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Sandbox Physics
Nuclear physics

L4 · Chapter 9

Read a deuterium measurement and recalculate its thermal average

You can trace a thermal reaction rate back to identifiable measurements and check its numerical scale yourself. Open Mossa et al., Nature 587 (2020), DOI 10.1038/s41586-020-2878-4, beside this chapter. The checked accepted manuscript contains the numerical tables on pages 21–22. The exercise follows those tables and the polynomial in Methods Eq. 2; the collaboration’s complete analysis requires additional data and calibration information.

Start with the measured rows

Extended Data Table 1 has four columns: centre-of-mass energy, S factor, statistical standard error and systematic standard error. There are thirteen rows. Preserve those columns separately when you copy them. The frozen numerical file also records the manuscript address and SHA256, so a future calculation can identify the exact source that was checked.

For example, the row at 99.5 keV99.5\,\mathrm{keV} gives S=0.850 eV bS=0.850\,\mathrm{eV\,b}, with statistical and systematic errors 0.008 eV b0.008\,\mathrm{eV\,b} and 0.021 eV b0.021\,\mathrm{eV\,b}. Adding their variances gives a marginal standard error of about 0.0225 eV b0.0225\,\mathrm{eV\,b}. It does not specify the correlation with the next row.

The table contains processed cross-section information, not individual detected photons. It supports a fit of the published S factors. Reconstructing detector efficiency or background subtraction would require the acquisition and calibration records that are absent from this table.

Source: V. Mossa et al.

Keep the two fitted curves separate

In Figure 1, first find the filled LUNA points, then the published smooth fit. Methods Eq. 2 describes a cubic fit that also used other datasets. The teaching workbench makes a new quadratic fit to selected LUNA rows. Its solid curve and the published dashed reference answer related questions with different inputs.

A new fit should save the selected point indices, basis, weights and coefficient covariance. Here the diagonal weights retain the statistical errors and the part of each systematic variance left after an assumed 2.4% common component is removed. The common scale is propagated separately. This explicit decomposition is a teaching choice because the table does not supply a full covariance matrix.

Read the residuals against the original central values. A small residual in the measured interval tests how the chosen curve describes those points. It cannot tell you how the same polynomial behaves far below the lowest point. The lowest collision energies are especially relevant when the temperature is reduced.

Source: V. Mossa et al.

Check the integral in physical units

The reproducible benchmark integrates the published cubic, using the nuclear masses and SI conversions stated in the preceding chapter. Composite Simpson quadrature in logarithmic energy resolves the low-energy tail. A separate test instead integrates cross section times speed times Maxwell density on a uniform energy grid. Their different variables and integrands make the agreement useful as an implementation check.

At T=0.1 GKT=0.1\,\mathrm{GK}, the first integral gives 5.75182 cm3 mol−1 s−15.75182\,\mathrm{cm^3\,mol^{-1}\,s^{-1}}. Extended Data Table 2 prints 5.74 cm3 mol−1 s−15.74\,\mathrm{cm^3\,mol^{-1}\,s^{-1}}, a relative difference of about 0.206%. The seven checked temperatures range from 0.001 GK0.001\,\mathrm{GK} to 1 GK1\,\mathrm{GK}; the largest relative difference is about 0.594%.

The mismatch is retained in the downloadable comparison. It has not been assigned to a particular cause. The published all-dataset covariance and its low and high rate columns would require a fuller analysis. These numbers check the central-rate scale, and the two independent numerical integrals agree much more closely with each other.

ΔR(T)=Rrecalculated(T)Rtable(T)−1\Delta_R(T)=\frac{R_{\rm recalculated}(T)}{R_{\rm table}(T)}-1
The residual is dimensionless. Save it at every checked temperature so an apparently good example cannot hide a larger difference elsewhere.

Source: R. Longland et al.

Ask which energies carried the answer

Return to the fitted thermal contribution, which is the S factor multiplied by both exponential weights. Shade the interval from the first selected energy to the last. Integrate inside the shading and divide by the full fitted rate. This fraction measures how much of that model calculation lies inside the sampled energy span.

At 100 MK, the new LUNA-only quadratic places about 47% inside that span. Its rate is about 5.38, lower than the separately integrated published cubic. At lower temperature the contribution moves below the first point. Different smooth low-energy continuations can then change the integral even when they describe the measured interval comparably well.

A pointwise error band is also insufficient to propagate a rate. Two curves can have identical uncertainty at each energy and different correlations across energies. A common normalization moves the entire integral in one direction; independent fluctuations partly cancel. Carry coefficient covariance or sample coherent whole curves before integrating.

State the result at the scale actually checked

The measurement is a published experimental result. The quadratic and its uncertainty decomposition are an instructional reanalysis of published numerical values. The independent quadratures are numerical evidence for the implemented integral. The exact reaction model outside the data, plasma effects and a complete nucleosynthesis calculation remain separate research questions.

For another reaction, begin by writing down the isotope pair, energy frame and data version. Identify every shared calibration before fitting. Save the selected measurements, parameter covariance and the integrand. Quote the energy coverage alongside the rate. These steps make it possible for another reader to repeat your calculation and see where new measurements would help.

Try the reasoning in an experiment

Read the same data at two temperatures

  1. Save the default calculation and locate the thirteen original measured rows in the downloaded record.
  2. Compare the calculated quadratic rate with the separately labelled published-cubic reference. Account for their different fit inputs.
  3. Choose A young star. Use the outside-span contribution to explain why a conditional fit error is an incomplete description of the low-temperature prediction.

Exercises and explanations

  1. A table lists systematic errors for every energy but no off-diagonal covariance. Can you infer that all of those errors are independent?

    Read the explanation

    No. Reusing the same beam-current, target-density or efficiency calibration can correlate several measurements. The table gives marginal error sizes. A full propagation needs the correlations, the underlying nuisance parameters, or a clearly stated sensitivity analysis such as the assumed common component used here.

  2. The published value is 5.745.74 and the recalculated value is 5.751825.75182 in the same units. Compute the relative residual and say what its agreement establishes.

    Read the explanation

    The ratio minus one is about 0.002060.00206, or 0.206%. It supports the central-rate scale for this one temperature and specified cubic. It says nothing by itself about the full fitted covariance, extrapolation accuracy or a predicted cosmic abundance.

Download the values and calculation

Sources and reproduced scope

Evaluated level energies and published reaction measurements retain their source versions and uncertainties. Model curves, schematic figures and synthetic scattering records are identified separately. The offline benchmark files state which calculations have been reproduced.

  1. V. Mossa et al., The baryon density of the Universe from an improved rate of deuterium burning, Nature 587, 210–213 (2020)

    Accepted manuscript, Eqs. 2–3 and Extended Data Tables 1–2: measured proton-deuteron S factors and reference thermal rates. The local subset fit and the published global fit are different calculations.

  2. R. Longland et al., Charged-particle thermonuclear reaction rates: I. Monte Carlo method and statistical distributions (2010), arXiv:1004.4136v1

    Thermal-rate probability distributions and the need to keep common normalization errors correlated. The course derives its own first-order covariance propagation for the chosen fit.

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