@article{6d0423ecc0864667acd4e442884c2c83,
title = "Weak lensing reveals a tight connection between dark matter halo mass and the distribution of stellar mass in massive galaxies",
abstract = "Using deep images from the Hyper Suprime-Cam (HSC) survey and taking advantage of its unprecedented weak lensing capabilities, we reveal a remarkably tight connection between the stellar mass distribution of massive central galaxies and their host dark matter halo mass. Massive galaxies with more extended stellar mass distributions tend to live in more massive dark matter haloes.We explain this connection with a phenomenological model that assumes, (1) a tight relation between the halo mass and the total stellar content in the halo, (2) that the fraction of in situ and ex situ mass at r <10 kpc depends on halo mass. This model provides an excellent description of the stellar mass functions (SMFs) of total stellar mass (Mmax∗) and stellar mass within inner 10 kpc (M10∗) and also reproduces the HSC weak lensing signals of massive galaxies with different stellar mass distributions. The best-fitting model shows that halo mass varies significantly at fixed total stellar mass (as much as 0.4 dex) with a clear dependence on M10∗. Our two-parameter Mmax∗-M10∗description provides a more accurate picture of the galaxy-halo connection at the high-mass end than the simple stellar-halo mass relation (SHMR) and opens a new window to connect the assembly history of haloes with those of central galaxies. The model also predicts that the ex situ component dominates the mass profiles of galaxies at r < 10 kpc for logM ≥11.7.",
keywords = "Elliptical and lenticular, cD-galaxies, Formation, Galaxies, Galaxies, Galaxies, Galaxies, Haloes, Photometry, Structure",
author = "Song Huang and Alexie Leauthaud and Andrew Hearin and Peter Behroozi and Christopher Bradshaw and Felipe Ardila and Joshua Speagle and Ananth Tenneti and Kevin Bundy and Jenny Greene and Cristoacute;bal Sif{\'o}n and Neta Bahcall",
note = "Funding Information: The Hyper Suprime-Cam (HSC) collaboration includes the astronomical communities of Japan and Taiwan, and Princeton University. The HSC instrumentation and software were developed by National Astronomical Observatory of Japan (NAOJ), Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), University of Tokyo, High Energy Accelerator Research Organization (KEK), Academia Sinica Institute for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University. Funding was contributed by the FIRST program from Japanese Cabinet Office, Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS), Japan Science and Technology Agency (JST), Toray Science Foundation, NAOJ, Kavli IPMU, KEK, ASIAA, and Princeton University. Funding for Sloan Digital Sky Survey-III (SDSS-III) has been provided by Alfred P. Sloan Foundation, the Participating Institutions, National Science Foundation, and U.S. Department of Energy. The SDSS-III website is http://ww w.sdss3.org. SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration, including University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, University of Cambridge, University of Florida, the French Participation Group, the German Participation Group, Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University. The Pan-STARRS1 surveys (PS1) have been made possible through contributions of Institute for Astronomy; University of Hawaii; the Pan-STARRS Project Office; the Max-Planck Society and its participating institutes: the Max Planck Institute for Astronomy, Heidelberg, and the Max Planck Institute for Extraterrestrial Physics, Garching; Johns Hopkins University; Durham University; University of Edinburgh; Queen{\textquoteright}s University Belfast; Harvard-Smithsonian Center for Astrophysics; Las Cumbres Observatory Global Telescope Network Incorporated; National Central University of Taiwan; Space Telescope Science Institute; National Aeronautics and Space Administration under Grant No. NNX08AR22G issued through the Planetary Science Division of the National Aeronautics and Space Administration (NASA) Science Mission Directorate; National Science Foundation under Grant No. AST-1238877; University of Maryland, and Eotvos Lorand University. This research makes use of software developed for the Large Synoptic Survey Telescope. We thank the LSST project for making their code available as free software at http: //dm.lsstcorp.org. The CosmoSim database used in this research is a service by the Leibniz-Institute for Astrophysics Potsdam (AIP). The MultiDark database was developed in cooperation with the Spanish MultiDark Consolider Project CSD2009-00064. This research made use of: STSCI PYTHON, a general astronomical data analysis infrastructure in Python. STSCI PYTHON is a product of the Space Telescope Science Institute, which is operated for NASA by Association of Universities for Research in Astronomy (AURA); SciPy, an open source scientific tool for Python (Jones et al. 2001); NumPy, a fundamental package for scientific computing with Python (Walt, Colbert & Varoquaux 2011); Matplotlib, a 2D plotting library for Python (Hunter 2007); Astropy, a community-developed core Python package for astronomy (Astropy Collaboration et al. 2013); scikit-learn, a machine-learning library in Python (Pedregosa et al. 2011); IPython, an interactive computing system for Python (P{\'e}rez & Granger 2007); sep Source Extraction and Photometry in Python (Barbary et al. 2015); palettable, colour palettes for Python; emcee, Seriously Kick-Ass MCMC in Python; Colossus, COsmology, haLO and large-Scale StrUcture toolS (Diemer 2015). Funding Information: The authors would like to thank Frank van den Bosch, Sandra Faber, Joel Primack for useful discussions and suggestions. This material is based upon work supported by the National Science Foundation under Grant No. 1714610. The authors acknowledge support from the Kavli Institute for Theoretical Physics. This research was also supported in part by National Science Foundation under Grant No. NSF PHY11-25915 and Grant No. NSF PHY17-48958. AL acknowledges support from the David and Lucille Packard foundation, and from the Alfred P Sloan foundation. Publisher Copyright: {\textcopyright} 2019 The Author(s).",
year = "2020",
month = mar,
day = "1",
doi = "10.1093/mnras/stz3314",
language = "English (US)",
pages = "3685--3707",
journal = "Monthly Notices of the Royal Astronomical Society",
issn = "0035-8711",
publisher = "Oxford University Press",
number = "3",
}