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Helioseismic and Neutrino Data Driven Reconstruction of Solar Properties
Solar and Stellar Astrophysics · Submitted 5 Oct 2017
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In addition we find that the values of most of the other inputs of the standard solar models required to better describe the helioseismic and neutrino data are in good agreement with those adopted as the standard priors, with the exception of the astrophysical factor $S_{11}$ and the microscopic diffusion rates, for which data suggests a 1% and 30% reduction respectively.
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The abstract, sentence by sentence
In this work we use Bayesian inference to quantitatively reconstruct the solar properties most relevant to the solar composition problem using as inputs the information provided by helioseismic and solar neutrino data. In particular, we use a Gaussian process to model the functional shape of the opacity uncertainty to gain flexibility and become as free as possible from prejudice in this regard. With these tools we first readdress the statistical significance of the solar composition problem. Furthermore, starting from a composition unbiased set of standard solar models we are able to statistically select those with solar chemical composition and other solar inputs which better describe the helioseismic and neutrino observations. In particular, we are able to reconstruct the solar opacity profile in a data driven fashion, independently of any reference opacity tables, obtaining a 4% uncertainty at the base of the convective envelope and 0.8% at the solar core. When systematic uncertainties are included, results are 7.5% and 2% respectively. In addition we find that the values of most of the other inputs of the standard solar models required to better describe the helioseismic and neutrino data are in good agreement with those adopted as the standard priors, with the exception of the astrophysical factor $S_{11}$ and the microscopic diffusion rates, for which data suggests a 1% and 30% reduction respectively. As an output of the study we derive the corresponding data driven predictions for the solar neutrino fluxes.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In this work we use Bayesian inference to quantitatively reconstruct the solar properties most relevant to the solar composition problem using as inputs the information provided by helioseismic and solar neutrino data. | contribution 0.67 | 0.95 | 0.07 | asserted 0.98 |
| s1 | In particular, we use a Gaussian process to model the functional shape of the opacity uncertainty to gain flexibility and become as free as possible from prejudice in this regard. | method 0.99 | 0.94 | 0.03 | asserted 0.99 |
| s2 | With these tools we first readdress the statistical significance of the solar composition problem. | contribution 0.96 | 0.84 | 0.05 | unclear 0.61 |
| s3 | Furthermore, starting from a composition unbiased set of standard solar models we are able to statistically select those with solar chemical composition and other solar inputs which better describe the helioseismic and neutrino observations. | contribution 0.55 | 0.96 | 0.15 | asserted 0.99 |
| s4 | In particular, we are able to reconstruct the solar opacity profile in a data driven fashion, independently of any reference opacity tables, obtaining a 4% uncertainty at the base of the convective envelope and 0.8% at the solar core. | result 0.97 | 0.97 | 0.36 | asserted 1.00 |
| s5 | When systematic uncertainties are included, results are 7.5% and 2% respectively. | result 1.00 | 0.90 | 0.10 | asserted 1.00 |
| s6 | In addition we find that the values of most of the other inputs of the standard solar models required to better describe the helioseismic and neutrino data are in good agreement with those adopted as the standard priors, with the exception of the astrophysical factor $S_{11}$ and the microscopic diffusion rates, for which data suggests a 1% and 30% reduction respectively. | result 1.00 | 0.96 | 0.20 | asserted 0.88 |
| s7 | As an output of the study we derive the corresponding data driven predictions for the solar neutrino fluxes. | result 0.83 | 0.96 | 0.11 | asserted 0.99 |
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The paper
Original abstract
In this work we use Bayesian inference to quantitatively reconstruct the solar properties most relevant to the solar composition problem using as inputs the information provided by helioseismic and solar neutrino data. In particular, we use a Gaussian process to model the functional shape of the opacity uncertainty to gain flexibility and become as free as possible from prejudice in this regard. With these tools we first readdress the statistical significance of the solar composition problem. Furthermore, starting from a composition unbiased set of standard solar models we are able to statistically select those with solar chemical composition and other solar inputs which better describe the helioseismic and neutrino observations. In particular, we are able to reconstruct the solar opacity profile in a data driven fashion, independently of any reference opacity tables, obtaining a 4% uncertainty at the base of the convective envelope and 0.8% at the solar core. When systematic uncertainties are included, results are 7.5% and 2% respectively. In addition we find that the values of most of the other inputs of the standard solar models required to better describe the helioseismic and neutrino data are in good agreement with those adopted as the standard priors, with the exception of the astrophysical factor $S_{11}$ and the microscopic diffusion rates, for which data suggests a 1% and 30% reduction respectively. As an output of the study we derive the corresponding data driven predictions for the solar neutrino fluxes.
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How the literature uses it
5 arXiv papers cite this in 7 sentences
Papers citing it · 4 sentences without a confident label
Uses its data or values 2 papers
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To set exclusion bounds on the scalar mass $m_S$ and mixing angle $sinθ$, we adopt 3% of measured solar luminosity, which is from the combination of helioseismology (speed of sound, surface helium and convective radius) and solar neutrino observations [ref] [equation] The resultant excluded region of $m_S$ and $sinθ$ is shown in Fig. [ref] as the cyan shaded region.
Improved stellar limits on a light CP-even scalar · Results · 0.98
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We will use 3% of the observed solar luminosity [ref] to derive the solar limits in the next section.
Stellar limits on light CP-even scalar · Luminosity limits · 0.86
Cites it as support 1 paper
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In particular, the CNO neutrino flux is very sensitive to the metallicity of the solar core, as can be seen in Table [ref], and hence a precise measurement of this flux would help in improving solar models, by providing another metallicity measurement against which they can be tested [ref] .
CNO Neutrino Grand Prix: The race to solve the solar metallicity problem · Introduction · 0.97
What it cites
Uses the method or tool of 1 paper
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In this work we use [ref] , a Bayesian inference tool which, given the prior and the likelihood, calculates the evidence with an uncertainty estimate, and generates posterior samples from distributions that may contain multiple modes and pronounced (curving) degeneracies in high dimensions.
Importance Nested Sampling and the MultiNest Algorithm · Statistical Framework · 1.00
Cites as support 1 paper
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Also, in a recent theoretical analysis of line broadening modeling in opacity calculations, [ref] have found that uncertainties linked to it are larger at the base of the convective envelope than in the core.
Line broadening and the solar opacity problem · Treatment of the radiative opacity · 0.98
Cites as background 1 paper
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Standard Solar Models (SSMs; [ref] ) describe the Sun present day properties as a result of its evolution starting at the pre-main sequence.
Solar neutrinos and the solar composition problem · Introduction · 1.00
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