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[Paper Review] Fragmentation cross-sections and model uncertainties in Cosmic Ray propagation physics

Nicola Tomassetti|arXiv (Cornell University)|Oct 30, 2015
Radiation Therapy and Dosimetry1 references3 citations
TL;DR

This paper evaluates the impact of fragmentation cross-section uncertainties on cosmic ray propagation models, focusing on B/C and Be/B ratios as constraints for transport parameters. It finds that nuclear data uncertainties significantly limit the resolution of the D₀/L degeneracy, suggesting direct 10Be measurements would yield cleaner constraints than Be/B ratios.

ABSTRACT

Abundances and energy spectra of cosmic ray nuclei are being measured with high accuracy by the AMS experiment. These observations can provide tight constraints to the propagation models of galactic cosmic rays. In the view of the release of these data, I present an evaluation of the model uncertainties associated to the cross-sections for secondary production of Li-Be-B nuclei in cosmic rays. I discuss the role of cross section uncertainties in the calculation of the boron-to-carbon and beryllium-to-boron ratios, as well as their impact in the determination of the cosmic-ray transport parameters.

Motivation & Objective

  • To assess how uncertainties in fragmentation cross-sections affect the determination of cosmic ray transport parameters.
  • To evaluate the role of B/C and Be/B ratios in resolving the D₀/L degeneracy in diffusion models.
  • To determine whether current isotopic or elemental ratios provide robust constraints under realistic nuclear data uncertainties.
  • To identify whether improved measurements of 10Be production would enhance parameter resolution compared to Be/B ratios.
  • To highlight the critical need for dedicated nuclear cross-section measurements to unlock the full potential of AMS data.

Proposed method

  • Uses the GALPROP code to simulate cosmic ray propagation in a cylindrical galactic halo with diffusive-reacceleration model.
  • Performs a grid scan over D₀, L, and v_A parameters (19×15×12 grid) to generate 187,245 models for interpolation.
  • Computes χ² statistics for B/C and Be/B ratios using mock AMS data, with errors combined in quadrature with nuclear uncertainties.
  • Applies tri-linear interpolation to model spectra and derives confidence contours in the D₀–L parameter space.
  • Repeats simulations with only 10Be production uncertainties to isolate their impact on parameter reconstruction.
  • Uses force-field approximation to model solar modulation and assumes steady-state propagation with zero-flux boundary conditions.

Experimental results

Research questions

  • RQ1How do uncertainties in fragmentation cross-sections affect the precision of derived cosmic ray transport parameters?
  • RQ2To what extent can the B/C and Be/B ratios resolve the D₀/L degeneracy in cosmic ray propagation models?
  • RQ3What is the relative constraining power of Be/B ratios versus direct 10Be measurements for parameter extraction?
  • RQ4How do systematic biases in single P→F nuclear reactions influence the inferred transport parameters?
  • RQ5Can precise Be/B data at E≳10 GeV/nucleon help detect nuclear physics biases in propagation models?

Key findings

  • Nuclear cross-section uncertainties significantly degrade the resolution of the D₀/L degeneracy, even with high-precision AMS data.
  • The B/C ratio alone constrains D₀ and L to a degenerate region, but only the combined B/C + Be/B ratios can resolve their individual values.
  • After accounting for nuclear uncertainties, the reconstructed uncertainties remain large: δD₀ ≈ 0.5×10²⁸ cm²/s⁻¹ and δL ≈ 0.5 kpc.
  • The Be/B ratio contributes little information due to large uncertainties in 10Be, 7,9Be, and 11B production cross-sections.
  • Direct measurement of 10Be flux at ~1–10 GeV/nucleon is shown to provide cleaner constraints than Be/B ratios.
  • Even when only 10Be production uncertainties are considered, parameter precision remains limited (δD₀ ≈ 1.5×10²⁸ cm²/s⁻¹, δL ≈ 1.5 kpc), underscoring the need for improved nuclear data.

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This review was created by AI and reviewed by human editors.