In the first paper of the MaNGA DynPop series, authors Kai Zhu, Shengdong Lu, Michele Cappellari, Ran Li, Shude Mao, and Liang Gao present a comprehensive analysis of the Dynamics and stellar Population (DynPop) for the MaNGA survey within SDSS Data Release 17. The study focuses on over 10,000 nearby galaxies and utilizes integral-field stellar kinematics from the MaNGA survey to extract dynamical properties using the axisymmetric Jeans Anisotropic Modelling (JAM) method. This method is known for its accuracy in this type of analysis. The authors assess systematic uncertainties by employing eight different dynamical models with varying assumptions. They consider two orientations of the velocity ellipsoid - cylindrically-aligned JAM$_{\rm cyl}$ or spherically-aligned JAM$_{\rm sph}$ - and four assumptions regarding dark vs. luminous matter distributions: mass-follows-light, free NFW dark halo, cosmologically-constrained NFW halo, and generalized NFW dark halo with a free inner slope. The catalogue provided includes quantities related to mass distributions such as density slopes and enclosed mass within specified radii for total mass, stellar mass, and dark matter components. Additionally, complete models are offered to calculate the full luminous and mass distribution of each galaxy. Visual assessments of model qualities aid in model selections. The authors also estimate observed scatter in measured quantities which decreases with improved data quality. Notably, for the highest quality data, there is remarkable consistency in measured quantities across different models, underscoring the robustness of the results. The paper spans 30 pages and includes 15 figures and 4 tables. The catalogue will be available at https://manga-dynpop.github.io/ upon publication in MNRAS (doi: 10.1093/mnras/stad1299).
- Authors present a comprehensive analysis of Dynamics and stellar Population (DynPop) for MaNGA survey within SDSS Data Release 17
- Study focuses on over 10,000 nearby galaxies using integral-field stellar kinematics from MaNGA survey
- Utilizes axisymmetric Jeans Anisotropic Modelling (JAM) method known for accuracy in dynamical properties extraction
- Employed eight different dynamical models with varying assumptions to assess systematic uncertainties
- Catalogue includes quantities related to mass distributions such as density slopes and enclosed mass within specified radii for total mass, stellar mass, and dark matter components
- Complete models offered to calculate full luminous and mass distribution of each galaxy
- Visual assessments of model qualities aid in model selections; observed scatter in measured quantities decreases with improved data quality
- Remarkable consistency in measured quantities across different models for highest quality data underscores robustness of results
SummaryAuthors studied how galaxies move and what they are made of using a special survey called MaNGA. They looked at 10,000 nearby galaxies to learn more about them. They used a method called JAM to figure out how the galaxies move accurately. By trying different models, they checked for any mistakes in their results. The study included information about how mass is spread out in galaxies and offered ways to calculate this.
Definitions
- Dynamics: The way things move or change over time.
- Stellar Population: Groups of stars that are similar in age and composition.
- Integral-field stellar kinematics: Studying how stars move within a galaxy using detailed observations.
- Axisymmetric: Having symmetry around an axis, like a spinning top.
- Anisotropic: Not the same in all directions.
- Modelling: Creating representations or simulations of real-world processes.
- Systematic uncertainties: Potential errors or biases that affect all data points in a consistent way.
- Catalogue: A list or collection of items with specific details.
- Density slopes: How quickly density changes with distance from the center of an object.
- Enclosed mass: The total amount of mass contained within a specified region.
- Luminous distribution: How light is spread out within an object.
Introduction:
The MaNGA DynPop series is a collection of papers that aim to provide a comprehensive analysis of the Dynamics and stellar Population (DynPop) for the MaNGA survey within SDSS Data Release 17. The first paper in this series, authored by Kai Zhu, Shengdong Lu, Michele Cappellari, Ran Li, Shude Mao, and Liang Gao, presents an in-depth study on over 10,000 nearby galaxies using integral-field stellar kinematics from the MaNGA survey.
Background:
The MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) survey is one of the key programs of the Sloan Digital Sky Survey IV (SDSS-IV). It aims to map the internal structure and dynamics of nearby galaxies using integral field spectroscopy. This technique allows for simultaneous measurements of both spatially resolved spectra and kinematic information across a large area of each galaxy.
Methodology:
In this study, the authors utilize the axisymmetric Jeans Anisotropic Modelling (JAM) method to extract dynamical properties from the MaNGA data. This method has been proven to be highly accurate in similar analyses. To assess systematic uncertainties in their results, eight different dynamical models with varying assumptions are employed.
These assumptions include two orientations of velocity ellipsoid - cylindrically-aligned JAM$_{\rm cyl}$ or spherically-aligned JAM$_{\rm sph}$ - as well as four different scenarios regarding dark matter distribution: mass-follows-light model where dark matter follows luminous matter distribution; free NFW dark halo model where dark matter follows a Navarro-Frenk-White profile; cosmologically-constrained NFW halo model where parameters are constrained by cosmological simulations; and generalized NFW dark halo model with a free inner slope.
Results:
The catalogue provided by this study includes quantities related to mass distributions such as density slopes and enclosed mass within specified radii for total mass, stellar mass, and dark matter components. Additionally, complete models are offered to calculate the full luminous and mass distribution of each galaxy. Visual assessments of model qualities aid in selecting the most appropriate model for each galaxy.
The authors also estimate observed scatter in measured quantities which decreases with improved data quality. Notably, for the highest quality data, there is remarkable consistency in measured quantities across different models, underscoring the robustness of the results.
Conclusion:
In conclusion, this paper presents a comprehensive analysis of Dynamics and stellar Population (DynPop) for over 10,000 nearby galaxies using integral-field stellar kinematics from the MaNGA survey. The use of multiple dynamical models with varying assumptions allows for a thorough assessment of systematic uncertainties in the results. The catalogue provided by this study will be valuable for future studies on galaxy dynamics and evolution.
Future Work:
This study provides a solid foundation for further research on nearby galaxies using MaNGA data. Future work could include expanding this analysis to larger samples or focusing on specific types of galaxies such as spiral or elliptical galaxies. Additionally, incorporating other observational techniques such as gas kinematics could provide even more insights into galaxy dynamics.
Availability:
The catalogue provided by this study will be publicly available at https://manga-dynpop.github.io/ upon publication in MNRAS (doi: 10.1093/mnras/stad1299). This will allow other researchers to access and utilize these valuable data sets for their own studies.
Overall Impact:
This paper adds significant value to our understanding of galaxy dynamics and evolution by providing a comprehensive analysis using state-of-the-art techniques on a large sample size. The results presented here will serve as an important reference point for future studies on nearby galaxies and their internal structures.
References:
Zhu K., Lu S., Cappellari M., Li R., Mao S., Gao L., 2021, MNRAS, stad1299. doi: 10.1093/mnras/stad1299
Manga-DynPop Catalogue. Available at: https://manga-dynpop.github.io/
