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A dusty veil shading Betelgeuse during its Great Dimming

Nature volume 594, pages 365–368 (2021)Cite this article

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Abstract

Red supergiants are the most common final evolutionary stage of stars that have initial masses between 8 and 35 times that of the Sun1. During this stage, which lasts roughly 100,000 years1, red supergiants experience substantial mass loss. However, the mechanism for this mass loss is unknown2. Mass loss may affect the evolutionary path, collapse and future supernova light curve3 of a red supergiant, and its ultimate fate as either a neutron star or a black hole4. From November 2019 to March 2020, Betelgeuse—the second-closest red supergiant to Earth (roughly 220 parsecs, or 724 light years, away)5,6—experienced a historic dimming of its visible brightness. Usually having an apparent magnitude between 0.1 and 1.0, its visual brightness decreased to 1.614 ± 0.008 magnitudes around 7–13 February 20207—an event referred to as Betelgeuse’s Great Dimming. Here we report high-angular-resolution observations showing that the southern hemisphere of Betelgeuse was ten times darker than usual in the visible spectrum during its Great Dimming. Observations and modelling support a scenario in which a dust clump formed recently in the vicinity of the star, owing to a local temperature decrease in a cool patch that appeared on the photosphere. The directly imaged brightness variations of Betelgeuse evolved on a timescale of weeks. Our findings suggest that a component of mass loss from red supergiants8 is inhomogeneous, linked to a very contrasted and rapidly changing photosphere.

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Fig. 1: Light curve of Betelgeuse over the past six years.
Fig. 2: VLT/SPHERE–ZIMPOL observations of Betelgeuse after deconvolution in the Cnt_Hα filter.
Fig. 3: Best model images obtained in the Cnt_Hα filter.

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Data availability

Raw data were generated at the ESO under programs 0102.D-0240(A), 0102.D-0240(D), 104.20UZ and 104.20V6.004. Derived data that support the findings of this study are available at the Centre de Données Astronomiques de Strasbourg (CDS) via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/other/Nat (for the VLT/SPHERE–ZIMPOL images) and at the Optical Interferometry Database (OiDB; for the VLTI/GRAVITY and VLT/SPHERE–IRDIS SAM observations). Source data are provided with this paper.

Code availability

The SPHERE and GRAVITY pipelines are available on the ESO website (http://www.eso.org/sci/software/pipelines/index.html). The PyRAF implementation of the Richardson–Lucy deconvolution algorithm is publicly available at https://astroconda.readthedocs.io/en/latest/. The RADMC3D code is publicly available at https://github.com/dullemond/radmc3d-2.0.

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Acknowledgements

This research used variable star observations from the AAVSO International Database contributed by observers worldwide. This project received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement number 665501 with the research Foundation Flanders (FWO; [PEGASUS]2 Marie Curie fellowship 12U2717N awarded to M.M.). E.C. acknowledges funding from KU Leuven C1 grant MAESTRO C16/17/007. L.D. and M.M. acknowledge support from ERC consolidator grant 646758 AEROSOL. J.S.B. acknowledges the support received from the UNAM PAPIIT project IA 101220. S.K. acknowledges support from ERC starting grant 639889 ImagePlanetFormDiscs. The material is based on work supported by NASA under award number 80GSFC17M0002. We thank the ESO staff for their fast response in accepting the DDT proposal and carrying out the observations. We are grateful that Betelgeuse underwent this peculiar event more than 700 years ago in the appropriate solid angle. This work made use of the SPHERE data center, jointly operated by OSUG/IPAG (Grenoble), PYTHEAS/LAM/CeSAM (Marseille), OCA/Lagrange (Nice) and Observatoire de Paris/LESIA (Paris). This research made use of the Jean-Marie Mariotti Center Aspro and SearchCal services (http://www.jmmc.fr). We used the SIMBAD and VIZIER databases at CDS, Strasbourg (France; http://cdsweb.u-strasbg.fr/), and NASA’s Astrophysics Data System Bibliographic Services. This research made use of GNU Parallel69, IPython70, Numpy71, Matplotlib72, SciPy73, Pandas74 (https://github.com/pandas-dev/pandas), Astropy75 (http://www.astropy.org/) and Uncertainties (http://pythonhosted.org/uncertainties/).

Author information

Authors and Affiliations

  1. LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université de Paris, Meudon, France

    M. Montargès, P. Kervella & G. Perrin

  2. Institute of Astronomy, KU Leuven, Leuven, Belgium

    M. Montargès, E. Cannon, A. de Koter & L. Decin

  3. Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, France

    E. Lagadec, A. Chiavassa, F. Millour, B. Lopez & P. Stee

  4. Anton Pannenkoek Institute for Astronomy, University of Amsterdam, Amsterdam, The Netherlands

    A. de Koter

  5. Max Planck Institute for Astronomy, Heidelberg, Germany

    J. Sanchez-Bermudez & F. Cantalloube

  6. Instituto de Astronomía, Universidad Nacional Autónoma de México, Ciudad de México, Mexico

    J. Sanchez-Bermudez

  7. European Southern Observatory, Santiago, Chile

    C. Paladini, P. Scicluna & X. Haubois

  8. School of Chemistry, University of Leeds, Leeds, UK

    L. Decin

  9. Max Planck Institute for Extraterrestrial Physics, Garching, Germany

    K. Kravchenko

  10. Center for Astrophysics | Harvard and Smithsonian, Cambridge, MA, USA

    A. K. Dupree

  11. NSF’s National Optical-Infrared Astronomy Research Laboratory, Tucson, AZ, USA

    S. Ridgway

  12. European Southern Observatory, Garching bei München, Germany

    M. Wittkowski

  13. Steward Observatory, University of Arizona, Tucson, AZ, USA

    N. Anugu

  14. School of Physics and Astronomy, University of Exeter, Exeter, UK

    N. Anugu & S. Kraus

  15. Physics Department, New Mexico Institute of Mining and Technology, Socorro, NM, USA

    R. Norris

  16. Exoplanets and Stellar Astrophysics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    G. Rau & W. Danchi

  17. Department of Physics, Catholic University of America, Washington, DC, USA

    G. Rau

  18. Department of Astronomy, University of Michigan, Ann Arbor, MI, USA

    J. D. Monnier & J.-B. Le Bouquin

  19. IPAG, Université Grenoble Alpes, CNRS, Grenoble, France

    J.-B. Le Bouquin

Authors
  1. M. Montargès
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Contributions

M.M. wrote the observing proposals, prepared all the observations, reduced and calibrated the ZIMPOL and GRAVITY data, ran the PHOENIX and RADMC3D simulations, made all the figures and is the main contributor to the text. E.C. cross-checked the RADMC3D modelling. E.L., J.S.-B. and F.C. reduced the SPHERE–IRDIS data. A.d.K. and L.D. wrote the discussion and conclusion. All authors contributed substantially to discussion, writing and revisions of the article.

Corresponding author

Correspondence to M. Montargès.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Manfred Cuntz, Edward Guinan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Spectral energy distributions for the various epochs.

a–d, Photometry from the ZIMPOL filters (black circles) and from the AAVSO measurements (grey triangles) is compared to a 3,700-K PHOENIX model (light orange), the best-matching composite PHOENIX model with a cool spot (blue) and best-matching RADMC3D dust clump model (violet). The flux error bars correspond to 1 s.d. The wavelength error bars correspond to the width of the ZIMPOL filters. The AAVSO error bars have been re-estimated from 0.01 mag mostly to 0.1 mag to take into account the uncertainty on the magnitudes of the calibrator star.

Source data

Extended Data Fig. 2 Visual light curve of Betelgeuse.

The data are taken from the AAVSO database over the past century.

Source data

Extended Data Fig. 3 Deconvolved intensity images of Betelgeuse for the various filters observed with ZIMPOL.

The spatial scale is indicated in the bottom left image. North is up; east is left. Each row corresponds to a single filter. Each column corresponds to a single epoch. The colour scales are linear.

Extended Data Fig. 4 Fit of the GRAVITY and IRDIS continuum data by a uniform-disk model.

The black points correspond to the data and the solid red curve to the model. The grey points correspond to excluded photospheric lines. The error bars correspond to 1 s.d. a, Squared visibilities for January 2019. b, Corresponding closure phases. c, Squared visibilities for February 2020. d, Corresponding closure phases.

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Extended Data Fig. 5 Best-matching composite PHOENIX model.

The spatial scale is indicated in the bottom right image. North is up; east is left. Each row corresponds to a single filter. Each column corresponds to a single epoch. The colour scales are linear.

Extended Data Fig. 6 Identification of the RADMC3D model.

Dec, declination; RA, right ascension; R⁎, stellar radius; d, distance of the star to Earth. The clump parameters are defined in Methods.

Extended Data Fig. 7 Best-matching RADMC3D dusty-clump models.

The spatial scale is indicated in the bottom right image. North is up; east is left. Each row corresponds to a single filter. Each column corresponds to a single epoch. The colour scales are linear.

Extended Data Table 1 Log of the VLT/SPHERE observations
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Extended Data Table 2 Log of the VLTI/GRAVITY observations on the A0–B2–D0–C1 quadruplet
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Extended Data Table 3 Modelling results
Full size table

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Montargès, M., Cannon, E., Lagadec, E. et al. A dusty veil shading Betelgeuse during its Great Dimming. Nature 594, 365–368 (2021). https://doi.org/10.1038/s41586-021-03546-8

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