Action-based dynamical models of M31-like galaxies

ABSTRACT In this work, we present an action-based dynamical equilibrium model to constrain the phase-space distribution of stars in the stellar halo, present-day dark matter distribution, and the total mass distribution in M31-like galaxies. The model comprises a three-component gravitational potential (stellar bulge, stellar disc, and a dark matter halo), and a double power-law distribution function (DF), $f(\mathbf {J})$, which is a function of actions. A Bayesian model-fitting algorithm was implemented that enabled both parameters of the potential and DF to be explored. After testing the model-fitting algorithm on mock data drawn from the model itself, it was applied to a set of three M31-like haloes from the Auriga simulations (Auriga 21, Auriga 23, and Auriga 24). Furthermore, we tested the equilibrium assumption and the ability of a double power-law DF to represent the stellar halo stars. The model incurs an error in the total enclosed mass of around 10 per cent out to 100 kpc, thus justifying the equilibrium assumption. Furthermore, the double power-law DF used proves to be an appropriate description of the investigated M31-like haloes. The anisotropy profiles of the haloes were also investigated and discussed from a merger history point of view, however, our approach underscores to capture the non-equilibrium anisotropy information of stellar haloes in Auriga models, which is expected.

Authors

Institutions

Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2024-08-15
DOI
https://doi.org/10.1093/mnras/stae1960
Citations
3
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
0.75

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Action-based dynamical models of M31-like galaxies

Matthew D A Orkney, Payel Das, Robert J. J. Grand, Paula Gherghinescu
3 citations
Monthly Notices of the Royal Astronomical Society
Data Visualization and Analytics
0.75
article

Action-based dynamical models of M31-like galaxies

Matthew D A Orkney, Payel Das, Robert J. J. Grand, Paula Gherghinescu
article en
3 citations

Abstract

ABSTRACT In this work, we present an action-based dynamical equilibrium model to constrain the phase-space distribution of stars in the stellar halo, present-day dark matter distribution, and the total mass distribution in M31-like galaxies. The model comprises a three-component gravitational potential (stellar bulge, stellar disc, and a dark matter halo), and a double power-law distribution function (DF), $f(\mathbf {J})$, which is a function of actions. A Bayesian model-fitting algorithm was implemented that enabled both parameters of the potential and DF to be explored. After testing the model-fitting algorithm on mock data drawn from the model itself, it was applied to a set of three M31-like haloes from the Auriga simulations (Auriga 21, Auriga 23, and Auriga 24). Furthermore, we tested the equilibrium assumption and the ability of a double power-law DF to represent the stellar halo stars. The model incurs an error in the total enclosed mass of around 10 per cent out to 100 kpc, thus justifying the equilibrium assumption. Furthermore, the double power-law DF used proves to be an appropriate description of the investigated M31-like haloes. The anisotropy profiles of the haloes were also investigated and discussed from a merger history point of view, however, our approach underscores to capture the non-equilibrium anisotropy information of stellar haloes in Auriga models, which is expected.

Monthly Notices of the Royal Astronomical SocietyVol. 533(4)
University of Surrey (GB), Institut d'Estudis Espacials de Catalunya (ES), Liverpool John Moores University (GB), Universitat de Barcelona (ES)
UK Research and Innovation, Science and Technology Facilities Council, H2020 European Research Council
Peace, Justice and strong institutions
Openalex Percentile: Top 28%
Data Visualization and Analytics
0.75
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.

Action-based dynamical models of M31-like galaxies — Matthew D A Orkney, Payel Das, et al. · Monthly Notices of the Royal Astronomical Society (2024) | TGRS Research Map | TGRS