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Evidence for a Vast Prograde Stellar Stream in the Solar Vicinity

Necib, Lina and Ostdiek, Bryan and Lisanti, Mariangela and Cohen, Timothy and Freytsis, Marat and Garrison-Kimmel, Shea and Hopkins, Philip F. and Wetzel, Andrew and Sanderson, Robyn (2020) Evidence for a Vast Prograde Stellar Stream in the Solar Vicinity. Nature Astronomy, 4 (11). pp. 1078-1083. ISSN 2397-3366. doi:10.1038/s41550-020-1131-2.

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Massive dwarf galaxies that merge with the Milky Way on prograde orbits can be dragged into the disk plane before being completely disrupted. Such mergers can contribute to an accreted stellar disk and a dark matter disk. Here we present Nyx, a vast stellar stream in the vicinity of the Sun, which provides the first indication that such an event occurred in the Milky Way. We identify about 200 stars that have coherent radial and prograde motion in this stream using a catalogue of accreted stars built by applying deep learning methods to the Gaia data. Taken together with chemical abundance and orbital information, these results strongly favour the interpretation that Nyx is the remnant of a disrupted dwarf galaxy. Further justified by FIRE hydrodynamic simulations, we demonstrate that prograde streams like Nyx can be found in the disk plane of galaxies and identified using our methods.

Item Type:Article
Related URLs:
URLURL TypeDescription ReadCube access Paper ItemAccreted star catalogue ItemSimulation m12f ItemPython Markov chain Monte Carlo code ItemExtreme deconvolution Correction ReadCube access - Author Correction
Necib, Lina0000-0003-2806-1414
Ostdiek, Bryan0000-0002-0376-6461
Lisanti, Mariangela0000-0002-8495-8659
Cohen, Timothy0000-0002-7040-3038
Freytsis, Marat0000-0002-6427-2895
Garrison-Kimmel, Shea0000-0002-4655-8128
Hopkins, Philip F.0000-0003-3729-1684
Wetzel, Andrew0000-0003-0603-8942
Sanderson, Robyn0000-0003-3939-3297
Additional Information:© 2020 Nature Publishing Group. Received 07 August 2019; Accepted 15 May 2020; Published 06 July 2020. We thank A. Helmi, J. Johnson, E. Kirby, N. Laracy, J. Read, N. Shipp and J. Wojno for helpful discussions. This work was performed in part at Aspen Center for Physics, which is supported by National Science Foundation grant PHY-1607611. This research was supported by the Munich Institute for Astro- and Particle Physics (MIAPP) of the DFG cluster of excellence ‘Origin and Structure of the Universe’. This research was supported in part by the National Science Foundation under Grant No. NSF PHY-1748958. L.N. is supported by the DOE under award number DESC0011632, and the Sherman Fairchild fellowship. M.L. is supported by the DOE under contract DESC0007968 and the Cottrell Scholar Program through the Research Corporation for Science Advancement. B.O. and T.C. are supported by the US Department of Energy under grant number DE-SC0011640. M.F. is supported by the Zuckerman STEM Leadership Program and in part by the DOE under grant number DE-SC0011640. S.G.-K. and P.F.H are supported by an Alfred P. Sloan Research Fellowship, NSF Collaborative Research grant number 1715847 and CAREER grant number 1455342, and NASA grants NNX15AT06G, JPL 1589742 and 17-ATP17-0214. A.W. is supported by NASA, through ATP grant 80NSSC18K1097 and HST grants GO-14734 and AR-15057 from STScI, and a Hellman Fellowship from UC Davis. This work utilized the University of Oregon Talapas high-performance computing cluster. Numerical simulations were run on the Caltech compute cluster ‘Wheeler’, allocations from XSEDE TG-AST130039 and PRAC NSF.1713353 supported by the NSF, and NASA HEC SMD-16-7592. R.S. thanks N. Carriero, I. Fisk and D. Simon of the Scientific Computing Core at the Flatiron Institute for their support of the infrastructure housing the synthetic surveys and simulations used for this work. This work has made use of data from the European Space Agency (ESA) mission Gaia (, processed by the Gaia Data Processing and Analysis Consortium (DPAC, Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. Funding for RAVE has been provided by: the Australian Astronomical Observatory; the Leibniz-Institut für Astrophysik Potsdam (AIP); the Australian National University; the Australian Research Council; the French National Research Agency; the German Research Foundation (SPP 1177 and SFB 881); the European Research Council (ERC-StG 240271 Galactica); the Istituto Nazionale di Astrofisica at Padova; The Johns Hopkins University; the National Science Foundation of the USA (AST-0908326); the W. M. Keck foundation; the Macquarie University; the Netherlands Research School for Astronomy; the Natural Sciences and Engineering Research Council of Canada; the Slovenian Research Agency; the Swiss National Science Foundation; the Science and Technology Facilities Council of the UK; Opticon; Strasbourg Observatory; and the Universities of Groningen, Heidelberg and Sydney. The RAVE website is at The accreted star catalogue used for this analysis is available at The simulation m12f is available at The IDs of the Nyx stars are available as a Supplementary Data file. Code availability: This analysis makes use of emcee and the extreme deconvolution for the Gaussian mixture model. The Python Markov chain Monte Carlo code emcee is freely available and documented at Extreme deconvolution is freely available at Details regarding the application of these two public codes are provided in ref. 22. Author Contributions: All authors discussed the results and commented on the manuscript. M.L. and T.C. conceived the project. L.N. built the data analysis pipeline. B.O., T.C. and M.F. conceptualized the machine learning algorithms. B.O. built the deep neural network and produced the accreted stellar catalogue. Interpretation of the results and writing of the original manuscript were done by L.N. and M.L. The FIRE-2 simulation code was built by P.F.H., and run by P.F.H., S.G.-K. and A.W. S.G.-K. and A.W. ran the halo finding algorithm on the simulation, and L.N. identified the mergers as a function of redshift. R.S. built the mock catalogues used in the training of the neural network. The authors declare no competing interests.
Errata:Necib, L., Ostdiek, B., Lisanti, M. et al. Author Correction: Evidence for a vast prograde stellar stream in the solar vicinity. Nat Astron (2022).
Group:TAPIR, Astronomy Department, Walter Burke Institute for Theoretical Physics
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0011632
Sherman Fairchild FoundationUNSPECIFIED
Department of Energy (DOE)DE-SC0007968
Cottrell Scholar of Research CorporationUNSPECIFIED
Department of Energy (DOE)DE-SC0011640
Zuckerman STEM Leadership ProgramUNSPECIFIED
Alfred P. Sloan FoundationUNSPECIFIED
NASA Hubble FellowshipGO-14734
NASA Hubble FellowshipAR-15057
University of California, DavisUNSPECIFIED
Flatiron InstituteUNSPECIFIED
Gaia Multilateral AgreementUNSPECIFIED
Australian Astronomical ObservatoryUNSPECIFIED
Leibniz-Institut fuer Astrophysik Potsdam (AIP)UNSPECIFIED
Australian National UniversityUNSPECIFIED
Australian Research CouncilUNSPECIFIED
Agence Nationale pour la Recherche (ANR)UNSPECIFIED
Deutsche Forschungsgemeinschaft (DFG)SPP 1177
Deutsche Forschungsgemeinschaft (DFG)SFB 881
European Research Council (ERC)240271
Istituto Nazionale di Astrofisica (INAF)UNSPECIFIED
Johns Hopkins UniversityUNSPECIFIED
W. M. Keck FoundationUNSPECIFIED
Macquarie UniversityUNSPECIFIED
Nederlandse Onderzoekschool voor de Astronomie (NOVA)UNSPECIFIED
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Slovenian Research AgencyUNSPECIFIED
Swiss National Science Foundation (SNSF)UNSPECIFIED
Science and Technology Facilities Council (STFC)UNSPECIFIED
Strasbourg ObservatoryUNSPECIFIED
University of GroningenUNSPECIFIED
University of HeidelbergUNSPECIFIED
University of SydneyUNSPECIFIED
Issue or Number:11
Record Number:CaltechAUTHORS:20190726-074106283
Persistent URL:
Official Citation:Necib, L., Ostdiek, B., Lisanti, M. et al. Evidence for a vast prograde stellar stream in the solar vicinity. Nat Astron 4, 1078–1083 (2020).
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97430
Deposited By: Tony Diaz
Deposited On:26 Jul 2019 17:47
Last Modified:19 May 2022 19:33

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