB model ingredients like the core-collapse supernova (CCSN) rate, neutrino emission spectra, and the fraction of failed supernovae are not precisely known. | gap 0.99 | 0.05 | 0.03 | asserted 1.00 |
| s2 | We quantify the uncertainty on each of these ingredients by (i) compiling a large database of recent star formation rate density measurements, (ii) combining neutrino emission from long-term axisymmetric CCSNe simulations and strategies for estimating the emission from the protoneutron star cooling phase, and (iii) assuming different models of failed supernovae. | method 0.96 | 0.93 | 0.04 | asserted 0.99 |
| s3 | Finally, we calculate the fluxes and event rates at multiple experiments and perform a simplified statistical estimate of the time required to significantly detect the DSNB at SK with the gadolinium upgrade and JUNO. | method 0.66 | 0.95 | 0.07 | asserted 0.99 |
| s4 | Our fiducial model predicts a flux of $5.1\pm0.4^{+0.0+0.5}_{-2.0-2.7}\,{\rm cm^2~s^{-1}}$ at SK employing Gd-tagging, or $3.6\pm0.3^{+0.0+0.8}_{-1.6-1.9}$ events per year, where the errors represent our uncertainty from star formation rate density measurements, uncertainty in neutrino emission, and uncertainty in the failed-supernova scenario. | result 1.00 | 0.94 | 0.47 | asserted 0.99 |
| s5 | In this fiducial calculation, we could see a $3\sigma$ detection by $\sim2030$ with SK-Gd and a $5\sigma$ detection by $\sim2035$ with a joint SK-Gd/JUNO analysis, but background reduction remains crucial. | result 0.84 | 0.84 | 0.54 | hedged possibility 0.99 |
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The paper
Original abstract
The sensitivity of current and future neutrino detectors like Super-Kamiokande (SK), JUNO, Hyper-Kamiokande (HK), and DUNE is expected to allow for the detection of the diffuse supernova neutrino background (DSNB). However, the DSNB model ingredients like the core-collapse supernova (CCSN) rate, neutrino emission spectra, and the fraction of failed supernovae are not precisely known. We quantify the uncertainty on each of these ingredients by (i) compiling a large database of recent star formation rate density measurements, (ii) combining neutrino emission from long-term axisymmetric CCSNe simulations and strategies for estimating the emission from the protoneutron star cooling phase, and (iii) assuming different models of failed supernovae. Finally, we calculate the fluxes and event rates at multiple experiments and perform a simplified statistical estimate of the time required to significantly detect the DSNB at SK with the gadolinium upgrade and JUNO. Our fiducial model predicts a flux of $5.1\pm0.4^{+0.0+0.5}_{-2.0-2.7}\,{\rm cm^2~s^{-1}}$ at SK employing Gd-tagging, or $3.6\pm0.3^{+0.0+0.8}_{-1.6-1.9}$ events per year, where the errors represent our uncertainty from star formation rate density measurements, uncertainty in neutrino emission, and uncertainty in the failed-supernova scenario. In this fiducial calculation, we could see a $3\sigma$ detection by $\sim2030$ with SK-Gd and a $5\sigma$ detection by $\sim2035$ with a joint SK-Gd/JUNO analysis, but background reduction remains crucial.
Human review
References
How the literature uses it
7 arXiv papers cite this in 9 sentences
Papers citing it · 5 sentences without a confident label
Uses its data or values 2 papers
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At low redshifts, we fit Eq. [ref] to the binned SFRD dataset of Ref. [ref] , using the inverse-variance weighted averages.
Lights, Camera, Axion: Tracing Axions from Supernovae in the Diffuse $\gamma$-ray Sky · Diffuse gamma-ray flux · 0.96
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For a more recent measurement of the SFR, see [ref] .
Energy-dependent Boosted Dark Matter from Diffuse Supernova Neutrino Background · Diffuse Supernova Neutrino Background · 0.87
Cites it as background 1 paper
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To date, the theoretical modeling of the DSNB is hampered by our incomplete understanding of the CCSN rate [ref] and the initial mass function [ref] , the rate of collapses forming a black hole (BH) [ref] , flavor conversion in the supernova (SN) core [ref] , the metallicity evolution of galaxies [ref] , binary stellar interactions [ref] , as well as the neutron star properties [ref] ; we refer the interested reader to Ref. [ref] for an overview of the relevant uncertainties.
Diffuse Neutrino Background from Magnetorotational Stellar Core Collapses · Introduction · 0.97
What it cites · 64 sentences without a confident label
Uses the method or tool of 5 papers
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To model the neutrino emission from successful supernovae, we use the suite of two-dimensional axisymmetric simulations from Ref. [ref] .
Supernova neutrino signals based on long-term axisymmetric simulations · Successful supernova neutrino emission · 0.83
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For the second phase of SK-Gd with 0.03% Gd, we follow the techniques from Ref. [ref] and conservatively assume that the background is reduced to a $S:B$ ratio of 2:1 following an implementation of convolutional neural networks into the SK-Gd analysis.
Prospects for the detection of the Diffuse Supernova Neutrino Background with the experiments SK-Gd and JUNO · Detection prospects · 0.95
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To estimate the late-phase neutrino emission after this over a timescale of $∼10 s$, we use an analytic function fitted to the existing data and a method that correlates PNS mass and shock revival time to estimate the late-phase neutrino emission [ref] .
Impact of late-time neutrino emission on the diffuse supernova neutrino background · Discussion and Conclusions · 0.92
Uses data or values from 27 papers
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For SK-Gd, from August 2020 to June 2022 the Gd concentration is $0.01%$, and we adopt an average total signal efficiency of $∼30%$ (see Fig. 1 of Ref. [ref] ).
Search for astrophysical electron antineutrinos in Super-Kamiokande with 0.01wt% gadolinium-loaded water · Detection prospects · 0.99
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Note, the model we choose for our fiducial model (diamond point) is the $40 M_⊙$, 3D, LS220 EOS model from Ref. [ref] , which lies between these.
Neutrino emission characteristics of black hole formation in three-dimensional simulations of stellar collapse · Calculating event rates · 0.87
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Finally, we multiply the flux by the cross section of the corresponding experiment ($σ_ν$) and the number of target protons, before integrating over the detection energy window, to obtain the DSNB event rate [equation] For SK doped with gadolinium (SK-Gd), $N_t=1.5×10^33$ is the number of target protons, we take $σ_ν$ as the IBD cross section (see Refs. [ref] ), and we adopt the detection energy range for SK-Gd from $9.3 MeV$ to $31.3 MeV$ [ref] .
The angular distribution of the reaction $\bar{\nu}_e + p \to e^+ + n$ · Calculating event rates · 0.90
Compares with 8 papers
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We find that the time it takes to reach a significant detection is longer than what is computed for SK-Gd and JUNO in Ref. [ref] .
Prospects for the detection of the Diffuse Supernova Neutrino Background with the experiments SK-Gd and JUNO · Discussion and Conclusions · 0.99
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The fiducial signal rates of Tables [ref] and [ref] agree generally well with the computed rates with normal ordering compared to Refs. [ref] .
Detecting the Diffuse Supernova Neutrino Background in the future Water-based Liquid Scintillator Detector Theia · Discussion and Conclusions · 1.00
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These direct measurements agree well within $z≲1$ (in contrast with older data [ref] but in agreement with more recent comparisons, see, e.g., [ref] ), but the direct measurements from Ref. [ref] between $1<z<2$ appear to be systematically lower than those inferred from SFRD measurements.
Cosmic Star Formation History · Direct CCSNe measurements · 0.96
Cites as background 36 papers
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Well-known compilations of SFRD measurements were carried out in Ref. [ref] in 2006 and again in Ref. [ref] in 2014.
Cosmic Star Formation History · SFRD measurements · 0.81
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Well-known compilations of SFRD measurements were carried out in Ref. [ref] in 2006 and again in Ref. [ref] in 2014.
On the normalisation of the cosmic star formation history · SFRD measurements · 0.81
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Theoretical estimates of the DSNB were initially uncertain by an overall factor of $∼10$ or more, but this factor has steadily decreased (see Refs. [ref] for theoretical models of the DSNB).
Shockwaves in Supernovae: New Implications on the Diffuse Supernova Neutrino Background · Introduction · 0.88
Discussion
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