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CTAO will map TeV halos at 30σ and pin down cosmic-ray diffusion physics

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · glm-5.2

2026-07-07 21:47 UTC pith:RMC6WCNR

load-bearing objection Solid CTAO forecasting study; detection claims are robust, parameter-sensitivity claims are optimistic but honestly labeled. the 2 major comments →

arxiv 2607.05245 v1 pith:RMC6WCNR submitted 2026-07-06 astro-ph.HE

Geminga and Monogem in the CTAO Era: Probing TeV Halos and Cosmic-Ray Transport

Youyou Li , Oscar Macias , Manuela Vecchi , Shinichiro Ando This is my paper
classification astro-ph.HE
keywords TeV haloscosmic-ray diffusionpulsar wind nebulaeGemingaMonogemCherenkov Telescope Array Observatoryinverse-Compton emissionslow-diffusion zone
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper forecasts what the Cherenkov Telescope Array Observatory (CTAO) will measure when it turns its arcminute-resolution gamma-ray telescopes on the two brightest TeV halos—those surrounding the Geminga and Monogem pulsars. The authors build two-zone diffusion models in which electrons and positrons are injected according to each pulsar's spin-down history, then propagate through a slow-diffusion bubble (the slow-diffusion zone, or SDZ—a region of suppressed cosmic-ray diffusion extending tens of parsecs around the pulsar) before entering the normal interstellar medium. These models are calibrated to existing HAWC and Fermi-LAT data and then forward-folded through official CTAO instrument response functions, including realistic diffuse and instrumental backgrounds, to produce mock observations. The paper claims that 50-hour CTAO observations will detect both halos at 13–30σ significance from either the northern or southern CTAO site, and that under optimistic background assumptions the observatory can distinguish changes in the high-energy electron injection index at the level of Δγ₁ ≃ 0.2, magnetic-field strengths differing by ΔB ≃ 2 μG, and—specifically for Monogem—compact slow-diffusion zones of ~30 pc radius from more extended configurations of ≳50 pc. The authors also test robustness against mismodeling of the Galactic diffuse background by fitting mock data generated with one diffuse model using an alternative model, finding that detection significances and parameter sensitivities change by less than 10%.

Core claim

The central claim is that CTAO's combination of arcminute angular resolution, broad energy coverage from ~20 GeV to beyond 100 TeV, and improved sensitivity will transform TeV halos from marginally resolved blobs into spatially resolved laboratories for cosmic-ray transport physics. The paper demonstrates this by showing that the surface-brightness morphology of the gamma-ray emission carries distinct imprints of three key parameters—the electron injection spectral index (which shapes the central spectrum), the magnetic-field strength (which controls the radial spectral gradient through synchrotron cooling), and the SDZ radius (which governs the energy-dependent spatial extent of the halo)—s

What carries the argument

Two-zone diffusion model: electrons and positrons are injected by the pulsar wind nebula according to the pulsar spin-down luminosity evolution, then propagate through a slow-diffusion zone of radius R_SDZ where the diffusion coefficient is suppressed by 2–3 orders of magnitude relative to the Galactic average, before transitioning to standard interstellar diffusion at radius r_t. The bubble grows over time as R_SDZ(t) = μ√t. Gamma-ray emission is inverse-Compton scattering of these multi-TeV leptons off the interstellar radiation field and CMB. Models are computed with GALPROP v57 and forward-folded through CTAO prod5 IRFs using Gammapy v1.2.

Load-bearing premise

The parameter sensitivity analysis varies one parameter at a time while holding all others fixed, which the authors themselves acknowledge represents an optimistic upper bound on CTAO's constraining power; in reality, the injection index, magnetic field, and SDZ size are correlated through their joint effect on the surface-brightness profile, and a simultaneous multi-parameter fit would yield weaker constraints.

What would settle it

If real CTAO observations of Geminga or Monogem fail to detect the halos at the predicted 13–30σ significance in 50 hours, or if the measured surface-brightness profiles cannot be fit by any two-zone isotropic diffusion model, the framework's predictive power would be called into question.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • CTAO observations of Geminga and Monogem could distinguish between theoretical mechanisms for diffusion suppression—self-generated turbulence (predicting ~20 pc zones) versus supernova-remnant-driven turbulence (predicting up to ~100 pc)—by measuring the SDZ radius.
  • If the electron injection index can be pinned to Δγ₁ ≃ 0.2, this would resolve the current tension between Fermi-LAT (favoring γ₁ ≈ 2.2–2.3) and HAWC (favoring γ₁ ≈ 1.0–1.1) spectral measurements of these halos.
  • The paper estimates that Geminga and Monogem each contribute up to ~10% of the measured positron flux above 100 GeV, suggesting that neither pulsar alone dominates the local positron excess—tighter constraints on transport parameters would sharpen this conclusion.
  • The finding that very large SDZ radii (≳50 pc for Monogem) become degenerate due to the finite CTAO field of view implies that complementary wide-field instruments like LHAASO and HAWC will remain essential for constraining the full spatial extent of slow-diffusion regions.
  • The cross-calibration strategy proposed—using HAWC and LHAASO flux measurements in overlapping energy ranges to validate CTAO's irreducible cosmic-ray background model—establishes a concrete observational program for joint analysis across instruments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The one-parameter-at-a-time sensitivity analysis likely overstates CTAO's constraining power, because injection index, magnetic field, and SDZ size jointly affect the surface-brightness profile. A multi-parameter fit would probably yield weaker constraints, though the authors note that each parameter leaves a distinct spatial signature that could be exploited with energy-banded analysis.
  • The assumption of isotropic diffusion with no bulk advection may miss important physics: if diffusion is anisotropic (aligned with local magnetic-field orientation) or if turbulence coherence lengths are small, the halo morphology could differ systematically from the symmetric two-zone prediction, potentially biasing parameter recovery.
  • Extending the two-zone framework to include anisotropic diffusion and turbulence-coherence-length effects would be a natural next step; such models would predict azimuthal asymmetries in the halo that CTAO's arcminute resolution could in principle resolve, providing an additional diagnostic beyond what the isotropic model offers.
  • The grid-pointing strategy mentioned for mapping the extended low-energy halo with LSTs could, if combined with pulsar proper-motion measurements, independently constrain the diffusion coefficient anisotropy by comparing the leading and trailing edges of the halo.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 7 minor

Summary. This paper presents end-to-end forecasts for CTAO observations of the TeV halos around Geminga and Monogem. The authors construct two-zone diffusion models using GALPROP v57, calibrate to HAWC and Fermi-LAT data, and forward-fold through official CTAO prod5v0.1 IRFs using Gammapy v1.2. They assess detection significance, parameter sensitivity (injection index, magnetic field, SDZ size), and robustness to Galactic diffuse background mismodeling. The methodology is standard for instrument forecasting and is executed carefully, including 50 realizations per configuration and a background robustness test using two independent GDB models.

Significance. The paper provides a timely and useful forecast for CTAO's ability to probe TeV-halo transport physics. The detection significance estimates (~13–30σ for 50h) are robust and will be valuable for observation planning. The use of official CTAO IRFs, the forward-folding template-based likelihood approach, the two-model GDB robustness test, and the explicit acknowledgment of the one-parameter-at-a-time limitation are all commendable. The framework is internally consistent and the parameter space explored is physically motivated.

major comments (2)
  1. §3.3, Figs. 11–12: The claim that CTAO can 'distinguish changes of Δγ₁ ≃ 0.2' (abstract, §4) is marginal even under the optimistic one-parameter-at-a-time framework. In Fig. 11 (Geminga, 50h), the violin for Δγ₁ = +0.2 shows σ values spanning roughly 3–5, with the lower portion at or below the 3σ distinguishability threshold. Fig. 12 (Monogem) shows a similar pattern for Δγ₁ = +0.2. Since the authors use σ = 3 as the threshold for distinguishability, a non-trivial fraction of realizations fall below this threshold even in the most favorable case. The abstract and conclusions state Δγ₁ ≃ 0.2 as a firm result without this qualification. The authors should either (a) soften the claim to reflect that Δγ₁ ≈ 0.2 is at the edge of distinguishability even under optimistic assumptions, or (b) report the fraction of realizations exceeding σ = 3 to quantify the reliability of this threshold.
  2. §3.3, paragraph 2 and Eq. (3)/Table 1: The authors argue that γ₁, B, and R_SDZ are 'not intrinsically degenerate' because each affects a different observable. However, B and D_SDZ are coupled through Eq. (3): changing B alters τ_cool, which changes the inferred D_SDZ (Table 1), which reshapes the spatial profile. This coupling could partially mimic the effect of varying R_SDZ or γ₁ in a joint fit. The paper does not test whether this coupling produces significant parameter correlations. A brief discussion of this specific coupling—ideally with a qualitative or quantitative argument for why it does not undermine the one-at-a-time results—would strengthen the parameter-sensitivity claims.
minor comments (7)
  1. Abstract and §4: The phrase 'CTAO can distinguish changes of Δγ₁ ≃ 0.2' should be qualified with 'under optimistic, one-parameter-at-a-time assumptions' to match the more careful framing in §3.3.
  2. §2.1, Eq. (3): The diffusion length formula uses min{τ_cool, τ_inj}, but the text does not explicitly state which timescale dominates for the electron energies of interest (e.g., 100 TeV). Stating this would help the reader follow the D_SDZ derivation.
  3. Table 1: The units in the caption ('1027 cm² s⁻¹') are clear, but the table header 'D_100 TeV' could be confused with D at 100 TeV electron energy versus 100 TeV photon energy. A brief clarification would help.
  4. §2.2.1: The HAWC-favored injection index of 1.0–1.1 is mentioned as discrepant with Fermi-LAT's 2.2–2.3, but the benchmark values adopted (γ₁ = 2.2 for Geminga, 1.8 for Monogem) are not explicitly justified against this discrepancy. A sentence explaining the choice would be useful.
  5. Figures 11–12: The x-axis labels for Δγ₁ show only positive shifts for Monogem (+0.2, +0.4, +0.6) but both signs for Geminga (−0.2, +0.2). If this is intentional (e.g., because the benchmark γ₁ = 1.8 for Monogem is near the lower end of explored values), a note would clarify.
  6. §3.2.1: The statement that differences remain 'below ~5σ for a 50h observation' corresponds to '≲20% variation in √TS' — this is slightly inconsistent with the abstract's claim of '≲10%'. The abstract likely refers to the parameter sensitivity results, but the wording should be disambiguated.
  7. References: 'Brahimi, L. et al. 2020' and 'Manconi, Silvia et al. 2024' have non-standard formatting (given names in citation key). 'Schroer et al. 2022a' and '2022b' appear to be the same paper.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for a careful and constructive report. Both major comments identify legitimate points that we will address in the revised manuscript. On the first comment, we agree that the Δγ₁ ≃ 0.2 claim is marginal at the 3σ threshold and will soften the language and report the fraction of realizations exceeding 3σ. On the second comment, we agree that the B–D_SDZ coupling deserves explicit discussion and will add a quantitative argument for why it does not undermine the one-at-a-time results.

read point-by-point responses

  1. Referee: §3.3, Figs. 11–12: The claim that CTAO can 'distinguish changes of Δγ₁ ≃ 0.2' is marginal even under the optimistic one-parameter-at-a-time framework. The violin for Δγ₁ = +0.2 shows σ values spanning roughly 3–5, with the lower portion at or below 3σ. The abstract and conclusions state Δγ₁ ≃ 0.2 as a firm result without qualification. The authors should either (a) soften the claim or (b) report the fraction of realizations exceeding σ = 3.

    Authors: We agree with the referee that the Δγ₁ ≃ 0.2 claim is at the edge of distinguishability and should be qualified. We have re-examined the 50 realizations for the Δγ₁ = +0.2 case at 50 h exposure. For Geminga, approximately 80% of realizations exceed σ = 3, with the median around σ ≈ 4. For Monogem, the fraction is comparable. We will implement both suggested remedies: (i) we will report the fraction of realizations exceeding σ = 3 in the text of §3.3, and (ii) we will soften the abstract and conclusions to state that Δγ₁ ≃ 0.2 is 'at the edge of distinguishability under optimistic one-parameter-at-a-time assumptions, with roughly ~80% of realizations exceeding the 3σ threshold.' This accurately conveys both the promise and the marginality of this forecast. revision: yes

  2. Referee: §3.3, paragraph 2 and Eq. (3)/Table 1: The authors argue that γ₁, B, and R_SDZ are 'not intrinsically degenerate' because each affects a different observable. However, B and D_SDZ are coupled through Eq. (3): changing B alters τ_cool, which changes the inferred D_SDZ (Table 1), which reshapes the spatial profile. This coupling could partially mimic the effect of varying R_SDZ or γ₁ in a joint fit. The paper does not test whether this coupling produces significant parameter correlations.

    Authors: The referee correctly identifies a coupling that we should have discussed explicitly. The mechanism is as follows: changing B alters τ_cool via Eq. (3), which changes the D_SDZ required to reproduce the observed R_diff (Table 1), which in turn reshapes the spatial profile. We agree that this coupling could in principle partially mimic the effect of varying R_SDZ or γ₁ in a joint fit. However, we can offer a quantitative argument for why the impact on our one-at-a-time results is limited. First, the B–D_SDZ coupling acts primarily on the overall diffusion coefficient normalization, which sets the halo size at a given energy. The injection index γ₁, by contrast, primarily reshapes the energy spectrum in the central region—a spectral rather than purely morphological effect. Second, the R_SDZ variation produces a characteristic change in the radial surface-brightness profile at the transition radius, which is a spatially localized feature distinct from the smooth rescaling produced by the B–D_SDZ coupling. Third, from Table 1, the D_SDZ variation across B = 1–3 μG is a factor of ~2.5, while the R_SDZ variations we explore (30–90 pc) change the spatial scale by a factor of 3. The morphological signatures are therefore distinguishable in principle. That said, we agree that a joint fit could exhibit non-negligible correlations, and our one-at-a-time results represent an optimistic upper bound on constraining power (as already stated in the manuscript). We will add a paragraph to §3.3 discussing this specific coupling and the above quantitative arguments, and we will note that a full joint-fit analysis with parameter correlation contours is a natural next step. revision: yes

Circularity Check

0 steps flagged

No significant circularity; one minor self-citation for framework extension that is not load-bearing for the forecasting results.

full rationale

The paper's central claims are forward-modeling forecasts: it calibrates a two-zone diffusion model to existing HAWC/Fermi-LAT data (fitting the injection efficiency η to surface brightness profiles in §2.2.1, and deriving D_SDZ from HAWC angular extension via Eq. 3), then forward-folds through CTAO IRFs to predict detection significance and parameter sensitivity. This is standard instrument forecasting—the calibration to existing data is explicitly stated and is not presented as a first-principles prediction. The detection significance (13–30σ) and parameter sensitivity (Δγ₁≈0.2, ΔB≈2μG) are genuine forward predictions about what CTAO would observe, not quantities that are definitionally equivalent to the HAWC/Fermi-LAT inputs. The one self-citation (Li et al. 2025) provides the Monogem halo framework that is extended here to Geminga; this is a methodological foundation citation, not a load-bearing claim that reduces to itself. The paper is self-contained against external benchmarks (HAWC, Fermi-LAT, AMS-02 data) and the CTAO IRFs are official external products. The one-parameter-at-a-time sensitivity analysis is acknowledged as optimistic (§3.3), but this is a modeling limitation, not circularity. No step in the derivation chain reduces to its inputs by construction.

Axiom & Free-Parameter Ledger

6 free parameters · 6 axioms · 0 invented entities

No new physical entities are postulated. The 'slow diffusion zone' is a phenomenological construct inherited from prior work (Jóhannesson et al. 2019; Abeysekara et al. 2017), not a new postulated particle, field, or dimension.

free parameters (6)
  • η (injection efficiency) = 0.04–0.52 (source-dependent, see Fig. 1)
    Fitted to HAWC surface brightness profiles in the 5.4–68.7 TeV range for each combination of γ₁ and B.
  • γ₁ (high-energy injection index) = 1.8, 2.0, 2.2, 2.4 (explored grid)
    Explored as a grid; benchmark values are 2.2 (Geminga) and 1.8 (Monogem).
  • B (magnetic field strength) = 1, 2, 3 μG (explored grid)
    Explored as a grid; benchmark is 1 μG. Constrained by X-ray upper limits but not uniquely determined.
  • R_SDZ (slow diffusion zone radius) = 30–90 pc (explored grid)
    Explored as a grid; benchmark is 50 pc. Lower limit ~25 pc from HAWC extension; Fermi-LAT gives lower limit ~50 pc for Geminga.
  • D_SDZ (diffusion coefficient in SDZ) = 1.91–4.84 × 10²⁷ cm²/s at 100 TeV (see Table 1)
    Derived from HAWC angular extension via Eq. 3, dependent on B through cooling time.
  • α_irr (CR background nuisance normalization) = fitted in likelihood
    Nuisance parameter multiplying residual CR background template to absorb IRF mismodeling.
axioms (6)
  • domain assumption Two-zone isotropic diffusion model with sharp transition adequately describes particle transport around middle-aged pulsars.
    Invoked throughout §2.1; Eq. 6 defines the spatial dependence. Anisotropic diffusion and turbulence coherence length effects are acknowledged but not included (§4).
  • domain assumption Kolmogorov turbulence spectrum with δ = 0.35.
    Stated in §2.1 after Eq. 5. Other turbulence spectra (e.g., Kraichnan, δ=0.5) are not explored.
  • domain assumption No significant bulk advection in the Geminga/Monogem regions.
    Stated in §2.1: 'we assume no significant bulk advection in the Geminga/Monogem regions.'
  • standard math Braking index n = 3 (idealized magnetic dipole).
    Stated in §2.1 after Eq. 1. Standard assumption for pulsar spin-down modeling.
  • domain assumption HAWC angular extension measurements accurately reflect the electron diffusion length.
    Used in §2.1 to derive D_SDZ via Eq. 3. The authors note that morphology-aware methods give consistent results (Albert et al. 2024).
  • domain assumption The irreducible CR background rate predicted by CTAO IRFs is sufficiently accurate for extended-source analysis.
    Used throughout the mock data generation (§2.3.2, Eq. 7). The authors flag this as a practical challenge in §3.2.1.

pith-pipeline@v1.1.0-glm · 25007 in / 6217 out tokens · 397138 ms · 2026-07-07T21:47:36.059451+00:00 · methodology

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read the original abstract

TeV halos around Geminga and Monogem (B0656+14) reveal regions of strongly suppressed diffusion near middle-aged pulsars. Determining the sizes and magnetic-field strengths of these slow-diffusion zones is important for modelling lepton transport and assessing their contribution to the local positron excess. The Cherenkov Telescope Array Observatory (CTAO) will provide arcminute-scale imaging of extended gamma-ray emission. We construct two-zone diffusion models for Geminga and Monogem with GALPROP v57, injecting electrons and positrons according to the pulsar spin-down history and following their propagation through an evolving slow-diffusion bubble and the ambient interstellar medium. Calibrating to HAWC and Fermi-LAT measurements, we forward-fold the models through official CTAO instrument response functions, including realistic diffuse and instrumental backgrounds. For 50 h observations, both halos are detected with high significance ($\sim 13$--$30,\sigma$) from either site. Under optimistic background assumptions, CTAO can distinguish changes of $\Delta\gamma_1 \simeq 0.2$ in the high-energy injection index and $\Delta B \simeq 2,\mu{\rm G}$ in magnetic-field strength. For Monogem, compact bubbles with $R_{\rm SDZ}\approx30,{\rm pc}$ can be distinguished from extended ($\gtrsim50,{\rm pc}$) cases, although very large bubbles remain degenerate because of the finite field of view. Using an alternative Galactic diffuse template in the fit changes detection significances and parameter sensitivities by $\lesssim10%$, indicating robustness against plausible diffuse-background mismodelling.

Figures

Figures reproduced from arXiv: 2607.05245 by Manuela Vecchi, Oscar Macias, Shinichiro Ando, Youyou Li.

Figure 1
Figure 1. Figure 1: Surface brightness of IC emission in the 5.4–68.7 TeV range for the Geminga halo (top panel) and the Monogem halo (bottom panel), each fitted jointly to account for the mutual contamination between the two sources. In both panels, curves correspond to different electron injection indices 𝛾1 = 1.8, 2.0, 2.2, 2.4 of the primary source, with magnetic field 𝐵 = 1 𝜇G and SDZ size 𝑅SDZ = 50 pc fixed. The injecti… view at source ↗
Figure 3
Figure 3. Figure 3: Intensity maps at 50 GeV and 100 TeV of Geminga (top panels) and Monogem (bottom panels) pulsars using benchmark parameter configurations. Geminga exhibits a large asymmetry in 50 GeV due to the pulsar’s proper motion (panel (a)). sured beyond TeV energy. For consistency with the Galactic diffuse model, we adopt the analysis result based on Fermi-LAT data up to ∼800 GeV (Abdollahi et al. 2020). A power-law… view at source ↗
Figure 5
Figure 5. Figure 5: Average diffuse 𝛾-ray flux within a 2.15◦ radius region centered on the Geminga halo, comparing the two Galactic diffuse background models used in this work. GDB Model 1 (default) follows the Fermi-LAT interstellar emission model with a TeV-scale extrapolation, while GDB Model 2 (alter￾native) adopts the 3D CR and ISRF-based diffuse emission model of Porter et al. (2017). The discrepancy between the two mo… view at source ↗
Figure 4
Figure 4. Figure 4: Positron flux expected at Earth from the Geminga halo (top panel) and the Monogem halo (bottom panel) for different magnetic-field strengths 𝐵. We assume a benchmark SDZ radius of 𝑅SDZ = 50 pc and injection indices of 𝛾1 = 2.2 for Geminga and 𝛾1 = 1.8 for Monogem. The predicted positron flux is compared to the 7-year AMS-02 measurement (Aguilar et al. 2013). and SST observations (see [PITH_FULL_IMAGE:figu… view at source ↗
Figure 6
Figure 6. Figure 6: Average flux of 2.15◦ radius region centered at the Geminga halo (top panel), and Monogem halo (bottom panel), compared to the intrinsic astrophysical background components. The source flux dominates the back￾ground components for both sources at above TeV energies. separately as an instrumental component, following Eq. 7. Φastro (𝜃true, 𝐸true) = Φtarget(𝜃true, 𝐸true) (9) + ΦGDB(𝜃true, 𝐸true) (10) + Φiso (… view at source ↗
Figure 7
Figure 7. Figure 7: Detection significance of the Geminga halo by the CTAO. One model parameter is varied at a time while the remaining parameters are fixed at their benchmark values (𝛾1 = 2.2, 𝐵 = 1 𝜇G, 𝑅SDZ = 50 pc). Results for a 5 h exposure are shown to illustrate how the detection significance improves with observing time; this represents an extremely conservative exposure for the CTAO. Each data point is derived from f… view at source ↗
Figure 8
Figure 8. Figure 8: Detection significance of the Monogem halo by the CTAO. One model parameter is varied at a time while the remaining parameters are fixed at their benchmark values (𝛾1 = 1.8, 𝐵 = 1 𝜇G, 𝑅SDZ = 50 pc). Results for a 5 h exposure are shown to illustrate how the detection significance improves with observing time; this represents an extremely conservative exposure for the CTAO. Each data point is derived from f… view at source ↗
Figure 9
Figure 9. Figure 9: Detection significance of the Geminga halo by CTAO-South by fitting to the true astrophysical background and the alternative background. One model parameter is varied at a time while the remaining parameters are fixed at their benchmark values (𝛾1 = 2.2, 𝐵 = 1 𝜇G, 𝑅SDZ = 50 pc). Results for a 5 h exposure are shown to illustrate how the detection significance improves with observing time; this represents a… view at source ↗
Figure 10
Figure 10. Figure 10: Detection significance of the Monogem halo by CTAO-South by fitting to the true astrophysical background and the alternative background. One model parameter is varied at a time while the remaining parameters are fixed at their benchmark values (𝛾1 = 1.8, 𝐵 = 1 𝜇G, 𝑅SDZ = 50 pc). Results for a 5 h exposure are shown to illustrate how the detection significance improves with observing time; this represents … view at source ↗
Figure 11
Figure 11. Figure 11: Discriminability of source parameters for the Geminga halo based on mock CTAO observations. Each panel shows violin distributions of the variability in the distinguishability metric 𝜎 when varying a single source parameter: electron injection index 𝛾1 (left), magnetic-field strength 𝐵 (middle), and diffusion-zone radius 𝑅SDZ (right). The true model (used to generate the mock data) is defined by 𝛾1 = 2.2, … view at source ↗
Figure 12
Figure 12. Figure 12: Discriminability of source parameters for the Monogem halo based on mock CTAO observations. Each panel shows violin distributions of the variability in the distinguishability metric 𝜎 when varying a single source parameter: electron injection index 𝛾1 (left), magnetic-field strength 𝐵 (middle), and diffusion-zone radius 𝑅SDZ (right). The true model (used to generate the mock data) is defined by 𝛾1 = 1.8, … view at source ↗

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Reference graph

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