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REVIEW 3 major objections 5 minor 7 references

Scattering and sputtering on the lunar surface; Insights from negative ions observed at the surface

T0 review · 3 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read A solar wind proton hitting the Moon has a 22% chance of scattering off the surface and an 8% chance of sputtering a hydrogen atom.

desk verdict First surface-based negative-ion constraints on lunar H scattering/sputtering, but the headline yields inherit a simulation prior for the unobserved backward-scattering lobe. read the letter →

arxiv 2602.16567 v1 pith:FTFE4UOI submitted 2026-02-18 physics.space-ph physics.atom-phphysics.ins-detstat.AP

Romain Canu-Blot , Martin Wieser , Umberto Rollero , Thomas Maynadié , Stas Barabash , Gabriella Stenberg Wieser , Aibing Zhang , Wenjing Wang
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Chi Wang
This is my paper
classification physics.space-phphysics.atom-phphysics.ins-detstat.AP
keywords lunarregolithsolarwindprotonsnegativehydrogenionsscatteringyieldsputteringBayesianinferencesurfacebindingenergyinelasticloss
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper uses the first direct measurements of negative hydrogen ions at the lunar surface—from the NILS instrument on the Chang'e-6 lander—to build and constrain a physics-based model of solar wind protons interacting with lunar regolith. The central result is a set of emission yields: a precipitating proton has about a 22% chance of scattering from the surface in any charge state and about an 8% chance of sputtering a surface hydrogen atom, so scattering outpaces sputtering as the channel that returns solar-wind hydrogen to the exosphere. The same analysis yields a 7–20% probability that emitted hydrogen leaves as a negative ion, a surface binding energy of about 5.5 eV, and inelastic energy losses roughly 50% larger than simulation priors, implying longer transport paths inside regolith grains. These numbers give a new, in-situ-anchored baseline for how the Moon converts solar wind into surface hydrogen, exospheric neutrals, and negative ions.

What carries the argument

The central object is the factorized differential flux model J^q(E,Ω) = P^q f_⊥ cos(SZA) [J_E^sc (η_sc J_Ω^sc) + J_E^sp (η_sp J_Ω^sp)], which separates energy from angular dependence for each emission process and connects charge state to a perpendicular-velocity-dependent ionization probability of hyperbolic-secant-squared form. Scattering is described by a modified discrete-streams solution with an inelastic energy loss that scales with total scattering angle and projectile energy plus Gaussian straggling (σ_ε = 0.7 μ_ε); sputtering is described by a primary-knock-on-atom recoil model extended to a multi-species regolith. The parameters are updated from physics- and simulation-based priors—

What would settle it

Measure the full angular distribution of scattered hydrogen (neutral or charged) from the lunar surface with an instrument that can see backward-scattering directions near the Sun line—for example, an orbital ENA imager with sensitivity to those angles, or a lander with a nearly hemispherical field of view. If the true backward lobe differs materially from the prior used here, the 22% total yield and the 1.5 scattering-to-sputtering ratio would shift accordingly.

Watch

Extended reading notes

Core claim

On the paper's own terms: by separating scattered and sputtered contributions to the measured negative-hydrogen flux and folding in a velocity-dependent ionization probability, the authors show that the total hydrogen scattering yield from lunar regolith is η_sc = 0.22 (68% HDI 0.16–0.27) and the sputtering yield is η_sp = 0.08 (0.04–0.16), giving η_sc/η_sp = 1.5 (+1.5/−1.1) at 300 km/s. The inference also returns a negative ionization probability of 7–20% that is nearly independent of emission velocity, a surface binding energy U ≈ 5.5 eV, and an inelastic-loss amplitude A470 = 147 eV that exceeds the simulation-based prior of 94.6 eV, which the authors interpret as longer effective path le

Load-bearing premise

The total scattering yield of 22% is dominated by backward-scattered particles at angles NILS never observed; that backward lobe is supplied by a simulation-based prior, so the true scattering yield depends on how well that prior matches reality.

Editorial extensions

If this is right

  • Scattering, not sputtering, is the dominant recycling channel for solar-wind hydrogen on the Moon; with η_sc/η_sp ≈ 1.5, roughly 60% of emitted hydrogen flux comes from scattered projectiles.
  • About 3.3% of incident protons return to space as negative hydrogen ions and about 0.8% as sputtered negative hydrogen, refining the earlier NILS estimate of 2.5% for scattered H−.
  • Lunar regolith is an efficient negative-ion source, with 7–20% ionization probability that depends only weakly on emission velocity, implying detectable negative-ion populations near airless bodies.
  • The larger inelastic energy losses imply effective path lengths of order 16 nm inside regolith grains, consistent with solar-wind implantation depths and with the hydrogen enrichment seen in returned samples.
  • The model is general: it can describe scattering and sputtering of any charge state from any homogeneous multi-species surface, with immediate applications to energetic neutral hydrogen and to other airless bodies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the 22% total yield holds, the Moon's efficiency at returning solar-wind hydrogen to space is higher than the 10–20% energetic-neutral-albedo range previously quoted—an orbital ENA measurement that resolves the backward-scattering lobe would settle whether the difference is real or an artifact of the prior.
  • The result depends on the unobserved backward-scattering lobe, fixed by a prior from simulations at SZA 60–75°; a lander with full-sky angular coverage, or an orbiter that images the sunward-scattered population, would test whether the angular profile used here is correct.
  • The high negative-ion probability suggests that airless bodies with rough, insulating regolith could be stronger sources of negative ions than previously assumed; because negative ions re-neutralize within a scale height of ~10 km, surface landers are the natural place to look for them.
  • The same analysis chain, applied to heavier emitted species or to alpha-particle projectiles, could separate how much of the observed oxygen and helium emission is scattered versus sputtered, testing the model's transferability.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper develops a semi-analytical model for the scattering and sputtering of hydrogen at the lunar surface, including negative-ion formation, and applies it to negative hydrogen ion observations from the NILS instrument on Chang'e-6. The model separates energy and angular distributions, uses a Bayesian framework with priors from ENA albedo measurements, SDTrimSP simulations, and laboratory ionization data, and infers scattering and sputtering yields. The headline results are a ~22% scattering yield and ~8% sputtering yield per incident solar wind proton, a scattered-to-sputtered flux ratio of about 1.5 at 300 km/s, a 7–20% probability of negative hydrogen ion emission, a surface binding energy of ~5.5 eV, and a larger inelastic energy loss than previous simulations suggested. The authors are careful to state caveats in Sec. 7.4 and 7.7, including that the backward-scattering lobe is unobserved and constrained by a simulation-based prior.

Significance. If the central quantitative claims are robust, the paper would be a valuable contribution: it provides a physics-based, observationally constrained model of ion interaction with an airless body's regolith, and it would establish that scattering, rather than sputtering, dominates solar-wind hydrogen recycling at the Moon, with a nontrivial fraction leaving as negative ions. The Bayesian treatment is rigorous, with explicit priors, posterior predictive checks, and an honest reporting of HDIs. The model's flexibility for other species and surfaces is a useful feature. However, the headline 22% scattering yield and the scattering>sputtering conclusion depend critically on a prior for the unobserved backward-scattering hemisphere, as the paper itself acknowledges. The broad HDI for the ratio (0.37–3.0) means the data alone do not firmly establish the ordering. This limits the certainty of the paper's main physical claim unless the prior sensitivity is addressed more convincingly.

major comments (3)
  1. [Sec. 7.4, Eq. 46, Table 3] The total scattering yield eta_sc=0.22 and the ratio eta_sc/eta_sp=1.5 are not constrained by NILS for the backward-scattering hemisphere. NILS observed only beta in [45,90] deg (forward sector), while Eq. 46 (from Szabo et al. 2023b, SZA 60-75 deg) fixes the backward lobe, which dominates the total yield. Section 7.4 states this explicitly. The posterior ratio itself has a 68% HDI of 0.369-3.00 (Table 3), meaning sputtering can dominate within the credible interval. The abstract and Sec. 8 present the 22% and the ordering as measured results. I request a rephrasing of the abstract/conclusions to state that these values are conditional on the adopted angular prior, and a quantitative sensitivity test (e.g., varying the forward/backward partition in Eq. 46) to show how eta_sc and the ratio change.
  2. [Sec. 4.8.6, Eqs. 39-41] The mean inelastic energy loss mu_eps and its straggling sigma_eps are fitted to SDTrimSP simulations (Szabo et al. 2023a,b), not derived from first principles, and the relative straggling is fixed at sigma_eps=0.7 mu_eps (Eq. 41). These parameters control the scattered energy distribution and are load-bearing for the inference of A470 (Table 3) and the 'longer path length' conclusion in Sec. 7.2. Since these parameters are fitted to simulations that the paper later argues underestimate inelastic losses, the prior is partly circular: the posterior A470=147 eV is interpreted as evidence that the simulations underestimated losses, but the shape of the energy distribution is inherited from those same simulations. I recommend treating the sigma_eps/mu_eps ratio as a free parameter or at least performing a sensitivity test over a plausible range (e.g., 0.5-0.9), and reporting how A470 and the
  3. [Sec. 5.2.2, Eq. 48b; Sec. 7.2] The factor nu_E, which scales the incident proton energy at the surface, is fixed to 1 by assumption (Sec. 5.2.2), and the paper acknowledges in Sec. 7.2 that the data favor nu_E ~ 0.85 and that allowing it to vary reduces A470 from 147 eV to ~110 eV. Because the claim of 'significant inelastic energy losses' and the derived effective path length rely on A470, the fixed nu_E=1 is a load-bearing assumption. The paper should present a joint inference of nu_E with the other parameters, or at minimum a sensitivity scan, and discuss how the main conclusions (yields, inelastic loss) shift. The current treatment risks overstating the inelastic loss by absorbing a systematic energy offset into mu_eps.
minor comments (5)
  1. [Abstract and Sec. 8] The abstract states 'roughly a 22% chance of scattering' and 'the resulting ratio ... = 1.5' without the conditionality on the angular prior. Please qualify these numbers as model-dependent, e.g., 'under the adopted simulation-based angular prior'.
  2. [Sec. 8] Typo: 'surface binding energy U of regolith of about 5.5 V' should be '5.5 eV'.
  3. [Fig. 7 caption] The caption uses Phi for the total scattering angle, while the text and Eq. 38 use Psi. Please unify the notation.
  4. [Sec. 5.1.1] The prior for U is described in Eq. 43 as TruncNorm(mu=5, sigma=0.5, lower=1), but Table F.1 lists U~Normal(mu=5, sigma=1) with a lower bound U>=1. These are inconsistent; please clarify which prior was used.
  5. [Sec. 7.3] The comparison of P-*eta_sc = 3.3% with the 2.5% yield from Wieser et al. (2025) is informative, but the estimated P- used for the comparison (0.15) is not clearly derived from the posterior; specify the assumed representative P- value.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the yield estimates are posterior updates from NILS counts under external priors, not re-statements of model inputs.

full rationale

The derivation chain is a genuine Bayesian update: Eq. 42 maps physical parameters (ηsc, ηsp, P−, U, angular yields) to NILS count rates through an instrument response, and the posterior is obtained from the Poisson likelihood and priors (Sec. 6.1). The two potentially worrisome priors are the ENA-albedo constraint (Eq. 45, from Vorburger et al. 2013) and the scattering angular prior (Eq. 46, from Szabo et al. 2023b). Neither is a fitted quantity renamed as a result: the likelihood updates ηENA from 0.16±0.05 to a posterior of 0.24 (Table 3), and ηsc/ηsp moves from a prior median of about 0.67 to a posterior median of about 2.0, so the 'scattering dominates sputtering' headline is not contained in the priors. The paper itself discloses the strongest limitation: NILS observed only β∈[45°,90°], and 'backward-scattering angles that are unobserved by NILS dominate and are constrained by the prior in Eq. 46' (Sec. 7.4). This makes the absolute ηsc prior-informed, but prior-dependence is not circularity: no equation defines a predicted quantity in terms of the same quantity, and the unobserved backward lobe is filled by an externally simulated distribution, not by the NILS fit itself. Self-citations (Canu-Blot et al. 2025 instrument calibration; Wieser et al. 2024/2025 previous ASAN/NILS analyses; Maynadié et al. 2025 magnetic-anomaly modeling) are separate, falsifiable studies and are not used as the sole justification of any target result. Hence no circular step; score 0.

Assumptions & free parameters 15 free parameters · 11 assumptions · 0 invented entities

The model is heavily parameterized: 15+ numbers are either fitted to NILS data, fitted to SDTrimSP simulations, or set by priors; the central yield and ionization numbers are posterior estimates, not closed-form predictions.

free parameters (15)
  • eta_sc (scattering yield) = 0.222 (MAP); 68% HDI 0.161-0.271
    Scattering yield inferred from NILS amplitude; prior via ENA albedo parametrization Eq. 45.
  • eta_sp (sputtering yield) = 0.0813 (MAP); 68% HDI 0.0378-0.156
    Sputtering yield inferred from NILS amplitude; wide HDI reflects low-energy model-data mismatch.
  • r (albedo split parameter) = Uniform(0,1); posterior not tabulated
    Splits the ENA albedo prior between scattering and sputtering (Eq. 45d); directly shapes the reported eta_sc/eta_sp ratio.
  • A470 (inelastic loss amplitude) = 147 eV (MAP); prior 94.6 eV
    Amplitude of the inelastic energy-loss model Eq. 40; updated by NILS, but interpretation is entangled with fixed nu_E=1.
  • k1 (inelastic loss sigmoid rate) = 0.0382 /deg (MAP)
    Sigmoid rate in Eq. 40; prior derived from SDTrimSP simulation fitting.
  • k2 (inelastic loss sigmoid exponent) = 0.742 (MAP)
    Sigmoid exponent in Eq. 40; prior derived from SDTrimSP simulation fitting.
  • S (saturation negative-ionization probability) = 0.904 (MAP)
    Saturation amplitude in Eq. 8; posterior updated by NILS data.
  • v50_perp = 9.41e6 m/s (MAP)
    Perpendicular speed for half-maximum ionization probability in Eq. 8; posterior from NILS.
  • k (ionization steepness) = 0.0998 (MAP)
    Steepness exponent in Eq. 8; posterior from NILS.
  • U (surface binding energy) = 5.45 eV (MAP); 68% HDI 4.57-6.49 eV
    Shared surface binding energy in sputtering and scattering models; prior 5±0.5 eV.
  • gamma_extra (extra sputtering energy loss) = 0.0228 (MAP); HDI ~0-0.097
    Empirical extra energy-loss factor in the sputtering model (Eq. 14b); posterior consistent with zero.
  • sigma_eps/mu_eps (relative energy straggling) = 0.7 (65% in Table 1)
    Relative straggling fixed by fitting Szabo SDTrimSP simulations; not inferred from NILS.
  • sigma_sc, sigma_sp (angular bin scales) = HalfNormal(sigma=1) hyperpriors
    Hierarchical bin-scale parameters for the discretized angular yields (Table F.1); inferred.
  • nu_f(t) (per-time flux modulation factors) = MAP time series (Fig. 10)
    Inferred in the simplified model Sec. 5.2.2 to describe magnetic-anomaly modulation; marginalized in main model; absolute scale fixed by assuming average=1.
  • eta_ENA (reconstructed ENA albedo) = 0.243 (MAP)
    Effective total neutral+negative hydrogen albedo reconstructed from the two yields; prior 0.16±0.05 partially sets the scale.
assumptions (11)
  • domain assumption The charge state of the projectile does not affect collision dynamics; only the precipitating flux magnitude matters.
    Sec. 4.3 postulates this based on femtosecond charge-exchange equilibration; supported by Lienemann et al. 2011, but unverified for lunar regolith.
  • domain assumption The negative-ionization probability follows P-(v_perp)=S sech^2[arcosh(sqrt2)(v50/v_perp)^k], fitted to silicon data, and applies to insulating lunar regolith.
    Sec. 4.4; the functional form is fit to Maazouz et al. silicon data (Fig. 3); regolith is an insulator, and the transfer relies on Borisov & Esaulov arguments.
  • domain assumption P0 = 1 - P- is energy- and angle-independent when applying the ENA albedo prior.
    Sec. 5.1.2 states this is not physically exact because P- depends on v_perp, but approximates it as constant to use the Vorburger albedo.
  • domain assumption The prior ENA albedo 0.16±0.05 from Vorburger et al. 2013 global orbital data applies at the Chang'e-6 landing site.
    Sec. 5.1.2; no local validation in the SPA magnetic-anomaly region is provided.
  • domain assumption The scattering angular prior Eq. 46, from Szabo et al. simulations at SZA 60-75°, describes the unobserved backward-scattered lobe at SZA ~50°.
    Sec. 5.1.3 and Sec. 7.4; the total scattering yield integrates directions NILS did not measure.
  • domain assumption Sputtered hydrogen angular emission follows a cosine law (Eq. 47).
    Sec. 5.1.3 based on Cassidy & Johnson; paper notes heavy-element forward sputtering could differ but assumes isotropic hydrogen recoils.
  • domain assumption The Forlano discrete-streams scattering model, derived for normal incidence and total scattering angles in [90°,180°], remains usable for angles below 100° after adding inelastic losses.
    Sec. 4.8.6 and Fig. 7a: the authors acknowledge the original model overestimates loss and is not derived for Psi<100°.
  • ad hoc to paper nu_E = 1, i.e., magnetic anomalies do not change the mean proton energy at the surface.
    Sec. 5.2.2 fixes nu_E=1 because nu_E and nu_f are non-identifiable; Sec. 7.2 admits nu_E≈0.85 would reconcile prior and posterior A470.
  • domain assumption Solar wind protons are mono-energetic, SZA is constant, and aberration and temperature broadening are neglected.
    Sec. 5.2.1; temperature effect is argued to be minor for the energy range studied.
  • domain assumption Alpha particles sputter hydrogen 2.8 times more efficiently than protons, giving k_alpha=0.11.
    Sec. 4.7.6; simulations and experiments suggest roughly 10x, but the authors keep the lower model-derived value for consistency.
  • domain assumption Microscopic surface roughness w=0.45 (mean inclination 27.7°, from an Apollo 16 sample) controls the beta-beta' mapping and visibility at the Chang'e-6 site.
    Sec. 4.5 and Appendix B; the rough-surface parameters are taken from a different lunar sample and transferred to CE-6 soil.

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Pith. "Pith review of Scattering and sputtering on the lunar surface; Insights from negative ions observed at the surface." pith.science (2026). https://pith.science/paper/FTFE4UOI

@misc{pith2026260216567,
  author       = {Pith},
  title        = {Pith review of: Scattering and sputtering on the lunar surface; Insights from negative ions observed at the surface},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FTFE4UOI}},
  note         = {Machine review of arXiv:2602.16567}
}
read the original abstract

Context. Airless planetary bodies are directly exposed to solar wind ions, which can scatter or become implanted upon impact with the regolith-covered surface, while also sputtering surface atoms. Aims. We construct a semi-analytical model for the scattering of ions of hundreds of eV and the sputtering of surface atoms, both resulting in the emission of negative ions from the lunar surface. Our model contains a novel description of the scattering process that is physics-based and constrained by observations. Methods. We use data from the Negative Ions at the Lunar Surface (NILS) instrument on the Chang'e-6 lander to update prior knowledge of ion scattering and sputtering from lunar regolith through Bayesian inference. Results. Our model shows good agreement with the NILS data. A precipitating solar wind proton has roughly a 22% chance of scattering from the lunar surface in any charge state, and about an 8% chance of sputtering a surface hydrogen atom. The resulting ratio of scattered to sputtered hydrogen flux is eta_sc / eta_sp = 1.5 for a proton speed of 300 km/s. We find a high probability (7-20%) that a hydrogen atom leaves the surface negatively charged. The angular emission distributions at near-grazing angles for both scattered and sputtered fluxes are controlled by surface roughness. Our model also indicates significant inelastic energy losses for hydrogen interacting with the regolith, suggesting a longer effective path length than previously assumed. Finally, we estimate a surface binding energy of 5.5 eV, consistent with the observations. Conclusions. Our model describes the scattering and sputtering of particles of any charge state from any homogeneous, multi-species surface. Using NILS data, we successfully applied the model to update our understanding of solar wind interacting with lunar regolith, and the emission of negative hydrogen ions.

Figures

Figures reproduced from arXiv: 2602.16567 by the authors.

Figure 1
Figure 1. Differential number flux of negative hydrogen ions, J− H , versus emission energy. Vertical bars show flux estimates, with thick and thin bars representing the 68% and 90% highest den￾sity intervals, respectively. Bar colour qualitatively reflects the signal significance, based on the Widely Applicable Information Criterion (Watanabe 2010) comparing models with and without hydrogen. Each panel corresponds to a speci… view at source ↗
Figure 2
Figure 2. Illustration of the solar wind impinging angles (orange) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Schematized sputtering induced by light ions. A proton [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (7 more)
Figure 5
Figure 5. Figure 5: Energy distribution of hydrogen atoms sputtered by 300 [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Schematized scattering of light ions from a surface. A [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Comparison of our energy distribution model with sim [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Relation between the mean inelastic energy loss, [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 10
Figure 10. Figure 10: Energy-time spectrogram of the nega￾tive hydrogen ion energy-differential flux at the surface, normalized by the upstream solar wind flux. The flux is expressed in units of 1/(sr). The flux is averaged over angles. The time is discon￾tinuous, an each bin is labelled b…
Figure 11
Figure 11. Figure 11: Lunar topography and observed macroscopic polar [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]
Figure 12
Figure 12. Figure 12: Comparison between the priors of the inelastic mean [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]

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Works this paper leans on

7 extracted references · 1 linked inside Pith

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    (2025, Eqs

    using the transformation defined in Wieser et al. (2025, Eqs. 14, 15, and 16). λ (u) =K (u) Z π/2 0 J (ξu,β,ϕ u)· ˜RΩ ˆu,β− π 2 sinβdβ , (C.5) withϕ u =f 2 (ωu,α u; SA), wheref 2 is defined in Wieser et al. (2025, Eq

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    andα u =−0.752 ◦ (Canu-Blot et al. 2025, Eq. 16). We neglect the dependency overω, and approximateϕ u≈ ϕ=f 2 αu; SA ≈213 ◦, where SA≈34 ◦ is the average Solar Azimuth angle over the mission. Appendix D: Bayesian inference The NILS dataset consists of 302 minutes of observations, with the instrument viewing the lunar surface for about half of that time. Th...

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    main we can write the normalization factorn= R∞ 0 J sc E dE, inde- pendent onE in, as: nu,∆′ ϵ =2 Z κln (A/u) 0 1− u A exp (x/κ) f (x) dx.(E.4) E.2

    Noting that dE= EinA κexp (x/κ)dx,(E.3a) e= A exp (x/κ)−u,(E.3b) Article number, page 25 A&A proofs:manuscript no. main we can write the normalization factorn= R∞ 0 J sc E dE, inde- pendent onE in, as: nu,∆′ ϵ =2 Z κln (A/u) 0 1− u A exp (x/κ) f (x) dx.(E.4) E.2. Analytical approximation For the special case of (p=0.97,κ=7.3 ), corresponding to a proton i...

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    2025

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