Ruprecht-Karls-Universität Heidelberg
Subproject A7: Unveiling Dark Matter and Fossil Records in the Solar Neighborhood

Subproject A7: Unveiling Dark Matter and Fossil Records in the Solar Neighborhood

Glenn van de Ven (MPIA), Andreas Just (ARI)

Inferring the local dark matter density is a challenge affected by, among other limitations, the still comparatively small stellar samples. A long-term goal of subproject A7 is to overcome these limitations in chemo-kinematical modeling for the extended Solar neighborhood. Excellent discrete data have become available over the past few years, but development of proper analysis and modeling methods has clearly been lagging behind. With our newly developed likelihood fitting approach, we have been able to show during the first funding period that the vertical and radial motions in the Milky Way disk are significantly coupled and robustly measure this so-called velocity ellipsoid tilt. This resulted in the problem that the commonly adopted modeling assumption of a zero tilt is invalid, but we succeeded in deriving and applying a new solution of the Jeans equations that allows for a non-zero tilt. These computationally-efficient Jeans models will make it possible to handle massive datasets such as from Gaia and to constrain the very large parameter range before applying more general but computationally-intensive modeling approaches like Schwarzschild's orbit-superposition method.

A self-consistent dynamical model fitted to these observational data from RAVE, SDSS, the Gaia-ESO Survey, and soon Gaia data can connect the Galactic gravitational potential and the phase-space distribution functions of the stars selected by abundance patterns. With accurate measurements of the gravitational potential and precise constraints on the stellar orbit distribution, we can address the following central issues in the second funding period: What is the local density of dark matter, needed to inform laboratory experiments designed to detect dark matter particles and to constrain the overall dark matter distribution in the Milky Way? Which fraction of stars at the solar radius has come from disrupted satellites and which fraction is trapped in dynamical resonances? This project will combine a likelihood approach with Schwarzschild’s orbit-superposition method to derive consistently the gravitational potential and the phase space distribution functions of stellar subpopulations dependent on their abundance pattern. We can address important questions such as: What is the local dark matter density and which stars originate from disrupted satellite galaxies?


Alfaro-Cuello, M.; Kacharov, N.; Neumayer, N.; Bianchini, P.; Mastrobuono-Battisti, A.; Lützgendorf, N.; Seth, A. C.; Böker, T.; Kamann, S.; Leaman, R.; Watkins, L. L.; van de Ven, G.
03/2020 ApJ 892, 20

Boecker, Alina; Leaman, Ryan; van de Ven, Glenn; Norris, Mark A.; Mackereth, J. Ted; Crain, Robert A.
01/2020 MNRAS 491, 823

Cordoni, G.; Milone, A. P.; Mastrobuono-Battisti, A.; Marino, A. F.; Lagioia, E. P.; Tailo, M.; Baumgardt, H.; Hilker, M.
01/2020 ApJ 889, 18

Leaman, Ryan; Fragkoudi, Francesca; Querejeta, Miguel; Leung, Gigi Y. C.; Gadotti, Dimitri A.; Husemann, Bernd; Falcón-Barroso, Jesus; Sánchez-Blázquez, Patricia; van de Ven, Glenn; Kim, Taehyun; Coelho, Paula; Lyubenova, Mariya; de Lorenzo-Cáceres, Adriana; Martig, Marie; Martinez-Valpuesta, Inma; Neumann, Justus; Pérez, Isabel; Seidel, Marja
09/2019 MNRAS 488, 3904

Taibi, S.; Battaglia, G.; Kacharov, N.; Rejkuba, M.; Irwin, M.; Leaman, R.; Zoccali, M.; Tolstoy, E.; Jablonka, P.
10/2018 A&A 618, A122

Leung, Gigi Y. C.; Leaman, Ryan; van de Ven, Glenn; Lyubenova, Mariya; Zhu, Ling; Bolatto, Alberto D.; Falcón-Barroso, Jesus; Blitz, Leo; Dannerbauer, Helmut; Fisher, David B.; Levy, Rebecca C.; Sanchez, Sebastian F.; Utomo, Dyas; Vogel, Stuart; Wong, Tony; Ziegler, Bodo
06/2018 MNRAS 477, 254

Pinna, F.; Falcón-Barroso, J.; Martig, M.; Martínez-Valpuesta, I.; Méndez-Abreu, J.; van de Ven, G.; Leaman, R.; Lyubenova, M.
04/2018 MNRAS 475, 2697

Abbate, F.; Mastrobuono-Battisti, A.; Colpi, M.; Possenti, A.; Sippel, A. C.; Dotti, M.
01/2018 MNRAS 473, 927

Martín-Navarro, Ignacio; Brodie, Jean P.; Romanowsky, Aaron J.; Ruiz-Lara, Tomás; van de Ven, Glenn
01/2018 Natur 553, 307

Zhu, Ling; van den Bosch, Remco; van de Ven, Glenn; Lyubenova, Mariya; Falcón-Barroso, Jesús; Meidt, Sharon E.; Martig, Marie; Shen, Juntai; Li, Zhao-Yu; Yildirim, Akin; Walcher, C. Jakob; Sanchez, Sebastian F.
01/2018 MNRAS 473, 3000

Bianchini, P.; Sills, A.; van de Ven, G.; Sippel, A. C.
08/2017 MNRAS 469, 4359

Zibetti, Stefano; Gallazzi, Anna R.; Ascasibar, Y.; Charlot, S.; Galbany, L.; García Benito, R.; Kehrig, C.; de Lorenzo-Cáceres, A.; Lyubenova, M.; Marino, R. A.; Márquez, I.; Sánchez, S. F.; van de Ven, G.; Walcher, C. J.; Wisotzki, L.
06/2017 MNRAS 468, 1902

de Vita, R.; Trenti, M.; Bianchini, P.; Askar, A.; Giersz, M.; van de Ven, G.
06/2017 MNRAS 467, 4057

Feldmeier-Krause, A.; Zhu, L.; Neumayer, N.; van de Ven, G.; de Zeeuw, P. T.; Schödel, R.
04/2017 MNRAS 466, 4040

Tsatsi, Athanasia; Mastrobuono-Battisti, Alessandra; van de Ven, Glenn; Perets, Hagai B.; Bianchini, Paolo; Neumayer, Nadine
01/2017 MNRAS 464, 3720

Zhu, Ling; van de Ven, Glenn; Watkins, Laura L.; Posti, Lorenzo
11/2016 MNRAS 463, 1117

Bianchini, P.; van de Ven, G.; Norris, M. A.; Schinnerer, E.; Varri, A. L.
06/2016 MNRAS 458, 3644

Bianchini, P.; Norris, M. A.; van de Ven, G.; Schinnerer, E.; Bellini, A.; van der Marel, R. P.; Watkins, L. L.; Anderson, J.
03/2016 ApJ 820, L22

Büdenbender, Alex; van de Ven, Glenn; Watkins, Laura L.
09/2015 MNRAS 452, 956

Polyachenko, E. V.; Just, A.
01/2015 MNRAS 446, 1203

Editor: T. Lisker
zum Seitenanfang/up