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Water ice in the debris disk around HD 181327

Nature volume 641, pages 608–611 (2025)Cite this article

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Abstract

Debris disks are exoplanetary systems that contain planets, minor bodies (asteroids, Kuiper belt objects, comets and so on) and micrometre-sized debris dust1. Because water ice is the most common frozen volatile, it plays an essential role in the formation of planets2,3 and minor bodies. Although water ice has been commonly found in Kuiper belt objects and comets in the Solar System4, no definitive evidence for water ice in debris disks has been obtained to date1. Here we report the discovery of water ice in the HD 181327 debris disk using the near-infrared spectrograph onboard the James Webb Space Telescope. We detected the solid-state broad absorption feature of water ice at 3 µm including a distinct Fresnel peak at 3.1 µm, which is indicative of large, crystalline water-ice particles. Gradients in the water-ice feature as a function of stellocentric distance reveal a dynamic environment in which water ice is destroyed and replenished. We estimated the water-ice mass fractions as ranging from 0.1% at approximately 85 au to 21% at approximately 113 au, indicating the presence of a water-ice reservoir in the HD 181327 disk beyond the snow line. The icy bodies that release water ice in HD 181327 are probably the extra-solar counterparts of water-ice-rich Kuiper belt objects in our Solar System.

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Fig. 1: Disk reflectance spectra at different stellocentric distances.
Fig. 2: Photodesorption timescales of water ice as a function of stellocentric distance.
Fig. 3: Spectral comparison between the HD 181327 disk and icy bodies in the Solar System.

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Data availability

The original data used in this work are from general observer programme 1563 (principal investigator: C.C.) and are publicly available from the Mikulski Archive for Space Telescopes at the STScI (https://mast.stsci.edu). The specific observations analysed can be accessed from https://doi.org/10.17909/mr4p-v151. The reduced data cube and extracted disk spectra used in the analysis are publicly available at Zenodo (https://doi.org/10.5281/zenodo.14985028)97.

Code availability

The postprocessing and dust reflectance modelling codes used in this work were developed by C.X., as detailed in Methods. The postprocessing used the jwstIFURDI package, which can be found at https://github.com/ChenXie-astro/jwstIFURDI.git. The dust reflectance modelling process used the DDRM package, which can be found at https://github.com/ChenXie-astro/DDRM.git. The method for solving the complex numbers in the Bruggeman rule used the mpmath package (https://mpmath.org/). The Mie scattering calculation used the miepython package, which can be found at https://github.com/scottprahl/miepython.git. The two-dimensional disk model uses the publicly available anadisk_model package, which can be found at https://github.com/maxwellmb/anadisk_model.git. The fitting procedure for the disk model uses the publicly available Markov chain Monte Carlo Ensemble sampler, the emcee package, which can be found at https://github.com/dfm/emcee.git, and the disk forward modelling code, the DebrisDiskFM package (https://github.com/seawander/DebrisDiskFM.git). In addition to emcee, Scipy (https://scipy.org/) was also used in the fitting procedure for the dust reflectance spectrum. Figures were made with Matplotlib v.3.3.0. under the Matplotlib licence at https://matplotlib.org/.

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Acknowledgements

This work is based on observations made with the NASA/ESA/CSA JWST. The data were obtained from the Mikulski Archive for Space Telescopes at the Space Telescope Science Institute (STScI), which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127 for JWST. These observations are associated with programme 01563. Support for this programme was provided by NASA through a grant from the STScI. B.T.B. is supported by an appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center, administered by Oak Ridge Associated Universities under contract with NASA. S.K.B. is supported in part by an STScI Postdoctoral Fellowship. C.M.L. would like to acknowledge support for this work from the Johns Hopkins University Applied Physics Laboratory sabbatical programme and the NASA New Horizons mission project. N.P.-A. acknowledges funding through the ATRAE programme of the Ministry of Science, Innovation, and Universities and the State Agency for Research in Spain.

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Authors and Affiliations

  1. William H. Miller III Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA

    Chen Xie, Christine H. Chen, Carl Ingebretsen & Kadin Worthen

  2. Space Telescope Science Institute, Baltimore, MD, USA

    Christine H. Chen, Dean C. Hines, Tracy Beck, Sarah K. Betti, Laurent Pueyo, Marshall D. Perrin & John A. Stansberry

  3. Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA

    Carey M. Lisse

  4. Institute of Space Sciences and Technologies of Asturias, University of Oviedo, Oviedo, Spain

    Noemí Pinilla-Alonso

  5. Department of Physics, University of Central Florida, Orlando, FL, USA

    Noemí Pinilla-Alonso

  6. Steward Observatory and the Department of Astronomy, The University of Arizona Tucson, Tucson, AZ, USA

    András Gáspár, Schuyler G. Wolff & Jarron M. Leisenring

  7. Goddard Space Flight Center, Greenbelt, MD, USA

    Bryce T. Bolin

  8. Eureka Scientific, Oakland, CA, USA

    Bryce T. Bolin

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C.X. led the programme and performed the data reduction, PSF subtraction, spectrum modelling and data analysis. C.X. wrote the manuscript. C.C. co-led the programme and assisted with writing and spectrum modelling. T.B., C.I. and K.W. assisted with the preprocessing of the data. S.K.B., M.D.P., L.P. and T.B. assisted with the postprocessing of the data. T.B. provided the cleaned empirical PSF model. S.G.W. coordinated the comparison of results with NIRCam observations. C.C., C.M.L., D.C.H., N.P.-A., A.G., B.T.B., J.A.S. and S.G.W. contributed to the interpretation of the results. N.P.-A. provided the spectra from the DiSCo program. C.C., T.B., A.G., J.M.L., C.M.L., M.D.P., L.P., J.A.S. and S.G.W. contributed to obtaining the JWST data. All authors participated in the discussion of the results or commented on the manuscript.

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Correspondence to Chen Xie.

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Extended data figures and tables

Extended Data Fig. 1 Scaling factor in RDI as a function of wavelength.

The scaling factor used in RDI is stable between 1.5–5 µm. The 2% drop below 1.5 µm is probably caused by the mismatch in spectral types of HD 181327 (F67) and Iota Mic (F298). Vertical dashed lines mark the Fresnel peak of water ice at 3.1 µm and the CO2-ice feature at 4.25 µm.

Extended Data Fig. 2 Throughput correction factors of RDI PSF subtraction.

The RDI throughputs of different disk-extracting regions as a function of wavelength. The flux loss caused by PSF subtraction is less than 10% and relatively stable between 1–5 µm. The drop in throughput at <1.1 µm is probably caused by the mismatch of spectral types between the science and reference stars. Vertical dashed lines mark the Fresnel peak of water ice at 3.1 µm and the CO2-ice feature at 4.25 µm.

Extended Data Fig. 3 Illustration of reflectance spectroscopy of the HD181327 disk.

a, Comparison between the measured disk spectrum after RDI and a scaled stellar photosphere model. The extracting region of the disk spectrum is located at a scattering angle of ~90° and the stellocentric distances of 90–105 au (region 2 in Fig. 1b). The solid-state feature of water ice at 3 µm leads to changes in the slope of the disk spectrum. The Fresnel peak of water ice is presented at 3.1 µm, indicated by the vertical grey line. b, The disk reflectance spectrum is overlaid with best-fit dust model spectra. The solid-state feature of water ice and the Fresnel peak are visible. The bowl-shaped dip at 3 µm can be fitted using the dust model with two dust populations (model #2 in Table 1), while showing deviations when using a single dust population (model #4 in Extended Data Table 1). The Fresnel peak is not fitted because we do not include large particles (i.e., ~1 mm) in the fit. c, The \({\chi }^{2}\) value per spectral channel shows the performance of model fitting in each spectral channel. The dust model with a single dust population shows significant residuals (blue curve) around 2.8 µm. Error bars represent 1 s.d.

Extended Data Fig. 4 Disk reflectance spectra at different stellocentric distances without applying the throughput corrections.

The disk reflectance spectra at 80–90 au, 90–105 au, and 105–120 au, similar to Fig. 1, but without the throughput correction. The water-ice gradient is visible and has a similar behaviour as in Fig. 1. The spectra are also bluer at larger distances, consistent with the trend found in Fig. 1. Error bars represent 1 s.d.

Extended Data Fig. 5 Disk reflectance spectra at two sides of the disk.

a-c, The disk reflectance spectra extracted from each side (east and west) of the disk at different stellocentric distances, showing no significant difference between the spectra at the two sides of the disk. The two-sides combined spectra are also shown in Fig. 1. Error bars represent 1 s.d.

Extended Data Fig. 6 Dust temperatures and sublimation timescales as a function of stellocentric distance.

a, The dust temperature for different grain radii \((a)\) as a function of stellocentric distance, as detailed in Methods. For micron-sized grains, the dust temperature is approximately 50 K at 80–120 au. b, The timescale for sublimating dirty water-ice grains of different grain sizes as a function of distance. The sublimation timescale is much larger than the stellar age of 18.5 Myr.

Extended Data Fig. 7 Photodesorption timescales as a function of stellocentric distance under the optically thin assumption.

The photodesorption timescale for destroying water ice from icy grains of different grain radii (\(a\)) as a function of distance, assuming optically thin (\(\tau \) = 0) in the radial direction of the disk.

Extended Data Fig. 8 Comparison of the water-ice feature.

To compare the water-ice feature, a continuum model (green curve) was created and subtracted from the measured disk reflectance spectrum (cyan points). The best-fit dust parameters (model #3) from Table 1 are used to create the continuum model as detailed in Methods. The continuum subtracted spectrum (purple points) is the water-ice-dominated disk spectrum shown in Fig. 1, showing the water-ice feature at 3 µm. The spectra of water-ice-rich icy bodies are also shown for comparison, adopted from refs. 17,36, with the eccentricity (\(e\)) and the perihelion distance (\(q\)) included in the label. Although the band depths of water ice are different potentially caused by different grain sizes and water-ice abundance, the spectra of icy bodies and the disk at locations with similar UV irradiation show a similarity in water ice. The location of the Fresnel peak at 3.1 µm is marked by the vertical dashed line. Error bars represent 1 s.d.

Extended Data Fig. 9 Corner plot of the dust parameters in model #1 at 80–90 au.

Posteriors of the dust parameters in the dust model as detailed in Methods and listed in Table 1. The vertical dashed lines denote the 16%, 50%, and 84% quantiles (68% uncertainties) of the distribution. The porosity is determined based on the volume fractions of the dust species (Methods).

Extended Data Fig. 10 Corner plot of the dust parameters in model #2 at 90–105 au.

Similar to Extended Data Fig. 9. Two dust populations were adopted as listed in Table 1. The last two parameters are the volume fractions of the second dust population (i.e., the H2O and olivine mixture).

Extended Data Fig. 11 Corner plot of the dust parameters in model #3 at 105–120 au.

Similar to Extended Data Fig. 10.

Extended Data Table 1 Best-fit dust grain parameters for the model using the single dust population
Full size table

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Xie, C., Chen, C.H., Lisse, C.M. et al. Water ice in the debris disk around HD 181327. Nature 641, 608–611 (2025). https://doi.org/10.1038/s41586-025-08920-4

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