MorphDecom

Overview

"MorphDecom" is a morphological decomposition method introduced by Liang, Jiang et al. (2024), building on earlier work by Zana et al. (2022). This innovative method combines threshold-based decomposition for elliptical components with a machine learning approach for disky components. It dynamically adjusts binding energy and circularity thresholds based on the specific galaxy system under analysis. If you find this method helpful to you and want to use it in your research, please cite Liang, Jiang et al. (2024). Please feel free to contact the authors Jinning Liang (jinning.liang@durham.ac.uk) and Fangzhou Jiang (fangzhou.jiang@pku.edu.cn), if you have any question.

Installation

Clone the repository using the following command:

git clone https://github.com/JinningLianggithub/MorphDecom.git

Modules

`decomposition.py`

Functions to use data from loaded simulation to calculate dynamical quantities decomposition.get_kinematics
Find binding energy decomposition.get_Ecut
Find circularity thresholds decomposition.get_etacut
Decompose galaxies into their constituent components decomposition.assign_label
Plot the face-on and edge-on image for different components decomposition.image_plot

`config.py`

Contains global variables and user controls including galaxy (Subfind) ID ID, snapshot of the simulation snap, hubble constant "little h" h, redshift of the simulation z, scale factor of the simulation c, total matter density of the simulation at redshift 0 Om0, baryonic matter density of the simulation at redshift 0 Omb, dark energy desity of the simulation at redshift 0 OmL, dark matter mass of the simulation as unit of 1e10Msun/h DMmass, and gravitational solfening length of the simulation at this redshift epsilon

`snap_z_a.txt`

Provides a lookup table for matching snapshots to redshift and scale factor in TNG50. This is only for converting redshift and scale factor from snapshot. One can use the table from other simulation or just input the redshift and scale factor manually

Dependencies

Install the following libraries and packages to ensure the software functions correctly:

numpy
scida
scipy
pytreegrav
intersect
scikit-learn
matplotlib

Usage Guide

First, one needs to download the particle data and obtain the information for the host subhalo. Here we assume that we already get the data and information. Full example including download TNG50 data is provided in example.ipynb.

To load the particle data, one can simply use h5py to read hdf5 file. Here we use powerful tool scida to load the data. And we construct two dictionaries saving subhalo information and particle data. For subhalo dictionary, it contains its position, velocity, half-stellar-mass radius, the number of gas, dark matter and stellar particles. For particle disctionary, it contains the coordinates, velocities, masses for gas, dark matter and stellar particles. One can also include the formation time for stellar particles to exlucde wind particles. All units must be physical rather than comoving. Here, we take kpc for length unit, kpc/Gyr for velocity unit and Msun for mass unit. This could be done by scida.

After constructing the subhalo and particle dictionary, one can calculate dynamical quantities using decomposition.get_kinematics. These quantities can be further used in finding energy and circularity threshold in decomposition.get_Ecut and decomposition.get_etacut.

Finally, one can use dynamical quantities and two thresholds to do decomposition and obtain a decomposition mask by decomposition.assign_label

Decomposition image

Using the plotting function decomposition.image_plot, one can generate the face-on and edge-on images of different components in this example galaxy

More usage

After doing decomposition for all stellar particles, one gets a decomposition mask for them. Next, one can select bulge, halo, thin disc and thick particles can calculate other quantities for them, e.g. chemical abundance, age, etc.

Credit and support

If you use "MorphDecom", please cite the following BibTex entry:

  @ARTICLE{2024arXiv240314749L,
         author = {{Liang}, Jinning and {Jiang}, Fangzhou and {Mo}, Houjun and {Benson}, Andrew and {Dekel}, Avishai and {Tavron}, Noa and {Hopkins}, Philip F. and {Ho}, Luis C.},
          title = "{Connection between galaxy morphology and dark-matter halo structure I: a running threshold for thin discs and size predictors from the dark sector}",
        journal = {arXiv e-prints},
       keywords = {Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics},
           year = 2024,
          month = mar,
            eid = {arXiv:2403.14749},
          pages = {arXiv:2403.14749},
            doi = {10.48550/arXiv.2403.14749},
  archivePrefix = {arXiv},
         eprint = {2403.14749},
   primaryClass = {astro-ph.GA},
         adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240314749L},
        adsnote = {Provided by the SAO/NASA Astrophysics Data System}
  }