Barnard’s star is a red dwarf, and has the largest proper motion (apparent motion across the sky) of all known stars. At a distance of 1.8 parsecs1, it is the closest single star to the Sun; only the three stars in the α Centauri system are closer. Barnard’s star is also among the least magnetically active red dwarfs known2,3 and has an estimated age older than the Solar System. Its properties make it a prime target for planetary searches; various techniques with different sensitivity limits have been used previously, including radial-velocity imaging4,5,6, astrometry7,8 and direct imaging9, but all ultimately led to negative or null results. Here we combine numerous measurements from high-precision radial-velocity instruments, revealing the presence of a low-amplitude periodic signal with a period of 233 days. Independent photometric and spectroscopic monitoring, as well as an analysis of instrumental systematic effects, suggest that this signal is best explained as arising from a planetary companion. The candidate planet around Barnard’s star is a cold super-Earth, with a minimum mass of 3.2 times that of Earth, orbiting near its snow line (the minimum distance from the star at which volatile compounds could condense). The combination of all radial-velocity datasets spanning 20 years of measurements additionally reveals a long-term modulation that could arise from a stellar magnetic-activity cycle or from a more distant planetary object. Because of its proximity to the Sun, the candidate planet has a maximum angular separation of 220 milliarcseconds from Barnard’s star, making it an excellent target for direct imaging and astrometric observations in the future.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Data availability

The public high-resolution spectroscopic raw data used in the study can be freely downloaded from the corresponding facility archives: HIRES,; UVES, HARPSpre and HARPSpost,; HARPS-N,; APF, Proprietary raw data are available from the corresponding author on reasonable request. The nightly averaged, fully calibrated radial velocities, spectroscopic indices and photometric measurements are available as Supplementary Data.

Additional information

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


  1. 1.

    Brown, A. G. A. et al. Gaia data release 2: summary of the contents and survey properties. Astron. Astrophys. 616, A1 (2018).

  2. 2.

    Liefke, C. & Schmitt, J. H. M. M. The NEXXUS database – X-ray properties of nearby stars. ESA Spec. Publ. 560, 755–756 (2005).

  3. 3.

    Suárez Mascareño, A., Rebolo, R., González Hernández, J. I. & Esposito, M. Rotation periods of late-type dwarf stars from time series high-resolution spectroscopy of chromospheric indicators. Mon. Not. R. Astron. Soc. 452, 2745–2756 (2015).

  4. 4.

    Zechmeister, M., Kürster, M. & Endl, M. The M dwarf planet search programme at the ESO VLT + UVES. A search for terrestrial planets in the habitable zone of M dwarfs. Astron. Astrophys. 505, 859–871 (2009).

  5. 5.

    Choi, J. et al. Precise Doppler monitoring of Barnard’s star. Astrophys. J. 764, 131 (2013).

  6. 6.

    Bonfils, X. et al. The HARPS search for southern extra-solar planets. XXXI. The M-dwarf sample. Astron. Astrophys. 549, A109 (2013).

  7. 7.

    van de Kamp, P. The planetary system of Barnard’s star. Vistas Astron. 26, 141–157 (1982).

  8. 8.

    Benedict, G. F. et al. Interferometric astrometry of Proxima Centauri and Barnard’s star using Hubble Space Telescope Fine Guidance Sensor 3: detection limits for substellar companions. Astron. J. 118, 1086–1100 (1999).

  9. 9.

    Gauza, B. et al. Constraints on the substellar companions in wide orbits around the Barnard’s star from CanariCam mid-infrared imaging. Mon. Not. R. Astron. Soc. 452, 1677–1683 (2015).

  10. 10.

    Anglada-Escudé, G. et al. A terrestrial planet candidate in a temperate orbit around Proxima Centauri. Nature 536, 437–440 (2016).

  11. 11.

    Quirrenbach, A. et al. CARMENES instrument overview. Proc. SPIE 9147, 91471F (2014).

  12. 12.

    Meschiari, S. et al. Systemic: a testbed for characterizing the detection of extrasolar planets. I. The systemic console package. Publ. Astron. Soc. Pacif. 121, 1016–1027 (2009).

  13. 13.

    Benedict, G. F. et al. Photometry of Proxima Centauri and Barnard’s star using Hubble Space Telescope Fine Guidance Sensor 3: a search for periodic variations. Astron. J. 116, 429–439 (1998).

  14. 14.

    Feng, F., Tuomi, M., Jones, H. R. A., Butler, R. P. & Vogt, S. A Goldilocks principle for modelling radial velocity noise. Mon. Not. R. Astron. Soc. 461, 2440–2452 (2016).

  15. 15.

    Kopparapu, R. K. et al. Habitable zones around main-sequence stars: dependence on planetary mass. Astrophys. J. 787, L29 (2014).

  16. 16.

    Gaidos, E., Mann, A. W., Kraus, A. L. & Ireland, M. They are small worlds after all: revised properties of Kepler M dwarf stars and their planets. Mon. Not. R. Astron. Soc. 457, 2877–2899 (2016).

  17. 17.

    Dressing, C. D. & Charbonneau, D. The occurrence of potentially habitable planets orbiting M dwarfs estimated from the full Kepler dataset and an empirical measurement of the detection sensitivity. Astrophys. J. 807, 45 (2015).

  18. 18.

    Perryman, M., Hartman, J., Bakos, G. Á. & Lindegren, L. Astrometric exoplanet detection with Gaia. Astrophys. J. 797, 14 (2014).

  19. 19.

    Casertano, S. et al. Parallax of Galactic Cepheids from spatially scanning the Wide Field Camera 3 on the Hubble Space Telescope: the case of SS Canis Majoris. Astrophys. J. 825, 11 (2016).

  20. 20.

    Trauger, J. et al. Hybrid Lyot coronagraph for WFIRST-AFTA: coronagraph design and performance metrics. J. Astron. Telesc. Instrum. Syst. 2, 011013 (2016).

  21. 21.

    Kennedy, G. M. & Kenyon, S. J. Planet formation around stars of various masses: the snow line and the frequency of giant planets. Astrophys. J. 673, 502–512 (2008).

  22. 22.

    Stevenson, D. J. & Lunine, J. I. Rapid formation of Jupiter by diffuse redistribution of water vapor in the solar nebula. Icarus 75, 146–155 (1988).

  23. 23.

    Morbidelli, A., Lambrechts, M., Jacobson, S. & Bitsch, B. The great dichotomy of the Solar System: small terrestrial embryos and massive giant planet cores. Icarus 258, 418–429 (2015).

  24. 24.

    Mulders, G. D., Pascucci, I. & Apai, D. An increase in the mass of planetary systems around lower-mass stars. Astrophys. J. 814, 130 (2015).

  25. 25.

    Drążkowska, J. & Alibert, Y. Planetesimal formation starts at the snow line. Astron. Astrophys. 608, A92 (2017).

  26. 26.

    Kley, W. & Nelson, R. P. Planet-disk interaction and orbital evolution. Annu. Rev. Astron. Astrophys. 50, 211–249 (2012).

  27. 27.

    Gaudi, B. S. Microlensing surveys for exoplanets. Annu. Rev. Astron. Astrophys. 50, 411–453 (2012).

  28. 28.

    Suzuki, D. et al. The exoplanet mass-ratio function from the MOA-II survey: discovery of a break and likely peak at a Neptune mass. Astrophys. J. 833, 145 (2016).

  29. 29.

    Passegger, V. M. et al. The CARMENES search for exoplanets around M dwarfs. Photospheric parameters of target stars from high-resolution spectroscopy. Astron. Astrophys. 615, A6 (2018).

  30. 30.

    Vogt, S. S. et al. HIRES: the high-resolution Echelle spectrometer on the Keck 10-m telescope. Proc. SPIE 2198, 362 (1994).

  31. 31.

    Crane, J. D. et al. The Carnegie planet finder spectrograph: integration and commissioning. Proc. SPIE 7735, 773553 (2010).

  32. 32.

    Vogt, S. S. et al. APF – the Lick Observatory automated planet finder. Publ. Astron. Soc. Pacif. 126, 359–379 (2014).

  33. 33.

    Butler, R. P. et al. Attaining Doppler precision of 3 m s−1. Publ. Astron. Soc. Pacif. 108, 500–509 (1996).

  34. 34.

    Mayor, M. et al. Setting new standards with HARPS. Messenger 114, 20–24 (2003).

  35. 35.

    Cosentino, R. et al. HARPS-N: the new planet hunter at TNG. Proc. SPIE 8446, 84461V (2012).

  36. 36.

    Anglada-Escudé, G. & Butler, R. P. The HARPS-TERRA project. I. Description of the algorithms, performance, and new measurements on a few remarkable stars observed by HARPS. Astrophys. J. Suppl. Ser. 200, 15 (2012).

  37. 37.

    Dumusque, X., Pepe, F., Lovis, C. & Latham, D. W. Characterization of a spurious one-year signal in HARPS data. Astrophys. J. 808, 171 (2015).

  38. 38.

    Quirrenbach, A. et al. CARMENES: an overview six months after first light. Proc. SPIE 9908, 990812 (2016).

  39. 39.

    Zechmeister, M. et al. Spectrum radial velocity analyser (SERVAL). High-precision radial velocities and two alternative spectral indicators. Astron. Astrophys. 609, A12 (2018).

  40. 40.

    Trifonov, T. et al. The CARMENES search for exoplanets around M dwarfs. First visual-channel radial-velocity measurements and orbital parameter updates of seven M-dwarf planetary systems. Astron. Astrophys. 609, A117 (2018).

  41. 41.

    Wright, J. T. & Eastman, J. D. Barycentric corrections at 1 cm s−1 for precise Doppler celocities. Publ. Astron. Soc. Pacif. 126, 838–852 (2014).

  42. 42.

    Lucy, L. B. Spectroscopic binaries with elliptical orbits. Astron. Astrophys. 439, 663–670 (2005).

  43. 43.

    Scargle, J. D. Studies in astronomical time series analysis. I. Modeling random processes in the time domain. Astrophys. J. Suppl. Ser. 45, 1–71 (1981).

  44. 44.

    Tuomi, M. et al. Habitable-zone super-Earth candidate in a six-planet system around the K2.5V star HD 40307. Astron. Astrophys. 549, A48 (2013).

  45. 45.

    Rasmussen, C. E. & Williams, C. K. I. Gaussian Processes for Machine Learning (MIT Press, Cambridge, 2006).

  46. 46.

    Foreman-Mackey, D., Agol, E., Ambikasaran, S. & Angus, R. Fast and scalable Gaussian process modeling with applications to astronomical time series. Astron. J. 154, 220 (2017).

  47. 47.

    Baluev, R. V. Accounting for velocity jitter in planet search surveys. Mon. Not. R. Astron. Soc. 393, 969–978 (2009).

  48. 48.

    Baluev, R. V. Assessing the statistical significance of periodogram peaks. Mon. Not. R. Astron. Soc. 385, 1279–1285 (2008).

  49. 49.

    Ford, E. B. Improving the efficiency of Markov chain Monte Carlo for analyzing the orbits of extrasolar planets. Astrophys. J. 642, 505–522 (2006).

  50. 50.

    Dumusque, X. Radial velocity fitting challenge. I. Simulating the data set including realistic stellar radial-velocity signals. Astron. Astrophys. 593, A5 (2016).

  51. 51.

    Duncan, D. K. et al. Ca ii H and K measurements made at Mount Wilson Observatory, 1966-1983. Astrophys. J. Suppl. Ser. 76, 383–430 (1991).

  52. 52.

    Suárez Mascareño, A. et al. HADES RV programme with HARPS-N at TNG. VII. Rotation and activity of M-Dwarfs from time-series high-resolution spectroscopy of chromospheric indicators. Astron. Astrophys. 612, A89 (2018).

  53. 53.

    Suárez Mascareño, A., Rebolo, R. & González Hernández, J. I. Magnetic cycles and rotation periods of late-type stars from photometric time series. Astron. Astrophys. 595, A12 (2016).

  54. 54.

    Pojmański, G., Pilecki, B. & Szczygieł, D. The All Sky Automated Survey. Catalog of variable stars. V. Declinations 0°-+28° of the Northern Hemisphere. Acta Astron. 55, 275–301 (2005).

  55. 55.

    Berta, Z. K., Irwin, J., Charbonneau, D., Burke, C. J. & Falco, E. E. Transit detection in the MEarth survey of nearby M dwarfs: bridging the clean-first, search-later divide. Astron. J. 144, 145 (2012).

  56. 56.

    Zechmeister, M. & Kürster, M. The generalised Lomb-Scargle periodogram. A new formalism for the floating-mean and Keplerian periodograms. Astron. Astrophys. 496, 577–584 (2009).

  57. 57.

    Affer, L. et al. HADES RV program with HARPS-N at the TNG GJ 3998: an early M-dwarf hosting a system of super-Earths. Astron. Astrophys. 593, A117 (2016).

  58. 58.

    Mortier, A. & Collier Cameron, A. Stacked Bayesian general Lomb-Scargle periodogram: identifying stellar activity signals. Astron. Astrophys. 601, A110 (2017).

Download references


The results are based on observations made with the CARMENES instrument at the 3.5-m telescope of the Centro Astronómico Hispano-Alemán de Calar Alto (CAHA, Almería, Spain), funded by the German Max-Planck-Gesellschaft (MPG), the Spanish Consejo Superior de Investigaciones Científicas (CSIC), the European Union and the CARMENES Consortium members; the 90-cm telescope at the Sierra Nevada Observatory (Granada, Spain) and the 40-cm robotic telescope at the SPACEOBS observatory (San Pedro de Atacama, Chile), both operated by the Instituto de Astrofísica de Andalucía (IAA); and the 80-cm Joan Oró Telescope (TJO) of the Montsec Astronomical Observatory (OAdM), owned by the Generalitat de Catalunya and operated by the Institute of Space Studies of Catalonia (IEEC). This research was supported by the following institutions, grants and fellowships: Spanish MINECO ESP2016-80435-C2-1-R, ESP2016-80435-C2-2-R, AYA2016-79425-C3-1-P, AYA2016-79245-C3-2-P, AYA2016-79425-C3-3-P, AYA2015-69350-C3-2-P, ESP2014-54362-P, AYA2014-56359-P, RYC-2013-14875; Generalitat de Catalunya/CERCA programme; Fondo Europeo de Desarrollo Regional (FEDER); German Science Foundation (DFG) Research Unit FOR2544, project JE 701/3-1; STFC Consolidated Grants ST/P000584/1, ST/P000592/1, ST/M001008/1; NSF AST-0307493; Queen Mary University of London Scholarship; Perren foundation grant; CONICYT-FONDECYT 1161218, 3180405; Swiss National Science Foundation (SNSF); Koshland Foundation and McDonald-Leapman grant; and NASA Hubble Fellowship grant HST-HF2-51399.001. J.T. is a Hubble Fellow.

Reviewer information

Nature thanks I. Snellen and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information


  1. Institut de Ciències de l’Espai (ICE, CSIC), Campus UAB, Bellaterra, Spain

    • I. Ribas
    • , J. C. Morales
    • , M. Perger
    • , A. Rosich
    • , E. Herrero
    •  & M. Lafarga
  2. Institut d’Estudis Espacials de Catalunya (IEEC), Barcelona, Spain

    • I. Ribas
    • , J. C. Morales
    • , M. Perger
    • , A. Rosich
    • , E. Herrero
    •  & M. Lafarga
  3. Centre for Astrophysics Research, University of Hertfordshire, Hatfield, UK

    • M. Tuomi
    • , F. Feng
    •  & H. R. A. Jones
  4. Institut für Astrophysik Göttingen, Georg-August-Universität Göttingen, Göttingen, Germany

    • A. Reiners
    • , S. Dreizler
    • , S. V. Jeffers
    • , L. Tal-Or
    •  & M. Zechmeister
  5. Department of Terrestrial Magnetism, Carnegie Institution for Science, Washington, DC, USA

    • R. P. Butler
    • , J. Teske
    •  & S. X. Wang
  6. Instituto de Astrofísica de Andalucía (IAA, CSIC), Granada, Spain

    • C. Rodríguez-López
    • , E. Rodríguez
    • , P. J. Amado
    • , M. J. López-González
    •  & G. Anglada-Escudé
  7. Instituto de Astrofísica de Canarias (IAC), La Laguna, Spain

    • J. I. González Hernández
    • , F. Murgas
    • , B. Toledo-Padrón
    • , V. J. S. Béjar
    • , E. Pallé
    • , R. Rebolo
    •  & A. Suárez Mascareño
  8. Universidad de La Laguna (ULL), Departamento de Astrofísica, La Laguna, Spain

    • J. I. González Hernández
    • , F. Murgas
    • , B. Toledo-Padrón
    • , V. J. S. Béjar
    • , E. Pallé
    •  & R. Rebolo
  9. Max-Planck-Institut für Astronomie, Heidelberg, Germany

    • T. Trifonov
    • , Th. Henning
    •  & M. Kürster
  10. UCO/Lick Observatory, University of California at Santa Cruz, Santa Cruz, CA, USA

    • S. S. Vogt
    •  & B. Holden
  11. Centro de Astrobiología, CSIC-INTA, ESAC, Villanueva de la Cañada, Spain

    • J. A. Caballero
    •  & M. Cortés-Contreras
  12. Thüringer Landessternwarte, Tautenburg, Germany

    • A. Hatzes
  13. School of Physics and Astronomy, Queen Mary University of London, London, UK

    • R. P. Nelson
    • , J. B. P. Strachan
    •  & G. Anglada-Escudé
  14. School of Geosciences, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, Tel Aviv, Israel

    • L. Tal-Or
  15. Landessternwarte, Zentrum für Astronomie der Universität Heidelberg, Heidelberg, Germany

    • A. Quirrenbach
    • , A. Kaminski
    • , S. Reffert
    •  & W. Seifert
  16. Centro Astronómico Hispano-Alemán (CSIC-MPG), Observatorio Astronómico de Calar Alto, Gérgal, Spain

    • M. Azzaro
  17. School of Physical Sciences, The Open University, Milton Keynes, UK

    • J. R. Barnes
    • , C. A. Haswell
    •  & D. Staab
  18. Departamento de Astronomía, Universidad de Chile, Santiago, Chile

    • Z. M. Berdiñas
    •  & J. Jenkins
  19. Kavli Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    • J. Burt
  20. Physikalisches Institut, Universität Bern, Bern, Switzerland

    • G. Coleman
  21. The Observatories, Carnegie Institution for Science, Pasadena, CA, USA

    • J. Crane
    •  & S. A. Shectman
  22. Department of Astrophysics and Planetary Science, Villanova University, Villanova, PA, USA

    • S. G. Engle
    •  & E. F. Guinan
  23. Warsaw University Observatory, Warsaw, Poland

    • M. Kiraga
  24. Department of Earth Sciences and Department of Physics, The University of Hong Kong, Pok Fu Lam, Hong Kong

    • M. H. Lee
  25. Departamento de Física de la Tierra Astronomía y Astrofísica and UPARCOS-UCM (Unidad de Física de Partículas y del Cosmos de la UCM), Facultad de Ciencias Físicas, Universidad Complutense de Madrid, Madrid, Spain

    • D. Montes
  26. Laboratoire Univers et Particules de Montpellier, Université de Montpellier, CNRS, Montpellier, France

    • J. Morin
  27. Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel

    • A. Ofir
  28. Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain

    • R. Rebolo
  29. Hamburger Sternwarte, Universität Hamburg, Hamburg, Germany

    • A. Schweitzer
  30. Las Cumbres Observatory Global Telescope Network, Goleta, CA, USA

    • R. A. Street
  31. Observatoire Astronomique de l’Université de Genève, Versoix, Switzerland

    • A. Suárez Mascareño
  32. Zentrum für Astronomie der Universität Heidelberg, Astronomisches Rechen-Institut, Heidelberg, Germany

    • Y. Tsapras


  1. Search for I. Ribas in:

  2. Search for M. Tuomi in:

  3. Search for A. Reiners in:

  4. Search for R. P. Butler in:

  5. Search for J. C. Morales in:

  6. Search for M. Perger in:

  7. Search for S. Dreizler in:

  8. Search for C. Rodríguez-López in:

  9. Search for J. I. González Hernández in:

  10. Search for A. Rosich in:

  11. Search for F. Feng in:

  12. Search for T. Trifonov in:

  13. Search for S. S. Vogt in:

  14. Search for J. A. Caballero in:

  15. Search for A. Hatzes in:

  16. Search for E. Herrero in:

  17. Search for S. V. Jeffers in:

  18. Search for M. Lafarga in:

  19. Search for F. Murgas in:

  20. Search for R. P. Nelson in:

  21. Search for E. Rodríguez in:

  22. Search for J. B. P. Strachan in:

  23. Search for L. Tal-Or in:

  24. Search for J. Teske in:

  25. Search for B. Toledo-Padrón in:

  26. Search for M. Zechmeister in:

  27. Search for A. Quirrenbach in:

  28. Search for P. J. Amado in:

  29. Search for M. Azzaro in:

  30. Search for V. J. S. Béjar in:

  31. Search for J. R. Barnes in:

  32. Search for Z. M. Berdiñas in:

  33. Search for J. Burt in:

  34. Search for G. Coleman in:

  35. Search for M. Cortés-Contreras in:

  36. Search for J. Crane in:

  37. Search for S. G. Engle in:

  38. Search for E. F. Guinan in:

  39. Search for C. A. Haswell in:

  40. Search for Th. Henning in:

  41. Search for B. Holden in:

  42. Search for J. Jenkins in:

  43. Search for H. R. A. Jones in:

  44. Search for A. Kaminski in:

  45. Search for M. Kiraga in:

  46. Search for M. Kürster in:

  47. Search for M. H. Lee in:

  48. Search for M. J. López-González in:

  49. Search for D. Montes in:

  50. Search for J. Morin in:

  51. Search for A. Ofir in:

  52. Search for E. Pallé in:

  53. Search for R. Rebolo in:

  54. Search for S. Reffert in:

  55. Search for A. Schweitzer in:

  56. Search for W. Seifert in:

  57. Search for S. A. Shectman in:

  58. Search for D. Staab in:

  59. Search for R. A. Street in:

  60. Search for A. Suárez Mascareño in:

  61. Search for Y. Tsapras in:

  62. Search for S. X. Wang in:

  63. Search for G. Anglada-Escudé in:


I.R. led the CARMENES team and the TJO photometry, organized the analysis of the data and wrote most of the manuscript. M.T. performed the initial radial-velocity analysis and, with J.C.M., M.P., S.D., A.Ro., F.F., T.T., S.S.V., A.H., A.K., S.S.V., J.J. and A.S.M., participated in the analysis of radial-velocity data using various methods. A.Re. co-led the CARMENES team. R.P.B. led the HIRES/PFS/APF team and reprocessed the UVES data. C.R.-L. coordinated the acquisition and analysis of photometry. J.I.G.H., R.R., A.S.M. and B.T.-P. acquired HARPS-N data and measured chromospheric indices from all spectroscopic datasets. T.T. and M.H.L. studied the dynamics. S.S.V. co-led the HIRES/APF teams. J.A.C. is responsible for the CARMENES instrument and, with A.S. and M.C.-C., determined the stellar properties. E.H., F.M., E.R., J.B.P.S., S.G.E., E.F.G., M.Ki. and M.J.L.-G. participated in the photometric monitoring. S.V.J. contributed to the analysis of activity and to the preparation of the manuscript. M.L. calculated the cross-correlation function parameters of CARMENES spectra. R.N. participated in the discussion of implications for planet formation. A.Q. and P.J.A. are principal investigators of CARMENES. M.A., V.J.S.B., T.H., M.Ku., D.M., E.P., S.R. and W.S. are members of the CARMENES Consortium. L.T.-O. calibrated the CARMENES data and carried out calculations of astrometric detection. M.Z. reduced the CARMENES data. J.T., J.B., J.D.C., B.H., S.A.S. and S.X.W. participated in the acquisition and discussion of HIRES, PFS and APF data. J.R.B., G.C., C.A.H., J.J., H.R.A.J., J.M., A.O., D.S., R.A.S. and Y.T. participated in the RedDots2017 Collaboration. Z.M.B. participated in the discussion of instrument systematics. G.A.-E. organized the collaboration, coordinated the RedDots2017 campaign, organized and performed analyses and participated in writing the manuscript. All authors were given the opportunity to review the results and comment on the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to I. Ribas.

Extended data figures and tables

  1. Extended Data Fig. 1 Hierarchical periodogram analysis.

    a, Magnitude of the window function |w| of the combined datasets. bd, Likelihood periodogram of the radial-velocity measurements considering the first signal search (b), the residuals after modelling a long-period (6,600 days) signal (c) and the residuals after modelling long-period and 233-day periodicities (d). No high-significance signals remain in d, in particular in the 10–40-day region, corresponding to the conservative habitable zone. The region below 10 days is not shown for clarity, but it is also devoid of significant periodic signals down to the Nyquist frequency of the dataset (2 days). Two different scales for the horizontal axis are used to improve the visibility of the low-frequency range. The red dotted line marks the 0.1% FAP threshold.

  2. Extended Data Fig. 2 Evolution of the significance of the 233-day signal.

    The top panel shows the PSD56 of a stacked periodogram57,58 and the bottom panel depicts a cumulative measurement of the semi-amplitude K of the signal, with the grey lines showing 1σ error bars. The horizontal red dotted line, green dashed line and blue solid lines show the 10%, 1% and 0.1% FAP thresholds. The evolution of the significance is stable with time and the variations in the amplitude over the last nine years of observations are smaller than 5% of the measured amplitude. The steady increase in signal significance and the stable amplitude are both consistent with the expected evolution of the evidence for a signal of Keplerian origin.

  3. Extended Data Fig. 3 Propagation of astrometric errors to radial velocity systematics.

    a, Spurious radial-velocity effect ΔRV that would be caused by offsets with respect to the catalogue coordinates (black and red) and proper motions (green and blue). b, Illustration of the radial-velocity effect caused by an offset in the parallax with respect to the catalogue value. The uncertainties of the astrometric parameters for Barnard’s star from the Hipparcos catalogue were used in the barycentric corrections, and are approximately 10 times smaller than the values used in this plot (15 mas in position, 1.5 mas yr−1 in proper motion and 1.5 mas in parallax), implying that catalogue errors introduce undetectable signals.

  4. Extended Data Fig. 4 Effect of Gaussian-process modelling applied to synthetic and real data.

    Blue squares represent the improvement in the log-likelihood using a Gaussian process to model the correlated noise when trying to detect a first signal. The same procedure is applied to simulated observations generated with white noise and a sinusoidal signal consistent with the parameters of the candidate planet (red circles). Even in absence of true correlated noise, the Gaussian process absorbs a substantial amount of significance (ΔlnL ≈ 30 for this selection of kernel parameters). The adopted kernel is a damped SHO, with a damping timescale of τ = Plife = 100 days, and each point corresponds to different values for the oscillator frequency ν (x axis). The PSD of an SHO kernel with ν = 140−1 d−1 and τ = 100 days is depicted as a black line. The greatest reduction in significance occurs when the trial frequency approaches that of the oscillator, but this reduction in significance extends out to a broad range of frequencies, therefore acting as a filter. The period of the candidate planet is marked with a vertical dashed line, and the likely rotation period derived from stellar activity is marked with a vertical dotted line.

  5. Extended Data Fig. 5 Distribution of empirical false alarms from synthetic observations with correlated noise.

    These simulations were obtained by generating synthetic observations following kernels derived from the observations, and then fitted to moving-average models. The resulting distribution of false alarms shows a clear excess around the measured rotation period of the star (vertical dashed blue line) and at low frequencies (long periods), owing to the use of the free offsets in the model (left of the rotation period). The empirical FAP was computed by counting the number of false alarms in the interval ΔlnL [32, ∞) and frequency [0, 1/230] (left of the green line and above the red line) and dividing by total number of false alarms in the same frequency interval (left of the green line). Empirical FAP thresholds of 10%, 1% and 0.1% are shown for reference. The candidate signal under discussion is shown as a red square and has an empirical FAP of about 0.8%. The orange histogram at the bottom shows the distribution of false alarms in frequency (arbitrary normalization).

  6. Extended Data Table 1 Log of observations of Barnard’s star
  7. Extended Data Table 2 Additional fit parameters and fit results
  8. Extended Data Table 3 Zero-point offsets between overlapping radial-velocity datasets from different instruments

Supplementary information

  1. Supplementary Data 1

    This file contains Supplementary Data 1: Time-series measurements of radial velocity. Individual files for each of the instruments used (APF, CARMENES, HARPSN, HARPSpost, HARPSpre, HIRES, PFS, UVES) are provided in plain ASCII format with space delimiters arranged in three columns with headers: • Column 1: Barycentric Julian Date of the mid-time of the observation (BJD) • Column 2: Radial velocity (RV) in units of m s-1 • Column 3: 1-sigma uncertainty in the radial velocity (sigma_RV) in units of m s-1

  2. Supplementary Data 2

    This file contains Supplementary Data 2: Time-series measurements of the Hα index. Individual files for each of the instruments used (APF, CARMENES, HARPSN, HARPSpost, HARPSpre, HIRES, PFS, UVES) are provided in plain ASCII format with space delimiters arranged in three columns with headers: • Column 1:Barycentric Julian Date of the mid-time of the observation (BJD) • Column 2:Hα index (Halphindex; dimensionless) • Column 3: 1-sigma uncertainty in the Hα index (sigma_Halpindex; dimensionless)

  3. Supplementary Data 3

    This file contains Supplementary Data 3: Time-series measurement of the S index of the Ca II H&K chromospheric lines. Individual files for each of the instruments with coverage in the relevant wavelength range (APF, HARPSN, HARPSpost, HARPSpre, HIRES) are provided in plain ASCII format with space delimiters arranged in three columns with headers: • Column 1: Barycentric Julian Date of the mid-time of the observation (BJD) • Column 2: S index (Sindex; dimensionless) • Column 3: 1-sigma uncertainty in the S index (sigma_Sindex; dimensionless)

  4. Supplementary Data 4

    This file contains Supplementary Data 4: Time-series photometric measurements. Individual files for each of the instruments used (AAVSO, APT, ASAS, MEarth, OAdM, RCT, SNO) are provided in plain ASCII format with space delimiters arranged in three columns with headers: • Column 1: Barycentric Julian Date of the mid-time of the observation (BJD) • Column 2: Differential magnitude (Delta_mag) • Column 3: 1-sigma uncertainty in the differential magnitudes (sigma_Deltamag). The photometric filters used are: AAVSO: Johnson V; APT: Johnson V; ASAS: Johnson V; MEarth: RG715nm; OAdM: Cousins R; RCT: Johnson V; SNO: Johnson V

About this article

Publication history





Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.