Abstract

Context. AMS-02 on the International Space Station has been releasing data of unprecedented accuracy. This poses new challenges for their interpretation. Aims. We refine the methodology to get a statistically sound determination of the cosmic-ray propagation parameters. We inspect the numerical precision of the model calculation, nuclear cross-section uncertainties, and energy correlations in data systematic errors. Methods. We used the 1D diffusion model in USINE. Our χ2 analysis includes a covariance matrix of errors for AMS-02 systematics and nuisance parameters to account for cross-section uncertainties. Mock data were used to validate some of our choices. Results. We show that any mis-modelling of nuclear cross-section values or the energy correlation length of the covariance matrix of errors biases the analysis. It also makes good models (χmin2/d.o.f. ≈ 1) appear as excluded (χmin2/d.o.f. ≫ 1). We provide a framework to mitigate these effects (AMS-02 data are interpreted in a companion paper). Conclusion. New production cross-section data and the publication by the AMS-02 collaboration of a covariance matrix of errors for each data set would be an important step towards an unbiased view of cosmic-ray propagation in the Galaxy.

Highlights

  • Particle physics detectors in space have opened a new era for the study of Galactic cosmic rays (GCRs)

  • We show that any mis-modelling of nuclear cross-section values or the energy correlation length of the covariance matrix of errors biases the to mitigate these aenffaelcytssis(.AItMaSls-o02mdaakteasagroeoidntmeropdreetlesd(χin2mian/cdo.mo.fp.a≈nio1n) appear as paper)

  • We showed in the most challenging case that NSS and Linear combination (LC) nuisance parameters enable to recover the correct transport parameters when starting from the wrong cross sections, whereas systematic errors dominate over a wide dynamical range

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Summary

Results

We show that any mis-modelling of nuclear cross-section values or the energy correlation length of the covariance matrix of errors biases the to mitigate these aenffaelcytssis(.AItMaSls-o02mdaakteasagroeoidntmeropdreetlesd(χin2mian/cdo.mo.fp.a≈nio1n) appear as paper)

Conclusion
Introduction
Parameters for Model A and Model B
B94 W97 T99 W03
Mock data generation
Results of the mock data analysis
Unbiased case
Biased case: uncertainties and biases on transport parameters
Conclusions on the impact of cross-section uncertainties
Handling systematics from experimental data
Building the covariance matrix: correlation length
Parameter and goodness-of-fit dependence on correlation length
Joint impact of cross-section uncertainties and data systematics
Simplified 1D model and solutions
Full Text
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