Clouds of Fluffy Aggregates: How They Form in Exoplanetary Atmospheres and Influence Transmission Spectra

Kazumasa Ohno; Ryo Tazaki; Satoshi Okuzumi

REVIEW 3 major objections 5 minor 1 cited by

Clouds of Fluffy Aggregates: How They Form in Exoplanetary Atmospheres and Influence Transmission Spectra

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Fluffy, uncompressed mineral aggregates can loft exoplanet clouds far higher than compact spheres and explain GJ1214b's flat transmission spectrum.

desk verdict A credible and useful case that fluffy aggregate clouds can explain high-altitude clouds and flat spectra on GJ1214b, but the GJ1214b conclusion leans on Df=2 and free monomer size more than the validation admits. read the letter →

arxiv 1908.02201 v3 pith:Y6QLVUNA submitted 2019-08-06 astro-ph.EP

Kazumasa Ohno , Satoshi Okuzumi , Ryo Tazaki This is my paper · ORCID

classification astro-ph.EP

keywords fluffyaggregatesfractaldustexoplanetcloudscloudmicrophysicstransmissionspectraGJ1214bparticleporosityscatteringslope

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 sets out to show that cloud particles in exoplanetary atmospheres grow as porous, fractal aggregates rather than compact spheres, and that this porosity reshapes both cloud altitude and observed spectra. The authors build a porosity-evolution model for fractal growth, collisional compression, and gas-drag compression, then couple it to a cloud microphysical model. They find that, in the cases studied, the aggregates remain uncompressed with fractal dimension near 2, so their settling velocity is set by the tiny monomer size rather than the large aggregate size. The resulting fluffy clouds climb to pressures as low as roughly $10^{-5}$ bar, obscure molecular absorption, and imprint a characteristic scattering slope of $\alpha$ = -2. Applied to GJ1214b, the model reproduces the observed flat spectrum when the atmosphere is at least 100 times solar metallicity and monomers are smaller than 1 micron.

What carries the argument

The load-bearing identity is the fractal mass-size relation for aggregate growth, N = k0(ragg/rmon)^Df with Df = 2, which translates into a filling factor phi_frac = $N^{-1}$/2: as an aggregate doubles in monomer number, its radius grows only by a factor of about 1.4, leaving large voids. From this relation, the paper derives an equilibrium filling factor phi_eq = max[phi_frac, phi_drag, phi_coll] by comparing fractal growth with the thresholds for gas-drag compression and collisional compression, and an analytic cloud-top pressure P_top proportional to rmon/(Kz), independent of aggregate size. The companion machinery is the modified mean field theory for aggregate opacity, which yields an intermediate-wavelength scattering law sigma_s proportional to $r_agg^{2}$ $r_mon^{2}$ $lambda^{-2}$ times a logarithmic factor, the source of the $\alpha$ = -2 spectral slope.

What would settle it

A size-resolved microphysical simulation that follows the full particle size distribution and finds aggregate-monomer collisions dominate enough to push the fractal dimension above about 2.3 near the cloud base would contradict the model's high-altitude prediction; likewise, a transmission spectrum of GJ1214b that shows clear molecular features at 1-2 microns or a scattering slope steeper than $lambda^{-2}$ would falsify the fluffy-cloud explanation for its flat spectrum.

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Extended reading notes

Core claim

The central claim is that mineral cloud particles in exoplanetary atmospheres grow as low-density fractal aggregates and stay uncompressed through most of the cloud's lifetime, so their aerodynamic behavior is governed by the monomer radius rather than the aggregate radius. With fractal dimension Df = 2, the filling factor falls as $N^{-1}$/2 and the aggregate radius grows only as $N^{1}$/2, leaving a particle that is hundreds to thousands of times less dense than its material. Because such an aggregate settles slowly, the authors find that fluffy-aggregate clouds ascend to much higher altitude than compact-sphere clouds, reaching pressures near $10^{-5}$ bar for high metallicity and submicron monomers. In transmission, these high clouds largely hide molecular features in the visible and near-infrared while producing a spectral slope $\alpha$ = -2 from wavelength-dependent scattering by the aggregate structure. The paper concludes that the flat spectrum of GJ1214b can be explained by such fluffy KCl clouds if the atmospheric metallicity is at least 100 times solar and the monomer size is below 1 micron.

Load-bearing premise

The model's tallest load-bearing assumption is that collisions leave aggregates with the open, roughly two-dimensional structure of fractal dimension 2; if collisions instead pack grains into denser near-spheres, settling speeds up and the high-altitude fluffy cloud disappears, and the paper separately needs monomers smaller than one micron to reproduce GJ1214b.

Editorial extensions

If this is right

Fluffy mineral clouds will appear at far higher altitude than compact-sphere models predict, so spectral retrievals that assume compact particles will systematically misplace cloud decks.
Transmission spectra of such clouds will be largely featureless shortward of about 2 microns but increasingly transparent at longer wavelengths, making JWST and ARIEL capable of detecting molecular features that HST/WFC3 cannot see.
A scattering slope of alpha = -2 in the visible or near-infrared can serve as a potential observable signature of fractal cloud aggregates when the atmospheric scale height is known.
GJ1214b's flat spectrum can be explained without invoking anomalously strong eddy mixing, provided the atmosphere is metal-rich and the condensation nuclei are submicron.
A high-metallicity atmosphere for GJ1214b, if confirmed, would connect transmission spectra to the planet's gas accretion history and formation pathway.

Reading between the lines

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

The same porosity machinery likely applies to photochemical hazes on warm exoplanets, not just KCl clouds, but aerosol charging and Coulomb restructuring could make haze compression much easier than the model assumes.
The paper's requirement of submicron monomers sits in tension with classical nucleation theory, which predicts roughly 10 micron KCl particles; laboratory measurements of KCl nucleation rates could decide whether the fluffy-cloud scenario is even viable for GJ1214b.
The alpha = -2 slope may be degenerate with mixtures of small and large compact spheres, so distinguishing fluffy aggregates from compact-particle mixtures will likely require observations that cross the 2 pi r_agg wavelength regime or additional polarization information.
A direct comparative test would be to observe several super-Earths of similar temperature but different metallicities: if fluffy aggregates dominate, cloud-top altitude should track condensation-nucleus abundance and monomer size more strongly than it tracks eddy mixing strength.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

First thing to know: this is a real step forward for exoplanet cloud microphysics. The authors couple fractal aggregation and compression, already used in disk and haze work, to mineral cloud formation, and show that KCl aggregates in GJ1214b can stay uncompressed, settle slowly, and form clouds near 10^-5 bar. That offers a physical route to high-altitude clouds that does not need the enhanced eddy diffusion Gao & Benneke had to invoke. The alpha=-2 scattering slope from aggregate interference is a clean, testable observable, and the mid-IR window prediction is useful.

The porosity model is clearly built and the analytical compression thresholds (Eqs 18, 21, 22) are a nice contribution. The opacity treatment with MMF is appropriate; it reproduces T-matrix over the relevant size/wavelength range. The fit to HST/WFC3 is honest, and the chi2_red=1.16 at 1000x solar is comparable to the enhanced-Kz compact-sphere model. Credit where earned: the caveats are openly discussed.

Now the soft spots. The Df=2 assumption is load-bearing: the slow settling and high cloud top both follow from N=(r/r_mon)^2. If monomer-aggregate collisions dominate, Df approaches 3 and the whole picture shrinks. The Section 5.1.1 test is not a self-consistent calculation. It imposes a Hansen size distribution, uses a constant-density kernel, and does not feed porosity back into the collision rates, so it cannot prove Df stays near 2 in the model's own growth regime. The authors admit the size distribution is unknown and defer to a forthcoming paper. That is a genuine gap, though not a fatal one for the paper's main qualitative claim; it does weaken the specific GJ1214b conclusion. The monomer size is also free, and classical nucleation would give ~10 um, not the <1 um required. The absence of code is a reproducibility limitation, but equations are explicit enough to rebuild. I agree with the stress-test note that the validation does not close the loop, but I would not call the paper circular: the authors flagged it and the rest stands on standard porosity physics.

Who is this for: people working on exoplanet clouds, transmission spectra, and microphysics. It deserves serious peer review; the central mechanism is plausible and the opacity work is solid, but the load-bearing microphysics needs a size-resolved follow-up before we rely on the GJ1214b explanation.

Referee Report

3 major / 5 minor

Summary. The paper constructs a porosity evolution model for cloud particle aggregates (CPAs) in exoplanetary atmospheres, coupling fractal growth, gas-drag compression, and collisional compression with a 1D double-moment cloud microphysical model. It applies the model to KCl clouds in a GJ1214b-like atmosphere, computes vertical cloud profiles and transmission spectra, and compares the synthetic spectra with observations. The central claims are that (i) CPAs grow as uncompressed Df≈2 fractal aggregates, (ii) fluffy-aggregate clouds reach much higher altitudes than compact-sphere clouds, with cloud-top pressures near 10^-5 bar for high metallicity and submicron monomers, (iii) the resulting spectra show an aggregate-scattering slope α≈−2 and obscure molecular features, and (iv) the flat observed spectrum of GJ1214b can be reproduced for metallicities ≳100× solar and monomer radii <1 μm.

Significance. If the central assumptions hold, this is a significant contribution: it offers a microphysical mechanism for high-altitude mineral clouds that compact-sphere models failed to produce, and it makes falsifiable predictions (the α=−2 aggregate scattering slope and the reappearance of molecular features at λ≳2 μm) that upcoming JWST/ARIEL observations can test. The analytic compression thresholds (Eqs. 18, 21, 22) and the cloud-top pressure estimate (Eq. 36) are useful parameter-free or nearly parameter-free results. The aggregate opacity treatment uses the MMF theory benchmarked against T-matrix calculations, and the GJ1214b comparison is conducted with published data and a chi-square grid, which is appropriate. The main caveat is that nearly every qualitative result depends on the untested Df=2 growth assumption and on the free submicron monomer size, so the strength of the conclusions currently exceeds the strength of the supporting microphysical calculation.

major comments (3)

[Section 5.1.1, Eqs. (42)-(44), Figure 10] The Df=2 assumption is load-bearing: Eq. (5) sets the filling factor, Eq. (36) makes the cloud-top pressure independent of aggregate size, and the α=−2 slope in Section 4.1.2 follows from the Df=2 mass–size relation. The validation in Section 5.1.1 does not close the loop. The mass-weighted collision rate in Eq. (42) is evaluated for a Hansen size distribution with a chosen effective variance b, a constant-density collision kernel, and no porosity-dependent aggregate radius; the resulting Df≈1.9–2.1 is then borrowed from Okuzumi et al. (2009) rather than computed with the model's own size and porosity distributions. The kernel is not fed back with the Df it is supposed to justify. A self-consistent, size-resolved growth calculation that evolves both the size distribution and the porosity-dependent collision kernel is needed to rule out monomer-aggregate-dominated growth, which would drive Df toward 3, increase settling velocities, and remove the high-altitude cloud that drives all the paper's spectral conclusions.
[Section 4.4 and Section 5.1.3] The required monomer radius rmon<1 μm is not an innocuous free parameter. The paper itself notes in Section 4.4 that classical nucleation theory followed by condensation yields KCl particles with effective sizes of roughly 10 μm (citing Gao & Benneke 2018), an order of magnitude above the values that produce acceptable fits to GJ1214b. Since the high cloud deck, the α=−2 slope, and the reduced chi-square improvement all depend on submicron monomers, the agreement with observations is conditional on a nucleation pathway that is not modeled. The manuscript should either provide a quantitative heterogeneous-nucleation or size-reduction argument, present a sensitivity study showing how the fit degrades as rmon approaches 10 μm, or explicitly reframe the GJ1214b comparison as a proof-of-concept with rmon as a free parameter rather than as a predictive explanation.
[Section 3.1, Eqs. (23)-(24), and Section 3.2] The double-moment closure assumes a narrowly peaked mass distribution, and the authors acknowledge in Section 3.2 that the resulting size profile cannot capture the decrease of the mean size caused by removal of the largest particles. This is more than a presentation caveat for the central claim: the effective fractal dimension depends on which collision pairs dominate growth, and the dominant collision pair depends on the full size distribution. The imposed monodisperse closure may bias the model toward aggregate-aggregate collisions and thus toward Df=2. At minimum, the manuscript should quantify the sensitivity of the vertical cloud extent and Ptop to the width of the assumed size distribution, or state explicitly that the central assertion of uncompressed Df=2 growth is established only in the monodisperse limit.

minor comments (5)

[Figure 3 caption] The caption says 'The top, middle, and bottom rows' but the figure has four rows (1×, 10×, 100×, and 1000× solar); please update the caption.
[Section 3.2] There are typos in this section: 'the could scale height' should be 'the cloud scale height', and 'cluod' should be 'cloud'.
[Section 4.3, Eq. (41)] The numerical prefactor in Eq. (41) should be derived explicitly; the relation between the transit-depth slope S, the pressure scale height H, and the opacity power-law index α involves a geometric factor that the text invokes rather than proves. This does not affect the qualitative conclusions but should be checked for consistency.
[Section 5.2] The text contains the typo 'runnaway gas accretion'; it should read 'runaway gas accretion'.
[Figure 9 caption] The caption contains the typo 'chi-squred'; it should be 'chi-squared'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the Df=2 fractal-growth assumption is a stated physical input supported by external simulations, and the GJ1214b comparison is a forward-model fit, not a prediction forced by construction.

full rationale

The paper's central mechanism—fluffy, uncompressed aggregates reaching high altitude—follows from the explicitly stated Df=2 fractal-growth assumption (Equation 5), which is an input assumption rather than a quantity derived from the paper's own output. The cloud-top pressure expression (Equation 36) is derived from that assumption plus settling physics, so it is a consequence, not a disguised restatement of the input. The alpha=-2 scattering slope is taken from the analytic result of Berry & Percival (1986) and implemented through MMF theory validated against T-matrix calculations (Tazaki & Tanaka 2018); it is not fitted to the target spectrum. The GJ1214b comparison is a forward model with free parameters (metallicity, monomer size, reference radius) and is explicitly presented as a fit with reduced chi-square values, not as a prediction obtained by inverting observed data through the model. The authors' self-citations—Okuzumi et al. (2009) for Df in aggregate collisions, Kataoka et al. (2013a,b) for compression, and Tazaki & Tanaka (2018) for aggregate optics—are supporting external simulations or calculations whose assumptions and outputs do not include the target conclusion of this paper. Section 5.1.1's validation of Df=2 uses a mass-weighted collision-rate calculation and cites Okuzumi et al. (2009) for the resulting fractal dimension; while this validation is not a fully self-consistent size-resolved growth calculation, that is a model caveat rather than circularity, and the paper itself flags the unknown CPA size distribution as a limitation. The acknowledged monomer-size discrepancy with classical nucleation theory is likewise a stated limitation, not a circular step. No equation in the paper reduces by construction to its own input, and no fitted parameter is renamed as a prediction.

Assumptions & free parameters 5 free parameters · 8 assumptions · 0 invented entities

The model rests on microphysical laws from the authors' own prior and collaborative work, plus standard atmospheric assumptions. No new physical entities are introduced. The free parameters (monomer size, metallicity, reference radius) are all fitted to the GJ1214b spectrum or varied as unknowns.

free parameters (5)

monomer radius r_mon = 0.01-1 um; best fit near 0.3 um for GJ1214b
Sets monomer mass, aggregate settling, and scattering; nucleation theory does not determine it, so it is treated as a free parameter (Sections 3.1.1, 4.4).
atmospheric metallicity = 1-1000x solar; best fit >=100x solar
Controls KCl vapor abundance and scale height; varied as a free parameter in the fits to the GJ1214b transmission spectrum (Section 4.4).
reference transit radius R0 = 2-3 Earth radii
Adjusted to match the observed planet-to-star radius ratio when computing spectra (Section 4.4).
critical rolling displacement xi_crit = 2 A (0.2 nm)
Uncertain by a factor of about 10 (Heim et al. 1999); authors choose 2 A to maximize compression (Section 2.1.2).
settling velocity width factor epsilon = 0.5
Numerical factor accounting for the finite width of the size distribution in collision velocity and coalescence (Eqs. 10, 28; Sato et al. 2016).

assumptions (8)

domain assumption Aggregate growth occurs by equal-mass collisions giving fractal dimension Df=2 and prefactor k0=1
Section 2.1.1, Eq. (5); based on Okuzumi et al. 2009; load-bearing for the low settling velocity and high cloud altitude.
domain assumption Compression laws of Wada et al. (2008) and Kataoka et al. (2013b) apply to KCl aggregates in exoplanetary atmospheres
Eqs. (8) and (13); from N-body simulations of dust aggregates; validity for other conditions or different fractal dimensions is not fully established (Section 5.1.2).
domain assumption Aerodynamic radius of an aggregate equals its characteristic radius
Used in Eq. (11) and justified in footnote 1 for Df>=2; for very fluffy Df<2 aggregates, settling is overestimated.
domain assumption All condensable KCl vapor is instantly incorporated into monomers at the cloud base
Section 3.1.1; nucleation and condensation are not modeled, and monomer size becomes a free parameter. The authors argue the condensation timescale is short, but homogeneous nucleation could occur higher up.
domain assumption Cloud particle size distribution is narrowly peaked (double-moment closure)
Section 3.1; cannot capture broadening or the loss of the largest particles. The authors argue the effect is minor for slowly settling aggregates (Section 3.2).
domain assumption Vertical transport is 1D eddy diffusion with Kz from Charnay et al. (2015a)
Eqs. (23)-(25); Kz is uncertain by an order of magnitude and directly controls the vertical extent of the clouds.
domain assumption MMF theory accurately computes aggregate opacities
Section 4.1.2; validated against T-matrix by Tazaki & Tanaka (2018) and used for all aggregate spectra.
standard math Gas opacities from TEA thermochemical equilibrium and HITRAN2016 line lists
Section 4.1.1; standard methods, but the assumed cloud-free PT profile and abundances feed the synthetic spectra.

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Pith. "Pith review of Clouds of Fluffy Aggregates: How They Form in Exoplanetary Atmospheres and Influence Transmission Spectra." pith.science (2026). https://pith.science/paper/Y6QLVUNA

@misc{pith2026190802201,
  author       = {Pith},
  title        = {Pith review of: Clouds of Fluffy Aggregates: How They Form in Exoplanetary Atmospheres and Influence Transmission Spectra},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y6QLVUNA}},
  note         = {Machine review of arXiv:1908.02201}
}

abstract

Transmission spectrum surveys have suggested the ubiquity of high-altitude clouds in exoplanetary atmospheres. Theoretical studies have investigated the formation processes of the high-altitude clouds; however, cloud particles have been commonly approximated as compact spheres, which is not always true for solid mineral particles that likely constitute exoplanetary clouds. Here, we investigate how the porosity of cloud particles evolve in exoplanetary atmospheres and influence the cloud vertical profiles. We first construct a porosity evolution model that takes into account the fractal aggregation and the compression of cloud particle aggregates. Using a cloud microphysical model coupled with the porosity model, we demonstrate that the particle internal density can significantly decrease during the cloud formation. As a result, fluffy-aggregate clouds ascend to altitude much higher than that for compact-sphere clouds assumed so far. We also examine how the fluffy-aggregate clouds affect transmission spectra. We find that the clouds largely obscure the molecular features and produce a spectral slope originated by the scattering properties of aggregates. Finally, we compare the synthetic spectra with the observations of GJ1214 b and find that its flat spectrum could be explained if the atmospheric metallicity is sufficiently high ($\ge100\times$ solar) and the monomer size is sufficiently small ($r_{\rm mon}<1~{\rm {\mu}m}$). The high-metallicity atmosphere may offer the clues to explore the gas accretion processes onto past GJ1214b.

Figures

Figures reproduced from arXiv: 1908.02201 by the authors.

**Figure 1.** Equilibrium filling factor of KCl particle aggregates at the base of the KCl cloud in the super-Earth GJ1214b. The left, center, and right panels are for monomer radii rmon = 0.01, 0.1, and 1 µm, respectively. The orange, blue, green, and black lines show the filling factors determined by fractal growth (φfrac; Equation 5), gas-drag compression (φdrag; Equation 15), collisional compression (φcoll; Equation 9), and a… view at source ↗

**Figure 2.** Cartoon illustrating the formation of fluffy-aggregate clouds. which we approximate as a diffusion process in the horizontal averaged sense (Parmentier et al. 2013; Charnay et al. 2015a; Zhang & Showman 2018a,b). The upward transport is limited by the downward settling motion of the particles. We treat these processes using 1D vertical transport equations with a collisional growth term (Ohno & Okuzumi 2018), ∂nc ∂t… view at source ↗

**Figure 3.** Vertical structure of a KCl cloud in GJ1214b from compact and fluffy aggregate models. The left, center, and right columns show the radius ragg, mass mixing ratio ρc/ρg, volume filling factor of CPAs, respectively. The top, middle, and bottom rows are for atmospheric metallicities of 1×, 10×, 100×, and 1000× solar, respectively. The vertical axes are atmospheric pressure for all panels. The light-green, green, and d… view at source ↗

Figures from the paper (9 more)

**Figure 4.** Figure 4: Extinction opacity of KCl aggregates with Df = 2 as a function of wavelength for different aggregate sizes ragg and monomer sizes rmon, calculated by the MMF theory. The left panel is for aggregates of fixed rmon = 0.1 µm and different ragg, wheres the right panel is f…

**Figure 5.** Figure 5: Cloud-top pressure of the fluffy-aggregate clouds as a function of wavelength. The dark-green, green, and light-green lines are for rmon = 1, 0.1, and 0.01 µm, respectively. The dashed lines indicate the pressure level of τmix = τfall for each monomer size. Each panel …

**Figure 6.** Figure 6: Synthetic transmission spectra of GJ1214b with a solar-metalicity atmosphere, from compact-sphere and fluffy-aggregate models (left and right panels, respectively) presented in Section 3. The purple, red, and orange lines are from the models assuming the monomer radii …

**Figure 7.** Figure 7: Synthetic transmission spectra of GJ1214b with a cloud of fluffy KCl aggregates for various atmospheric metallicities. tral slope is proportional to the pressure scale height H (see Equation 41), which decreases with increasing the atmospheric metallicity. The effect …

**Figure 8.** Figure 8: Synthetic transmission spectra of GJ1214 b (colored lines) compared with the observational spectrum to date (black and gray points). The left panel shows all observed transit depth ranging from 0.3 to 5 µm and the best-fit spectra for comparisons with data of HST/WFC3 …

**Figure 9.** Figure 9: Reduced chi-squared values for the synthetic transmission spectra of GJ1214b as a function of the monomer radius and atmospheric metallicity. The left panel shows the chi-squred values obtained by fitting models to all observational data. The middle panel shows the res…

**Figure 10.** Figure 10: Normalized mass-weighted collision rate between particles with masses mt and m. The black, gray, and silver lines show the collision rate for the Hansen size distributions with b = 0.1, 0.5, and 1.0, respectively. The corresponding size distributions normalized by a…

**Figure 11.** Figure 11: Comparison of our porosity model with that used in Adams et al. (2019). The vertical and horizontal axes show the fractal dimension Df and number of monomers Nmon. Different colored lines exhibit the evolution track of Df for different monomer size, and the gray line…

**Figure 12.** Figure 12: Extinction mass opacity of aggregates with Df = 2 for a variety of condensable materials. of hazes will be helpful to constrain the atmospheric metallicity of GJ1214b. The compact-sphere cloud is still not ruled out (Gao & Benneke 2018). Kz for settling aerosols is s…

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Works this paper leans on

133 extracted references · 61 canonical work pages · cited by 1 Pith paper

[1]

Adams, D., Gao, P., de Pater, I., & Morley, C. V . 2019, ApJ, 874, 61

2019
[2]

2016, ApJL, 832, L19

Arakawa, S., & Nakamoto, T. 2016, ApJL, 832, L19

2016
[3]

Geometrical structure and thermal conductivity of dust aggregates formed via ballistic cluster-cluster aggregation

Arakawa, S., Takemoto, M., & Nakamoto, T. 2019, arXiv e-prints, arXiv:1908.03125

work page Pith review arXiv 2019
[4]

J., & Scott, P

Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481

2009
[5]

K., Aigrain, S., Irwin, P

Barstow, J. K., Aigrain, S., Irwin, P. G. J., & Sing, D. K. 2017, ApJ, 834, 50

2017
[6]

E., Mandell, A., Pontoppidan, K., et al

Batalha, N. E., Mandell, A., Pontoppidan, K., et al. 2017, PASP, 129, 064501 Clouds of Fluffy Aggregates 21

2017
[7]

Batygin, K., & Stevenson, D. J. 2010, ApJL, 714, L238

2010
[8]

L., Miller-Ricci Kempton, E., & Homeier, D

Bean, J. L., Miller-Ricci Kempton, E., & Homeier, D. 2010, Nature, 468, 669

2010

Show all 133 references

[9]

L., D´esert, J.-M., Kabath, P., et al

Bean, J. L., D´esert, J.-M., Kabath, P., et al. 2011, ApJ, 743, 92

2011
[10]

A., Lothringer, J., et al

Benneke, B., Knutson, H. A., Lothringer, J., et al. 2019, Nature Astronomy, 361

2019
[11]

V ., & Percival, I

Berry, M. V ., & Percival, I. C. 1986, Optica Acta, 33, 577

1986
[12]

K., Charbonneau, D., D´esert, J.-M., et al

Berta, Z. K., Charbonneau, D., D´esert, J.-M., et al. 2012, ApJ, 747, 35

2012
[13]

Blecic, J., Harrington, J., & Bowman, M. O. 2016, ApJS, 225, 4

2016
[14]

2000, Icarus, 143, 138

Blum, J., & Wurm, G. 2000, Icarus, 143, 138

2000
[15]

1996, Icarus, 124, 441

Blum, J., Wurm, G., Kempf, S., & Henning, T. 1996, Icarus, 124, 441

1996
[16]

F., & Huﬀman, D

Bohren, C. F., & Huﬀman, D. R. 1983, Absorption and scattering of light by small particles

1983
[17]

Brown, T. M. 2001, ApJ, 553, 1006

2001
[18]

A., Winn, J

Carter, J. A., Winn, J. N., Holman, M. J., et al. 2011, ApJ, 730, 82

2011
[19]

K., Irwin, J., et al

Charbonneau, D., Berta, Z. K., Irwin, J., et al. 2009, Nature, 462, 891

2009
[20]

2015b, ApJL, 813, L1 Col´on, K

Charnay, B., Meadows, V ., Misra, A., Leconte, J., & Arney, G. 2015b, ApJL, 813, L1 Col´on, K. D., & Gaidos, E. 2013, ApJ, 776, 49

2013
[21]

2011, ApJ, 736, 78 Crossﬁeld, I

Croll, B., Albert, L., Jayawardhana, R., et al. 2011, ApJ, 736, 78 Crossﬁeld, I. J. M., & Kreidberg, L. 2017, AJ, 154, 261 de Mooij, E. J. W., Brogi, M., de Kok, R. J., et al. 2012, A&A, 538, A46 D´esert, J.-M., Bean, J., Miller-Ricci Kempton, E., et al. 2011, ApJ, 731, L40

2011
[22]

Dominik, C., & Tielens, A. G. G. M. 1995, Philosophical

1995
[23]

1997, ApJ, 480, 647

Magazine, Part A, 72, 783 —. 1997, ApJ, 480, 647

1997
[24]

V ., Jord´an, A., et al

Espinoza, N., Rackham, B. V ., Jord´an, A., et al. 2019, MNRAS, 482, 2065

2019
[25]

M., Sing, D

Evans, T. M., Sing, D. K., Goyal, J. M., et al. 2018, AJ, 156, 283

2018
[26]

Ford, I. J. 1997, PhRvE, 56, 5615

1997
[27]

J., Mordasini, C., Nettelmann, N., et al

Fortney, J. J., Mordasini, C., Nettelmann, N., et al. 2013, ApJ, 775, 80

2013
[28]

J., Sudarsky, D., Hubeny, I., et al

Fortney, J. J., Sudarsky, D., Hubeny, I., et al. 2003, ApJ, 589, 615

2003
[29]

2014, Nature, 513, 526

Fraine, J., Deming, D., Benneke, B., et al. 2014, Nature, 513, 526

2014
[30]

D., Deming, D., Gillon, M., et al

Fraine, J. D., Deming, D., Gillon, M., et al. 2013, ApJ, 765, 127

2013
[31]

S., Lustig-Yaeger, J., Fortney, J

Freedman, R. S., Lustig-Yaeger, J., Fortney, J. J., et al. 2014, ApJS, 214, 25

2014
[32]

S., Marley, M

Freedman, R. S., Marley, M. S., & Lodders, K. 2008, ApJS, 174, 504

2008
[33]

R., Roller, C., Lopes, E., et al

Gamache, R. R., Roller, C., Lopes, E., et al. 2017, Journal of Quantitative Spectroscopy and Radiative Transfer, 203, 70

2017
[34]

2018, ApJ, 863, 165

Gao, P., & Benneke, B. 2018, ApJ, 863, 165

2018
[35]

S., & Ackerman, A

Gao, P., Marley, M. S., & Ackerman, A. S. 2018, ApJ, 855, 86

2018
[36]

Gharib-Nezhad, E., & Line, M. R. 2019, ApJ, 872, 27

2019
[37]

2014, A&A, 563, A21

Gillon, M., Demory, B.-O., Madhusudhan, N., et al. 2014, A&A, 563, A21

2014
[38]

2010, A&A, 520, A27

Guillot, T. 2010, A&A, 520, A27

2010
[39]

Guillot, T., Ida, S., & Ormel, C. W. 2014, A&A, 572, A72

2014
[40]

Hansen, J. E. 1971, Journal of Atmospheric Sciences, 28, 1400

1971
[41]

M., Lewis, N

He, C., H¨orst, S. M., Lewis, N. K., et al. 2018a, arXiv e-prints, arXiv:1812.06957 —. 2018b, AJ, 156, 38

arXiv
[42]

1999, Physical Review Letters, 83, 3328

Heim, L.-O., Blum, J., Preuss, M., & Butt, H.-J. 1999, Physical Review Letters, 83, 3328

1999
[43]

2013, Philosophical Transactions of the Royal Society of London Series A, 371, 20110581

Helling, C., & Fomins, A. 2013, Philosophical Transactions of the Royal Society of London Series A, 371, 20110581

2013
[44]

2017, A&A, 603, A123

Helling, C., Tootill, D., Woitke, P., & Lee, G. 2017, A&A, 603, A123

2017
[45]

2008, A&A, 485, 547

Helling, C., Woitke, P., & Thi, W.-F. 2008, A&A, 485, 547

2008
[46]

2019, arXiv e-prints, arXiv:1906.08127

Helling, C., Iro, N., Corrales, L., et al. 2019, arXiv e-prints, arXiv:1906.08127

2019 arXiv
[47]

2017, MNRAS, 470, 2972

Heng, K., & Kitzmann, D. 2017, MNRAS, 470, 2972

2017
[48]

2011, MNRAS, 416, 1419 H¨orst, S

Hori, Y ., & Ikoma, M. 2011, MNRAS, 416, 1419 H¨orst, S. M., He, C., Lewis, N. K., et al. 2018, Nature Astronomy, 2, 303

2011
[49]

Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90

2007
[50]

2012, ApJ, 753, 66

Ikoma, M., & Hori, Y . 2012, ApJ, 753, 66

2012
[51]

Jacobson, M. Z. 2005, Fundamentals of Atmospheric Modeling, 828

2005
[52]

2014, A&A, 568, A42

Kataoka, A., Okuzumi, S., Tanaka, H., & Nomura, H. 2014, A&A, 568, A42

2014
[53]

P., Lewis, N

Kataria, T., Showman, A. P., Lewis, N. K., et al. 2013, ApJ, 767, 76

2013
[54]

2019, ApJ, 876, L5

Kawashima, Y ., Hu, R., & Ikoma, M. 2019, ApJ, 876, L5

2019
[55]

2018, ApJ, 853, 7 —

Kawashima, Y ., & Ikoma, M. 2018, ApJ, 853, 7 —. 2019, ApJ, 877, 109

2018
[56]

2018, MNRAS, 475, 94

Kitzmann, D., & Heng, K. 2018, MNRAS, 475, 94

2018
[57]

D., Showman, A

Komacek, T. D., Showman, A. P., & Parmentier, V . 2019, arXiv e-prints, arXiv:1904.09676

2019 arXiv
[58]

Stevenson, K. B. 2018, ApJL, 858, L6 22 O hno, Okuzumi, & Tazaki

2018
[59]

L., D´esert, J.-M., et al

Kreidberg, L., Bean, J. L., D´esert, J.-M., et al. 2014, Nature, 505, 69

2014
[60]

R., Bean, J

Kreidberg, L., Line, M. R., Bean, J. L., et al. 2015, ApJ, 814, 66

2015
[61]

1997, JQSRT, 57, 819

Kuntz, M. 1997, JQSRT, 57, 819

1997
[62]

2018, MNRAS, 479, 635

Kurokawa, H., & Tanigawa, T. 2018, MNRAS, 479, 635

2018
[63]

2019, A&A, 623, A179

Kuwahara, A., Kurokawa, H., & Ida, S. 2019, A&A, 623, A179

2019
[64]

2014, A&A, 572, A35

Lambrechts, M., Johansen, A., & Morbidelli, A. 2014, A&A, 572, A35

2014
[65]

2017, A&A, 606, A146

Lambrechts, M., & Lega, E. 2017, A&A, 606, A146

2017
[66]

Showman, A. P. 2019, arXiv e-prints, arXiv:1905.02976

2019 arXiv
[67]

2011, ApJ, 728, 80

Lavvas, P., Sander, M., Kraft, M., & Imanaka, H. 2011, ApJ, 728, 80

2011
[68]

V ., & Griﬃth, C

Lavvas, P., Yelle, R. V ., & Griﬃth, C. A. 2010, Icarus, 210, 832

2010
[69]

J., & Chiang, E

Lee, E. J., & Chiang, E. 2016, ApJ, 817, 90

2016
[70]

J., Chiang, E., & Ormel, C

Lee, E. J., Chiang, E., & Ormel, C. W. 2014, ApJ, 797, 95

2014
[71]

2016, A&A, 594, A48

Lee, G., Dobbs-Dixon, I., Helling, C., Bognar, K., & Woitke, P. 2016, A&A, 594, A48

2016
[72]

Lee, G., Helling, C., Giles, H., & Bromley, S. T. 2015, A&A, 575, A11

2015
[73]

Lee, G. K. H., Blecic, J., & Helling, C. 2018, A&A, 614, A126

2018
[74]

K., Parmentier, V ., Kataria, T., et al

Lewis, N. K., Parmentier, V ., Kataria, T., et al. 2017, arXiv e-prints, arXiv:1706.00466

2017 arXiv
[75]

R., & Parmentier, V

Line, M. R., & Parmentier, V . 2016, ApJ, 820, 78

2016
[76]

J., Manners, J., et al

Lines, S., Mayne, N. J., Manners, J., et al. 2019, MNRAS, 488, 1332

2019
[77]

J., Boutle, I

Lines, S., Mayne, N. J., Boutle, I. A., et al. 2018, A&A, 615, A97

2018
[78]

D., Benneke, B., Crossﬁeld, I

Lothringer, J. D., Benneke, B., Crossﬁeld, I. J. M., et al. 2018, AJ, 155, 66

2018
[79]

J., & Madhusudhan, N

MacDonald, R. J., & Madhusudhan, N. 2019, MNRAS, 486, 1292

2019
[80]

M., et al

Malik, M., Kitzmann, D., Mendonc ¸a, J. M., et al. 2019, AJ, 157, 170

2019
[81]

S., Ackerman, A

Marley, M. S., Ackerman, A. S., Cuzzi, J. N., & Kitzmann, D. 2013, Clouds and Hazes in Exoplanet Atmospheres, ed. S. J

2013
[82]

Mbarek, R., & Kempton, E. M. R. 2016, ApJ, 827, 121

2016
[83]

1991, Reviews of Geophysics, 29, 317

Meakin, P. 1991, Reviews of Geophysics, 29, 317

1991
[84]

2006, A&A, 452, 701

Minato, T., K¨ohler, M., Kimura, H., Mann, I., & Yamamoto, T. 2006, A&A, 452, 701

2006
[85]

V ., Fortney, J

Morley, C. V ., Fortney, J. J., Kempton, E. M.-R., et al. 2013, ApJ, 775, 33

2013
[86]

V ., Fortney, J

Morley, C. V ., Fortney, J. J., Marley, M. S., et al. 2012, ApJ, 756, 172 —. 2015, ApJ, 815, 110

2012
[87]

V ., Knutson, H., Line, M., et al

Morley, C. V ., Knutson, H., Line, M., et al. 2017, AJ, 153, 86

2017
[88]

I., Line, M

Moses, J. I., Line, M. R., Visscher, C., et al. 2013, ApJ, 777, 34

2013
[89]

2012, A&A, 544, A41

Murgas, F., Pall´e, E., Cabrera-Lavers, A., et al. 2012, A&A, 544, A41

2012
[90]

2018, ApJ, 867, 127

Ogihara, M., & Hori, Y . 2018, ApJ, 867, 127

2018
[91]

2017, ApJ, 835, 261 —

Ohno, K., & Okuzumi, S. 2017, ApJ, 835, 261 —. 2018, ApJ, 859, 34

2017
[92]

2019, ApJ, 874, 1

Ohno, K., & Zhang, X. 2019, ApJ, 874, 1

2019
[93]

2009, ApJ, 698, 1122

Okuzumi, S. 2009, ApJ, 698, 1122

2009
[94]

2009, ApJ, 707, 1247

Okuzumi, S., Tanaka, H., & Sakagami, M.-a. 2009, ApJ, 707, 1247

2009
[95]

2003, Journal of Aerosol Science, 34, 383

Onischuk, A., di Stasio, S., Karasev, V ., et al. 2003, Journal of Aerosol Science, 34, 383

2003
[96]

W., & Min, M

Ormel, C. W., & Min, M. 2019, A&A, 622, A121

2019
[97]

W., Shi, J.-M., & Kuiper, R

Ormel, C. W., Shi, J.-M., & Kuiper, R. 2015, MNRAS, 447, 3512

2015
[98]

Palik, E. D. 1985, Handbook of optical constants of solids

1985
[99]

P., & Lian, Y

Parmentier, V ., Showman, A. P., & Lian, Y . 2013, A&A, 558, A91

2013
[100]

2006, Icarus, 182, 274 —

Paszun, D., & Dominik, C. 2006, Icarus, 182, 274 —. 2009, A&A, 507, 1023

2006
[101]

2017, MNRAS, 471, 4355

Pinhas, A., & Madhusudhan, N. 2017, MNRAS, 471, 4355

2017
[102]

2019, MNRAS, 482, 1485

Pinhas, A., Madhusudhan, N., Gandhi, S., & MacDonald, R. 2019, MNRAS, 482, 1485

2019
[103]

2018, ApJ, 860, 18

Powell, D., Zhang, X., Gao, P., & Parmentier, V . 2018, ApJ, 860, 18

2018
[104]

2017, ApJ, 834, 151

Rackham, B., Espinoza, N., Apai, D., et al. 2017, ApJ, 834, 151

2017
[105]

Rodrigues, P. C. R., & Silva Fernandes, F. M. S. 2007, The Journal of Chemical Physics, 126, 024503

2007
[106]

2019, ApJ, 872, 1

Roman, M., & Rauscher, E. 2019, ApJ, 872, 1

2019
[107]

Rossow, W. B. 1978, Icarus, 36, 1

1978
[108]

S., Rinsland, C

Rothman, L. S., Rinsland, C. P., Goldman, A., et al. 1998, JQSRT, 60, 665

1998
[109]

2004, JQSRT, 86, 231

Ruyten, W. 2004, JQSRT, 86, 231

2004
[110]

2019, Icarus, 317, 48

Sakuraba, H., Kurokawa, H., & Genda, H. 2019, Icarus, 317, 48

2019
[111]

2016, A&A, 589, A15

Sato, T., Okuzumi, S., & Ida, S. 2016, A&A, 589, A15

2016
[112]

Seager, S., & Sasselov, D. D. 2000, ApJ, 537, 916

2000
[113]

2012, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (Wiley)

Seinfeld, J., & Pandis, S. 2012, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (Wiley)

2012
[114]

M., & Burrows, A

Sharp, C. M., & Burrows, A. 2007, ApJS, 168, 140

2007
[115]

K., Fortney, J

Sing, D. K., Fortney, J. J., Nikolov, N., et al. 2016, Nature, 529, 59

2016
[116]

2008, ApJ, 684, 1310

Suyama, T., Wada, K., & Tanaka, H. 2008, ApJ, 684, 1310

2008
[117]

2012, ApJ, 753, 115

Suyama, T., Wada, K., Tanaka, H., & Okuzumi, S. 2012, ApJ, 753, 115

2012
[118]

K., Tanaka, H., Yamamoto, T., & Kawamura, K

Tanaka, K. K., Tanaka, H., Yamamoto, T., & Kawamura, K. 2011, JChPh, 134, 204313

2011
[119]

2007, ApJ, 667, 557

Tanigawa, T., & Ikoma, M. 2007, ApJ, 667, 557

2007
[120]

2016, ApJ, 823, 48

Tanigawa, T., & Tanaka, H. 2016, ApJ, 823, 48

2016
[121]

2018, ApJ, 860, 79

Tazaki, R., & Tanaka, H. 2018, ApJ, 860, 79

2018
[122]

2016, ApJ, 823, 70

Tazaki, R., Tanaka, H., Okuzumi, S., Kataoka, A., & Nomura, H. 2016, ApJ, 823, 70

2016
[123]

2018, Atoms, 6, 26

Tennyson, J., & Yurchenko, S. 2018, Atoms, 6, 26

2018
[124]

2016, in Proc

Tinetti, G., Drossart, P., Eccleston, P., et al. 2016, in Proc. SPIE, V ol. 9904, Space Telescopes and Instrumentation 2016: Optical, Infrared, and Millimeter Wave, 99041X

2016
[125]

2016, A&A, 596, A90 Clouds of Fluffy Aggregates 23

Venturini, J., Alibert, Y ., & Benz, W. 2016, A&A, 596, A90 Clouds of Fluffy Aggregates 23

2016
[126]

2015, A&A, 576, A114

Venturini, J., Alibert, Y ., Benz, W., & Ikoma, M. 2015, A&A, 576, A114

2015
[127]

2017, ApJ, 848, 95

Venturini, J., & Helled, R. 2017, ApJ, 848, 95

2017
[128]

2007, ApJ, 661, 320 —

Wada, K., Tanaka, H., Suyama, T., Kimura, H., & Yamamoto, T. 2007, ApJ, 661, 320 —. 2008, ApJ, 677, 1296

2007
[129]

R., Sing, D

Wakeford, H. R., Sing, D. K., Kataria, T., et al. 2017, Science, 356, 628

2017
[130]

Westwood, A. R. C., & Hitch, T. T. 1963, Journal of Applied Physics, 34, 3085

1963
[131]

2003, A&A, 399, 297

Woitke, P., & Helling, C. 2003, A&A, 399, 297

2003
[132]

T., & Toon, O

Wolf, E. T., & Toon, O. B. 2010, Science, 328, 1266

2010
[133]

M., He, C., McGuiggan, P., & Bridges, N

Yu, X., H¨orst, S. M., He, C., McGuiggan, P., & Bridges, N. T. 2017, Journal of Geophysical Research (Planets), 122, 2610

2017

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

Adams, D., Gao, P., de Pater, I., & Morley, C. V . 2019, ApJ, 874, 61

2019

[2] [2]

2016, ApJL, 832, L19

Arakawa, S., & Nakamoto, T. 2016, ApJL, 832, L19

2016

[3] [3]

Geometrical structure and thermal conductivity of dust aggregates formed via ballistic cluster-cluster aggregation

Arakawa, S., Takemoto, M., & Nakamoto, T. 2019, arXiv e-prints, arXiv:1908.03125

work page Pith review arXiv 2019

[4] [4]

J., & Scott, P

Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481

2009

[5] [5]

K., Aigrain, S., Irwin, P

Barstow, J. K., Aigrain, S., Irwin, P. G. J., & Sing, D. K. 2017, ApJ, 834, 50

2017

[6] [6]

E., Mandell, A., Pontoppidan, K., et al

Batalha, N. E., Mandell, A., Pontoppidan, K., et al. 2017, PASP, 129, 064501 Clouds of Fluffy Aggregates 21

2017

[7] [7]

Batygin, K., & Stevenson, D. J. 2010, ApJL, 714, L238

2010

[8] [8]

L., Miller-Ricci Kempton, E., & Homeier, D

Bean, J. L., Miller-Ricci Kempton, E., & Homeier, D. 2010, Nature, 468, 669

2010