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The JWST Early Release Science Program for Direct Observations of Exoplanetary Systems VIII: patchy forsterite and enstatite clouds in the atmosphere of VHS 1256 b, retrieval lessons learned and outlook to the future

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims that the full 1-18 micron JWST spectrum of VHS 1256 b is best reproduced by patchy forsterite and enstatite clouds over an iron deck, with a cloud coverage fraction of 0.985 and a ΔBIC of 297 over uniform clouds.

desk verdict A transparent first retrieval of the full JWST spectrum of VHS 1256 b, but the headline patchy-cloud result rests on an ad hoc 10x NIRSpec error inflation and should not be read as robust until the model ranking is repeated with native errors. read the letter →

arxiv 2608.06583 v1 pith:SNNNTQOY submitted 2026-08-06 astro-ph.EP

Niall Whiteford , Jacqueline K. Faherty , Ben Burningham , Johanna M. Vos , Simon Petrus , Polychronis Patapis , Beth A. Biller , Andrew Skemer
show 118 more authors
Sasha Hinkley Emily Calamari Genaro Suárez Kelle L. Cruz Brittany E. Miles Aarynn L. Carter Francisco A. Martinez Melanie J. Rowland Olivier Absil Arthur D. Adams William O. Balmer Anthony Boccaletti Mariangela Bonavita Mickaël Bonnefoy Mark Booth Brendan P. Bowler Zackery W. Briesemeister Marta L. Bryan Per Calissendorff Faustine Cantalloube Benjamin Charnay Gaël Chauvin Christine H. Chen Elodie Choquet Valentin Christiaens Gabriele Cugno Thayne Currie Camilla Danielski Matthew De Furio Trent J. Dupuy Samuel M. Factor Michael P. Fitzgerald Jonathan J. Fortney Kyle Franson Julien H. Girard Eileen C. Gonzales Carol A. Grady Thomas Henning Dean C. Hines Callie E. Hood Kielan K. W. Hoch Alex R. Howe Markus Janson Paul Kalas Jens Kammerer Grant M. Kennedy Pierre Kervella Minjae Kim Daniel Kitzmann Adam L. Kraus Masayuki Kuzuhara Pierre-Olivier Lagage Anne-Marie Lagrange Kellen Lawson Cecilia Lazzoni Jarron M. Leisenring Ben W. P. Lew Michael C. Liu Pengyu Liu Jorge Llop-Sayson James P. Lloyd Anna Lueber Bruce Macintosh Mathilde Mâlin Elena Manjavacas Sebastián Marino Mark S. Marley Christian Marois Raquel A. Martinez Elisabeth C. Matthews Brenda C. Matthews Dimitri Mawet Johan Mazoyer Michael W. McElwain Stanimir Metchev Michael R. Meyer Maxwell A. Millar-Blanchaer Paul Mollière Sarah E. Moran Caroline V. Morley Sagnick Mukherjee Paulina Palma-Bifani Eric Pantin Marshall D. Perrin Laurent Pueyo Sascha P. Quanz Andreas Quirrenbach Shrishmoy Ray Isabel Rebollido Jea Adams Redai Bin B. Ren Emily Rickman Steph Sallum Matthias Samland Benjamin Sargent Joshua E. Schlieder Karl R. Stapelfeldt Jordan M. Stone Ben J. Sutlieff Motohide Tamura Xianyu Tan Christopher A. Theissen Pascal Tremblin Taichi Uyama Malavika Vasist Arthur Vigan Kevin Wagner Jason J. Wang Kimberly Ward-Duong Schuyler G. Wolff Kadin Worthen Mark C. Wyatt Marie Ygouf Alice Zurlo Xi Zhang Keming Zhang Zhoujian Zhang Yifan Zhou
This is my paper · ORCID
classification astro-ph.EP
keywords atmosphericretrievalcloudpatchinesssilicatecloudsforsteriteenstatiteVHS1256bJWSTspectroscopybrowndwarfatmospheres
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 analyzes the full $1$--$18\,\mu$m JWST spectrum of the young planetary-mass companion VHS 1256 b with a 1D atmospheric retrieval model, asking which cloud species and structures best explain the data. It claims that the spectrum is best described by two patchy silicate cloud layers, forsterite ($\mathrm{Mg}_2\mathrm{SiO}_4$) and enstatite ($\mathrm{MgSiO}_3$), sitting above a deep iron deck, with a cloud coverage fraction of 0.985. The preference for patchy clouds over uniform clouds is reported as $\Delta\mathrm{BIC}=297$. The retrieval also constrains the abundances of H$_2$O, CO, CO$_2$, CH$_4$, and NH$_3$, yields an approximately solar C/O ratio, and returns a temperature-pressure profile that is more isothermal (less adiabatic) than self-consistent cloud models predict. The authors caution that these conclusions depend on how the relative signal-to-noise of the two instruments is treated.

What carries the argument

The argument is carried by a flexible 1D retrieval framework that couples scattering radiative transfer with a five-parameter analytic temperature-pressure parameterization, free molecular abundances, and slab or deck cloud prescriptions. A slab is a finite cloud layer whose base pressure, top pressure, and optical depth are retrieved; a deck is an optically thick cloud whose top pressure and a decay-height scale are retrieved. Patchiness is implemented by linearly combining a fully cloudy and a fully clear model spectrum, weighted by a retrieved coverage fraction. Cloud particle sizes follow a Hansen distribution with retrieved effective radius and spread. Model comparison is performed with the Bayesian Information Criterion (BIC), and the relative weighting of the two instruments is controlled by inflating the NIRSpec error bars by a factor of 10 and by applying per-order tolerance factors.

What would settle it

Run the same retrieval with native NIRSpec error bars: if the MIRI silicate feature is no longer fit and the patchy forsterite-plus-enstatite model no longer beats uniform clouds by $\Delta\mathrm{BIC}>10$, the headline conclusion rests on the error-inflation choice rather than on the data themselves.

Watch

Extended reading notes

Core claim

The central claim is that the $1$--$18\,\mu$m spectrum of VHS 1256 b is best reproduced by an atmosphere with an iron cloud deck deep in the photosphere and two patchy, vertically extended silicate slab clouds, forsterite and enstatite, near the $10^{-1}$ to $10^{-3}$ bar region. The patchy geometry is strongly preferred over uniform clouds ($\Delta\mathrm{BIC}=297$), with a retrieved cloud coverage fraction of $0.985^{+0.012}_{-0.022}$ (about 1.5 percent cloud-free area). A combination with quartz ($\mathrm{SiO}_2$) or a single silicate species ranks lower. Alongside the cloud structure, the analysis yields molecular abundance constraints for H$_2$O, CO, CO$_2$, CH$_4$, and NH$_3$, a C/O ratio slightly above solar after correcting for oxygen locked in clouds, a radius of $1.29\,R_{\mathrm{Jup}}$, and a non-adiabatic temperature-pressure profile. The paper emphasizes that this cloud ranking is contingent on the adopted data treatment, specifically the decision to inflate the NIRSpec error bars by a factor of 10 so that NIRSpec and MIRI have comparable weight.

Load-bearing premise

The central cloud ranking follows from a data treatment in which the NIRSpec error bars were inflated by a factor of 10 to lower their weight relative to MIRI; if that re-weighting is unjustified, the preference for patchy forsterite and enstatite clouds could disappear.

Editorial extensions

If this is right

  • VHS 1256 b's atmosphere would be nearly fully cloud-covered (98.5 percent), with a 1.5 percent cloud-free fraction that is consistent with its extreme spectral variability arising from small clearings rather than large holes.
  • The retrieved silicate clouds sit higher and are more vertically extended than those inferred for slightly cooler and warmer comparison objects, supporting a picture in which low surface gravity and vertical mixing loft cloud material.
  • The constraint on NH$_3$ in an L-type object extends the spectral range over which ammonia can serve as a diagnostic of substellar atmospheric chemistry.
  • Because retrieved parameters shift strongly under alternative data treatments, the paper urges caution against over-interpreting precision without first checking sensitivity to the choice of data and instrument weighting.
  • If the patchy two-silicate structure is correct, the 10 micron silicate feature should vary in step with molecular band strengths as cloud-free patches rotate into view.

Reading between the lines

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

  • The factor-of-10 error inflation may encode a prior about the relative trustworthiness of NIRSpec versus MIRI, so a retrieval that treats instrument weights as free parameters could either confirm or erase the preferred model ranking.
  • The retrieved non-adiabatic temperature-pressure profile, if confirmed at native resolution, would strengthen the thermochemical-instability explanation for very red L/T objects, a competing theory to clouds alone.
  • Because the retrieved cloud coverage fraction is so close to one, the model predicts that rotational variability is dominated by the few clear patches; comparing synthetic light curves against the observed 25-38 percent amplitude variations is a sharp test of the patchy geometry.
  • The low retrieved enstatite optical depth combined with very small particle sizes hints that the assumed Hansen size distribution may be smoothing over a bimodal grain population, which microphysical cloud models could clarify.
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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

4 major / 5 minor

Summary. This paper presents the first retrieval analysis of the full 1-18 micron JWST ERS #1386 spectrum of VHS 1256 b, using the Brewster framework with data convolved to R=300. The authors test four cloud scenarios combining an iron deck with uniform or patchy forsterite, enstatite, and quartz slabs. Using Delta BIC, they conclude that the data are best described by patchy forsterite and enstatite slabs over an iron deck (Delta BIC = 297, scenario 4), with a cloud coverage fraction of 0.985. They also report constraints on H2O, CO, CO2, CH4, and NH3, a solar C/O ratio, and a non-adiabatic temperature-pressure profile. In a separate test (Section 6), they show that retrieved parameters shift strongly when the NIRSpec error bars are not inflated or when only NIRSpec data are used, and they caution that retrieval constraints should not be over-interpreted.

Significance. If the central claim is robust, this would be an important step in characterizing the L/T transition for a young, planetary-mass companion with silicate clouds, and it would demonstrate the value of combining NIRSpec and MIRI data for cloud-species identification. The paper is commendably transparent about the data treatment: it explicitly documents the order-of-magnitude inflation of NIRSpec error bars, the rationale for this choice, and the sensitivity of the results to data selection (Section 6, Figure 12). The authors also openly acknowledge that the winning cloud model is 'by no means the final word' (Section 7.6). However, the headline conclusion about patchy silicates is computed under a post-hoc reweighting of the data, and the paper's own sensitivity test shows that retrieved posteriors do not overlap across data treatments. The significance of the result therefore hinges on whether the error-inflation choice can be justified or marginalized over, which the manuscript does not currently demonstrate.

major comments (4)
  1. [Section 4.2.2 and Section 5.1] The abstract and Section 5.1 state a 'strong preference for patchy silicate cloud coverage' (Delta BIC = 297) computed with NIRSpec V2 error bars inflated by a factor of 10. This inflation was introduced post-hoc after early V2 retrievals overfit NIRSpec and ignored the MIRI silicate feature. Because the likelihood is Gaussian, inflating NIRSpec errors by 10x reduces the weight of those data by 100x in the exponent, so both the Delta BIC values in Table 2 and the best-fit parameters in Section 5 are properties of this chosen reweighting rather than of the raw data. The paper's own Section 6 shows that using native errors or NIRSpec-only data yields non-overlapping posteriors (Figure 12). The authors should either marginalize over a per-instrument noise scaling (e.g., jitter) or present the model ranking under all data treatments with equal emphasis, and the headline claims must carry the conditionality explicitly.
  2. [Section 6, Figure 12] The sensitivity test in Section 6 demonstrates that retrieved parameters shift strongly across the three data treatments (inflated NIRSpec+MIRI, native NIRSpec+MIRI, and NIRSpec-only), including the temperature-pressure profile and goodness-of-fit. This test, however, was performed only with the uniform forsterite+iron cloud model, not with the preferred patchy forsterite+enstatite+iron scenario (scenario 4). It therefore does not directly establish whether the cloud-species ranking in Table 2 survives the choice of data treatment. The authors need to repeat at least the top-ranked scenarios under the alternative error treatments, or otherwise quantify the robustness of the Delta BIC ranking to the reweighting.
  3. [Section 4.3 and Table 2] The BIC calculation in Eq. (1) counts the explicit model parameters k but does not include the NIRSpec error-inflation factor of 10, which is a free parameter chosen by the authors and varied during the analysis. Since the Gaussian likelihood depends on this factor, the Delta BIC values in Table 2 are conditional on an unmodeled choice; effectively the model comparison has an extra degree of freedom that is not penalized. This makes the reported 'very strongly indicative' Delta BIC = 297 overconfident. A fitted noise model or a sensitivity analysis across a range of inflation factors is needed before the model ranking can be considered robust.
  4. [Section 5.6 and Table 1] The retrieved surface gravity and mass converge to the upper limit of the prior (log g ~ 4.68, mass ~ 32 M_Jup, against a prior bound of 35 M_Jup), as the authors acknowledge. These values are therefore prior-dominated, yet Table 1 and the summary in Section 8 present them as retrieved constraints with quoted uncertainties. This is misleading; the appropriate scientific statement is that the data only place an upper limit on mass and log g. The paper should recast these as upper limits or otherwise discuss the prior sensitivity explicitly in the main results.
minor comments (5)
  1. [Section 5.1] The text states a cloud-free fraction of '0.0155%', but 1 - 0.985 = 0.015, which is 1.5%, not 0.0155%. This appears to be a factor-of-100 error and should be corrected.
  2. [Table 2] The third row lists 'Mg2SiO3 Slab' as a cloud species; this is likely a typo for Mg2SiO4 (forsterite) or MgSiO3 (enstatite). The species name should be checked against the text and corrected.
  3. [Figure 12] The corner plot in Figure 12 would benefit from axis labels and a clear legend distinguishing the three data treatments, so that the non-overlap of the posteriors is immediately visible to the reader.
  4. [Section 4.2.2] The phrase 'deflated the SNR' is confusing; the operation described is an inflation of the error bars, which reduces the signal-to-noise ratio. Rephrasing as 'inflated the error bars' would be clearer.
  5. [General] The manuscript does not state the version of Brewster used or provide a reproducibility statement with the exact configuration files and random seeds. Given the complexity of the retrieval setup and the known sensitivity to settings, a reproducibility statement would help the community verify and build on these results.

Circularity Check

1 steps flagged · score 6.0 of 10

The patchy forsterite/enstatite preference (ΔBIC=297) is computed under a 10x NIRSpec error inflation that was introduced specifically to make the MIRI silicate feature fit; Section 6 shows the ranking is not robust to this input.

  1. fitted input called prediction [Section 4.2.2 (Error bars) and Section 5.1 (best fitting cloud model), with Table 2]
    "Early in our retrieval analysis with the V2 reduction, we encountered clear issues with the weighting of the NIRSpec data vs. the weighting of the MIRI data. These early V2 retrievals clearly focused on fitting ... the NIRSpec data and almost entirely ignored ... the silicate feature at 8.5-10 microns. As a result ... we increased the error bars (deflated the SNR) of the NIRSpec V2 data by a factor of 10 ... allowed for an improved model fit across the MIRI dataset, particularly the silicate feature near 10um. ..."

    All six cloud-model rankings in Table 2, including the headline ΔBIC=297 preference for the patchy forsterite+enstatite+Fe scenario, are computed under the Gaussian likelihood with NIRSpec V2 error bars inflated by 10x (Section 4.2.2). That inflation was not derived from an independent noise model; it was adopted after seeing that native-weight retrievals ignored the MIRI 8.5-10 micron silicate feature, and it was kept because it allowed that feature to be fit. Because BIC = k ln(n) − 2 ln L, the relative weighting of NIRSpec versus MIRI data enters directly into every ΔBIC value, so the model ranking is a function of the hand-chosen inflation factor.

full rationale

The paper is largely self-contained as a retrieval application: the Brewster framework and cloud scenarios are adopted from earlier same-author papers (Burningham et al. 2021; Vos et al. 2023), but those citations are not used as a uniqueness theorem or to forbid alternative models; the cloud ranking is computed here from the JWST data, and the molecular constraints, radius, C/O, and T-P profile are new outputs. I do not flag self-citation as circular. The one substantive circularity-adjacent step is the 10x NIRSpec error inflation in Section 4.2.2: it is a model-input choice made after observing that native-weight retrievals failed to fit the MIRI silicate feature, and all headline ΔBIC values are computed under it. Section 6 and Section 7.6 explicitly demonstrate and acknowledge that retrieved parameters and even the ability to fit the silicate feature depend on this treatment, so the paper is transparent about the limitation; however, the abstract and Section 5.1 present the patchy forsterite/enstatite preference without this caveat. Because the central model-ranking claim is conditional on a hand-tuned reweighting selected to fit the feature that the claim then 'predicts,' the circularity score is set to 6 rather than 0-2. The remaining results (chemistry abundances, radius, C/O, T-P shape) are not circular in the same way, though they share the same data-treatment sensitivity.

Assumptions & free parameters 10 free parameters · 8 assumptions · 0 invented entities

The central claim is supported almost entirely by parameters fitted to the JWST spectrum: cloud optical depths, pressures, particle sizes, coverage fraction, molecular abundances, TP profile shape, and error-scale factors. The most consequential free parameter is the hand-chosen 10x NIRSpec error inflation, which is not fitted to data but is adjusted ad hoc and directly controls the model ranking. No new physical entities are introduced. The key axioms are the 1D, static, constant-abundance modeling assumptions and the adequacy of the chosen cloud and TP parameterizations, all of which the paper itself partially questions.

free parameters (10)
  • NIRSpec error inflation factor = 10x (chosen by hand, not fitted)
    Applied in Sec 4.2.2 to deflate NIRSpec SNR so the MIRI silicate feature is weighted comparably; this choice directly sets up the model ranking that yields the central cloud claim.
  • Cloud coverage fraction (C_frac) = 0.985 +0.012/-0.022
    Patchy cloud fraction from Eq 2; the headline 'patchy' result.
  • Forsterite slab optical depth = tau_1um = 5.21 +0.90/-1.83
    Retrieved silicate cloud opacity for the preferred model.
  • Enstatite slab log10 optical depth = -2.36 +0.13/-0.13
    Very low retrieved opacity; claimed to contribute to the silicate feature despite being nearly transparent.
  • Iron deck top pressure = log P = 0.46 +0.07/-0.08 bar
    Position of the optically thick iron deck beneath the silicate slabs.
  • Molecular abundances (H2O, CO, CO2, CH4, NH3, and 7 unconstrained species) = e.g., log H2O = -3.50, log CO = -3.00, log CO2 = -6.73, log CH4 = -5.44, log NH3 = -5.86
    Free-chemistry mixing ratios, constant with pressure; five are constrained, the rest are not.
  • TP profile parameters (alpha1, alpha2, P1, P3, T3) = Effectively an isothermal retrieved profile
    Madhusudhan and Seager 2009 parameterization; drives the non-adiabatic retrieved gradient seen in Fig 6.
  • b error tolerance factors = 4 factors (one per NIRSpec order plus one for all MIRI)
    Foreman-Mackey et al. 2013 error scaling, fitted within the retrieval.
  • Surface gravity, radius, inferred mass = log g = 4.68 +0.03/-0.12, R = 1.29 R_Jup, mass = 32 M_Jup (pegged at 35 M_Jup prior edge)
    Bulk parameters retrieved from the spectrum; not independent measurements and mass/log g hit the prior boundary (Table 1 note).
  • Order scaling factors = 3 factors (G235H, G395H, and one for all MIRI; G140H anchors)
    Middle-ground treatment chosen in Sec 4.2.3 after per-order scaling was found to remove the silicate feature.
assumptions (8)
  • domain assumption Brewster's 1D two-stream scattering radiative transfer accurately models the observable disk of VHS 1256 b
    Section 4.1; the object is known to be highly variable and patchy, so a 1D plane-parallel model may misrepresent the disk-integrated spectrum.
  • domain assumption The Madhusudhan and Seager (2009) TP parameterization, with inversions forbidden, spans the true TP structure
    Section 4.1; the paper notes scatter and a lack of stability in retrieved TP profiles (Sec 5.5).
  • domain assumption Free chemistry with vertically constant mixing ratios is adequate for molecular abundance inference
    Section 4.1; the paper acknowledges this restricts the ability to capture pressure-dependent abundance changes (Sec 5.4).
  • domain assumption The slab/deck cloud parameterization plus the linear patchiness formula (Eq 2) captures the cloud structure
    Section 4.4; the paper itself calls the recipe a simplification of a 4D world (Sec 7.6).
  • ad hoc to paper NIRSpec error inflation by 10x is an appropriate way to balance instrument weighting for model comparison
    Section 4.2.2; no independent justification is provided, and the model ranking changes without it.
  • domain assumption Opacity data (Freedman et al. 2008, 2014) and condensate opacities (Scott and Duley 1996; Wakeford and Sing 2015) are accurate over 1-18 micron
    Section 4.1; the molecular and cloud opacity sources are taken from the literature without validation against this object.
  • domain assumption BIC is a valid criterion for ranking these models
    Section 4.3 uses Kass and Raftery thresholds; the paper later notes BIC has known drawbacks (Sec 7.3, citing Welbanks et al. 2023 and Thorngren et al. 2025).
  • domain assumption The V2 data reduction (Miles et al. 2023) provides reliable absolute flux calibration across orders, with residual offsets captured by the fitted scaling factors
    Sections 3.2 and 4.2.3 discuss order stitching and scaling; the model assumes any calibration mismatch is absorbed by the scaling parameters.

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Cite this review

Pith. "Pith review of The JWST Early Release Science Program for Direct Observations of Exoplanetary Systems VIII: patchy forsterite and enstatite clouds in the atmosphere of VHS 1256 b, retrieval lessons learned and outlook to the future." pith.science (2026). https://pith.science/paper/SNNNTQOY

@misc{pith2026260806583,
  author       = {Pith},
  title        = {Pith review of: The JWST Early Release Science Program for Direct Observations of Exoplanetary Systems VIII: patchy forsterite and enstatite clouds in the atmosphere of VHS 1256 b, retrieval lessons learned and outlook to the future},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SNNNTQOY}},
  note         = {Machine review of arXiv:2608.06583}
}
abstract

JWST defines a new era for the data-driven approach of retrieval modelling, which has become a cornerstone tool for the statistical inference of exoplanetary and brown dwarf properties. The Early Release Science program #1386 observations of VHS 1256 b represent a huge jump in data quality, data quantity and spectral coverage for such objects. VHS 1256 b is a young, planetary mass and extremely variable companion that populates the enigmatic L/T cohort of substellar atmospheres. In this first retrieval analysis of the full 1 - 18 micron dataset, we apply the Brewster retrieval framework to the NIRSpec and MIRI spectroscopic observations of VHS 1256 b, exploring a variety of cloud species and structures. Using Delta(BIC) we find that the data is best described by a forsterite (Mg$_{2}$SiO$_{4}$) and enstatite (MgSiO$_{3}$) cloud combination. Our analysis shows a strong preference for patchy silicate cloud coverage, which aligns with VHS 1256 b's extensive and well documented spectral variability. Our retrieval is able to place constraints on the abundances of H$_{2}$O, CO, CO$_{2}$, CH$_{4}$ as well as NH$_{3}$. We also show that the retrieved parameters are sensitive to the data used and the relative signal-to-noise ratios between data from different instruments. We conclude with the next steps for the wider retrieval community to better understand young and cloudy exoplanetary atmospheres.

Figures

Figures reproduced from arXiv: 2608.06583 by the authors.

Figure 1
Figure 1. Color magnitude diagram illustrating the brown dwarf color evolution across the L (magenta) and T (blue) sequences. We have illustrated VHS 1256 b’s colors along with its spectral type counterparts including HR 8799 bcd and PSO 318. Also highlighted are 2MASSW J2224438- 015852 (mid L), SIMP J01365662+0933473 and 2MASS J21392676+0220226 (early Ts) as they have been the sub￾jects of the same Brewster retrieval recipe … view at source ↗
Figure 2
Figure 2. Illustration of the VHS 1256 b data used in this study. (Top) Full ERS #1386 program (V2) spectrum of VHS 1256 b with overlapping wavelengths between orders shown in grey. With 12 spectral orders populating the NIRSpec and MIRI data combination we have 11 overlaps present across the global spectrum. (Middle) NIRSpec data at its native resolution and the binned spectrum used in our retrieval analysis. (Bottom) MIRI d… view at source ↗
Figure 3
Figure 3. This resulted in NIRSpec and MIRI data hav￾ing a much more comparable SNR. We found that increasing the error bars of the NIR￾Spec data to more closely match that of MIRI (see Fig￾ure 3) allowed for an improved model fit across the MIRI dataset, particularly the silicate feature near 10 µm. As mentioned previously we convolved the JWST data from their native resolutions to a constant R=300. The resulting errors for … view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: This figure outlines the various cloud scenarios we investigated for VHS 1256 b. (a) illustrates scenario 1 which combined an iron deck with a uniform silicate slab cloud. (b) illustrates scenario 2 which combined an iron deck cloud with 2 uniform silicate slab clouds.…
Figure 5
Figure 5. Figure 5: Best fits from our retrieval analysis of VHS 1256 b. We show the global fit along with a zoomed illustration to NIRSpec/G140H, NIRSpec/G235H, NIRSpec/G395H and the MIRI Channels 1, 2 and 3 data. We note that this fit is to the spectrum when binned to resolution R=300 a…
Figure 6
Figure 6. Figure 6: (Left) The retrieved temperature pressure profile. For comparison we have also illustrated several temperature pressure profiles from self-consistent grid models Sonora Diamondback with fsed=3. Condensate curves of relevant species explored in this work are also indica…
Figure 7
Figure 7. Figure 7: Top: Comparison of retrieved cloud position for VHS 1256 b and objects from Burningham et al. (2021) and Vos et al. (2023). Middle and Bottom: Comparison of retrieved particle size distributions for silicate and iron clouds for VHS 1256 b and objects from Burningham et…
Figure 8
Figure 8. Figure 8: Corner plots of retrieved values for the retrieval preferred model fit to VHS 1256 b. Bottom left: retrieved posteriors of molecular chemistry. Top right: retrieved posteriors of bulk parameters and spectral scaling factors. We note that Radius (R), Mass (M), C/O and […
Figure 10
Figure 10. Figure 10: This figure outlines a potential cloud scenarios we didn’t investigate for VHS 1256 b. Within our retrieval analysis we included a list of 12 molecules for abundance measurements. Our best fit re￾trieval places tight constraints on H2O, CO, CH4, CO2 and NH3 – see corn…
Figure 11
Figure 11. Figure 11: Retrieved molecular vertical mixing ratios (green solid line) compared to the vertical mixing profiles predicted by a thermochemical equilibrium model (green dashed lines). Grey shading indicates the photospheric region. et al. 2024). For this experiment we used Brews…
Figure 12
Figure 12. Figure 12: Bottom left: Comparison corner plot of retrieved modelled parameters which are well constrained using 3 different data inputs. Top Right: Comparison of the retrieval spectral fit to the NIRSpec data when employing both NIRSpec and MIRI data vs only NIRSpec data. Middl…
Figure 13
Figure 13. Figure 13: Corner plot of retrieved cloud parameter values for the preferred model fit to VHS 1256 b. (1) denotes the parameters of the Mg2SiO4 slab, (2) denotes the parameters of the MgSiO43 slab and (3) denotes the parameters of the Fe deck. Also shown is the cloud-free patch …

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.