jeans
A package for calculating properties of (spherical) dark matter halos and embedded (spherical) stellar populations, including integration of the (spherical) Jeans equation in 2D (observed projections) and 3D.
Author: Matthew G. Walker (2024)
Instructions
- Install jeans. You can either pip install the released version or install from github
pip install jeans
Available Dark Matter Halo Models
The alpha/beta/gamma ('abg_triangle') halo has mass density profile $\rho(r)=\frac{\rho_s}{(r/r_s)^{\gamma}[1+(r/r_s)^{\alpha}]^{(\beta-\gamma)/\alpha}}$.
The Navarro-Frenk-White ('nfw') halo is a special case of the above, with $(\alpha,\beta,\gamma)=(1,3,1)$, but can be called directly.
The Dehnen Cusp ('dehnen_cusp') halo is a special case of the 'abg' halo, with $(\alpha,\beta,\gamma)=(1,4,1)$, but can be called directly.
The Dehnen Core ('dehnen_core') halo is a special case of the 'abg' halo, with $(\alpha,\beta,\gamma)=(1,4,0)$, but can be called directly.
The core-NFW ('cnfw') halo is by Read et al. (arXiv:1805.06934), defined in terms of the enclosed mass profile, $M_{\rm cNFW}(r)=M_{\rm NFW}(r)f^n$, where $M_{\rm NFW}(r)$ is the enclosed mass profile of the NFW halo and $f^n=[\tanh(r/r_c)]^n$, with $r_c$ a core radius.
The core-NFW-tides ('cnfwt') halo is by Read et al. (arXiv:1805.06934), with density profile $\rho_{\rm cNFWt}(r)=\rho_{\rm cNFW}(r)$ for $r<r_{\rm t}$ and $\rho_{\rm cNFWt}(r)=\rho_{\rm cNFW}(r_{\rm t})(r/r_{\rm t})^{-\delta}$, allowing for power-law decrease in density beyond `tidal' radius $r_{\rm t}$.
For cNFW and cNFWt models, the standard definitions of parameters $M_{\triangle}$, $c_{\triangle}$ and $r_{\triangle}$ apply to the density and mass profile of the corresponding NFW halo that would be obtained by setting $r_{\rm c}=0$ and $r_{\rm t}=\infty$.
Available Models for Tracer component
The alpha/beta/gamma ('abg') model has number density profile $\nu(r)=\frac{\nu_0}{(r/r_s)^{\gamma}[1+(r/r_s)^{\alpha}]^{(\beta-\gamma)/\alpha}}$.
The Plummer model ('plum') is a special case of the 'abg' model, with $(\alpha,\beta,\gamma)=(2,5,0)$, but can be called directly.
The 'a2bg' model is a special case of the 'abg' model, with $\alpha=2$, but can be called directly.
The exponential model 'exp' is defined in terms of projected density, $\Sigma(R)=\Sigma_0\exp(-R/r_s)$.
Available Models for velocity dispersion anisotropy of the tracer component
The only model currently implemented is that of Read et al. (arXiv:1805.06934): $\beta(r)\equiv 1-\sigma^2_{\rm t}/\sigma^2_{\rm r}=\beta_0+(\beta_{\infty}-\beta_0)/(1+(r/r_{\beta})^{-n})$, where $\sigma_{\rm r}$ is the radial component of the velocity dispersion and $\sigma_{\rm t}=\sigma_{\theta}=\sigma_{\phi}$ is the tangential component (the two angular components have equal magnitude in the absence of rotation).
Usage
See notebook in the examples folder.
Examples
See notebook in the examples folder.
Acknowledgement
Release files for jeans 2.3.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| jeans-2.3.4.tar.gz | 13.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jeans-2.3.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 26.4 kB
Release files / jeans-2.3.4.tar.gz
| Download URL | jeans-2.3.4.tar.gz |
|---|---|
| Size | 13.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c0b9015b8cacbe1fbf1c39f1e87c5fa8c7c08a36c62704b00dd011b99619d1a7
|
|
BLAKE2b-256 checksum How to use checksums |
0c7fd56f3449cc9c54a15431fee215ce2a16d47b1a8bf366a2bdeb03639a850b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.8
|
Release files / jeans-2.3.4-py3-none-any.whl
| Download URL | jeans-2.3.4-py3-none-any.whl |
|---|---|
| Size | 12.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
e0f5b5f1dc0e37ee240cbb870c93da15377f5bee076136a97061727d6a099ee4
|
|
BLAKE2b-256 checksum How to use checksums |
f13c2a20bdae90d37e4c35e03e0fabf15fd8e2b0e09073573165f7c441617924
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.8
|