Dark Matter Distribution in Milky Way analog Galaxies

Abstract Our current understanding of how dark matter (DM) is distributed within the Milky Way (MW) halo, particularly in the solar neighborhood, is based on either careful studies of the local stellar orbits, model assumptions on the global shape of the MW halo, or from direct acceleration measurements. In this work, we undertake a study of external galaxies, with the intent of providing insight into the DM distribution in MW-analog galaxies. For this, we carefully select a sample of galaxies similar to the MW, based on maximum atomic hydrogen (H i ) rotational velocity ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>v</mml:mi> <mml:mrow> <mml:mo>max</mml:mo> <mml:mo>,</mml:mo> <mml:mi mathvariant="normal">H</mml:mi> <mml:mspace width="0.25em"/> <mml:mi mathvariant="normal">I</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>200</mml:mn> <mml:mo>–</mml:mo> <mml:mn>280</mml:mn> </mml:math> km s −1 ) and morphological type (Sab–Sbc) criteria. With a need for deep, highly resolved H i , our resulting sample is composed of five galaxies from the VLA Imaging of Virgo in Atomic Gas (VIVA) survey and The H i Nearby Galaxy Survey (THINGS). To perform our baryonic analysis, we use deep mid-IR images at 3.6 and 4.5 μ m from the Spitzer Survey of Stellar Structures in Galaxies (S 4 G). Based on the dynamical three-dimensional modeling software 3D-Based Analysis of Rotating Objects via Line Observations (3D- BAROLO ), we construct rotation curves (RCs) and derive the gas and stellar contributions from the galaxy's gaseous and stellar disks' mass surface density profiles. Through a careful decomposition of their RCs into their baryonic (stars, gas) and DM components, we isolate the DM contribution by using a Markov Chain Monte Carlo-based approach. Based on the Sun's location and the MW’s R 25 , we define the corresponding location of the solar neighborhood in these systems. We put forward a window for the DM density ( ρ dm = 0.21–0.55 GeV cm −3 ) at these galactocentric distances in our MW analog sample, consistent with the values found for the MW’s local DM density, based on more traditional approaches found in the literature.

Authors

Institutions

Publication Details

Journal
The Astrophysical Journal
Published
2024-08-01
DOI
https://doi.org/10.3847/1538-4357/ad53c8
Citations
4
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
1.33
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dark Matter Distribution in Milky Way analog Galaxies

N. G. de Isídio, A. Cortesi, Thiago S. Gonçalves, Davi C. Rodrigues et al.
4 citations
The Astrophysical Journal
Galaxies: Formation, Evolution, Phenomena
1.33
article

Dark Matter Distribution in Milky Way analog Galaxies

N. G. de Isídio, A. Cortesi, Thiago S. Gonçalves, Davi C. Rodrigues, Pedro P. B. Beaklini, T. H. Randriamampandry, Karín Menéndez‐Delmestre, Luis Henry Quiroga-Nuñez, Nicolás Garavito-Camargo, M. Grossi, Y. Cavalcante-Coelho, Amanda E. Araujo-Carvalho
article en
4 citations

Abstract

Abstract Our current understanding of how dark matter (DM) is distributed within the Milky Way (MW) halo, particularly in the solar neighborhood, is based on either careful studies of the local stellar orbits, model assumptions on the global shape of the MW halo, or from direct acceleration measurements. In this work, we undertake a study of external galaxies, with the intent of providing insight into the DM distribution in MW-analog galaxies. For this, we carefully select a sample of galaxies similar to the MW, based on maximum atomic hydrogen (H i ) rotational velocity ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>v</mml:mi> <mml:mrow> <mml:mo>max</mml:mo> <mml:mo>,</mml:mo> <mml:mi mathvariant="normal">H</mml:mi> <mml:mspace width="0.25em"/> <mml:mi mathvariant="normal">I</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>200</mml:mn> <mml:mo>–</mml:mo> <mml:mn>280</mml:mn> </mml:math> km s −1 ) and morphological type (Sab–Sbc) criteria. With a need for deep, highly resolved H i , our resulting sample is composed of five galaxies from the VLA Imaging of Virgo in Atomic Gas (VIVA) survey and The H i Nearby Galaxy Survey (THINGS). To perform our baryonic analysis, we use deep mid-IR images at 3.6 and 4.5 μ m from the Spitzer Survey of Stellar Structures in Galaxies (S 4 G). Based on the dynamical three-dimensional modeling software 3D-Based Analysis of Rotating Objects via Line Observations (3D- BAROLO ), we construct rotation curves (RCs) and derive the gas and stellar contributions from the galaxy's gaseous and stellar disks' mass surface density profiles. Through a careful decomposition of their RCs into their baryonic (stars, gas) and DM components, we isolate the DM contribution by using a Markov Chain Monte Carlo-based approach. Based on the Sun's location and the MW’s R 25 , we define the corresponding location of the solar neighborhood in these systems. We put forward a window for the DM density ( ρ dm = 0.21–0.55 GeV cm −3 ) at these galactocentric distances in our MW analog sample, consistent with the values found for the MW’s local DM density, based on more traditional approaches found in the literature.

The Astrophysical JournalVol. 971(1)
Florida Institute of Technology (US), Universidade Federal do Rio de Janeiro (BR), Heidelberg University (DE), National Radio Astronomy Observatory (US), Valongo Observatory (BR), Flatiron Health (United States) (US), Flatiron Institute (US), Universidade Federal do Espírito Santo (BR), Heidelberg Institute for Theoretical Studies (DE), University of the Western Cape (ZA)
Openalex Percentile: Top 20%
Galaxies: Formation, Evolution, Phenomena
1.33
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.