Apside 0.4

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Apside — Galactic Potential + Leapfrog Orbit Integrator (High-Precision Python)

Apside is a Python package for integrating test-particle orbits in a Milky-Way–like gravitational field using a symplectic leapfrog integrator. It provides:

• Analytic accelerations for a 3-component Galaxy model (bulge + disk + halo)
• Matching analytic potentials and specific energy
• A leapfrog stepper that imports and uses acceleration.py
• Optional high-precision arithmetic via Python’s Decimal

Units and conventions

• Position: kpc
• Velocity: km/s
• Mass: Msun
• Time: kpc / (km/s)
• Specific energy: (km/s)^2

Gravitational constant:

$$ G = 4.3009 \times 10^{-6}\ \mathrm{kpc},(\mathrm{km/s})^2,M_\odot^{-1} $$

Let position be $\mathbf{r}=(x,y,z)$ and radius $r=|\mathbf{r}|=\sqrt{x^2+y^2+z^2}$.

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1) Potentials and accelerations (what the code computes)

Apside models the Galaxy as:

$$ \Phi(\mathbf{r}) = \Phi_{\mathrm{bulge}}(\mathbf{r}) + \Phi_{\mathrm{disk}}(\mathbf{r}) + \Phi_{\mathrm{halo}}(\mathbf{r}) $$

The acceleration is defined by Newtonian gravity as the negative gradient of the potential:

$$ \mathbf{a}(\mathbf{r}) = -\nabla \Phi(\mathbf{r}) $$

So the total acceleration returned by acceleration(x,y,z) is:

$$ \mathbf{a}(\mathbf{r}) = \mathbf{a}{\mathrm{bulge}}(\mathbf{r}) + \mathbf{a}{\mathrm{disk}}(\mathbf{r}) + \mathbf{a}_{\mathrm{halo}}(\mathbf{r}) $$

The code implements each component separately and sums them.

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2) Bulge: Hernquist model

Potential

A spherical Hernquist bulge has specific potential:

$$ \Phi_{\mathrm{bulge}}(r) = -\frac{G M_b}{r + a_b} $$

This is what phiBulge(x,y,z) computes (with $r=\sqrt{x^2+y^2+z^2}$).

Derivation of acceleration from the potential

For any spherical potential $\Phi(r)$, the gradient is:

$$ \nabla \Phi(r) = \frac{d\Phi}{dr},\frac{\mathbf{r}}{r} $$

Therefore the acceleration is:

$$ \mathbf{a}(r) = -\nabla \Phi(r) = -\frac{d\Phi}{dr},\frac{\mathbf{r}}{r} $$

Compute derivative:

$$ \Phi_{\mathrm{bulge}}(r) = -G M_b (r+a_b)^{-1} $$

$$ \frac{d\Phi_{\mathrm{bulge}}}{dr} = -G M_b \cdot \left(-1\right)(r+a_b)^{-2} = \frac{G M_b}{(r+a_b)^2} $$

So:

$$ \mathbf{a}_{\mathrm{bulge}}(r) = -\frac{G M_b}{(r+a_b)^2}\frac{\mathbf{r}}{r} $$

Component form:

$$ a_x = -\frac{G M_b}{(r+a_b)^2}\frac{x}{r},\quad a_y = -\frac{G M_b}{(r+a_b)^2}\frac{y}{r},\quad a_z = -\frac{G M_b}{(r+a_b)^2}\frac{z}{r} $$

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3) Disk: Miyamoto–Nagai model

Define cylindrical radius and vertical helper:

$$ R = \sqrt{x^2+y^2} $$

$$ B = \sqrt{z^2 + b_d^2} $$

$$ D = \sqrt{R^2 + (a_d + B)^2} $$

Potential

$$ \Phi_{\mathrm{disk}}(x,y,z) = -\frac{G M_d}{D} $$

This is what phiDisk(x,y,z) computes.

Derivation of acceleration from the potential

Start from:

$$ \Phi_{\mathrm{disk}} = -G M_d D^{-1} $$

The acceleration components are:

$$ a_x = -\frac{\partial \Phi}{\partial x},\quad a_y = -\frac{\partial \Phi}{\partial y},\quad a_z = -\frac{\partial \Phi}{\partial z} $$

Use chain rule:

$$ \frac{\partial \Phi}{\partial x} = -G M_d \frac{\partial}{\partial x}(D^{-1}) = -G M_d (-1) D^{-2}\frac{\partial D}{\partial x} = \frac{G M_d}{D^2}\frac{\partial D}{\partial x} $$

So:

$$ a_x = -\frac{\partial \Phi}{\partial x} = -\frac{G M_d}{D^2}\frac{\partial D}{\partial x} $$

Now compute derivatives of $D=\sqrt{R^2+(a_d+B)^2}$.

Since $D = (R^2+(a_d+B)^2)^{1/2}$:

$$ \frac{\partial D}{\partial x} = \frac{1}{2} (R^2+(a_d+B)^2)^{-1/2} \cdot 2R\frac{\partial R}{\partial x} = \frac{R}{D}\frac{\partial R}{\partial x} $$

And $R=\sqrt{x^2+y^2}$ gives:

$$ \frac{\partial R}{\partial x} = \frac{x}{R} $$

Therefore:

$$ \frac{\partial D}{\partial x} = \frac{R}{D}\cdot \frac{x}{R} = \frac{x}{D} $$

Plug into $a_x$:

$$ a_x = -\frac{G M_d}{D^2}\cdot \frac{x}{D} = -\frac{G M_d}{D^3}x $$

Similarly:

$$ a_y = -\frac{G M_d}{D^3}y $$

For $z$, $D$ depends on $z$ through $B$:

$$ \frac{\partial D}{\partial z} = \frac{1}{2}(R^2+(a_d+B)^2)^{-1/2}\cdot 2(a_d+B)\frac{\partial B}{\partial z} = \frac{(a_d+B)}{D}\frac{\partial B}{\partial z} $$

And $B=\sqrt{z^2+b_d^2}$ implies:

$$ \frac{\partial B}{\partial z} = \frac{z}{B} $$

So:

$$ \frac{\partial D}{\partial z} = \frac{(a_d+B)}{D}\cdot \frac{z}{B} $$

Then:

$$ a_z = -\frac{G M_d}{D^2}\frac{\partial D}{\partial z} = -\frac{G M_d}{D^2}\cdot \frac{(a_d+B)}{D}\cdot \frac{z}{B} = -\frac{G M_d}{D^3}\frac{(a_d+B)z}{B} $$

These are the forms implemented in diskAccel.

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4) Halo: NFW model

Potential

$$ \Phi_{\mathrm{halo}}(r) = -\frac{G M_h}{r}\ln!\left(1+\frac{r}{r_s}\right) $$

This is what phiHalo(x,y,z) computes (with a small epsilon floor on $r$).

Derivation of acceleration from the potential

Again use spherical-gradient identity:

$$ \mathbf{a}{\mathrm{halo}}(r) = -\frac{d\Phi{\mathrm{halo}}}{dr}\frac{\mathbf{r}}{r} $$

Let:

$$ \Phi_{\mathrm{halo}}(r) = -G M_h , f(r), \quad f(r)=\frac{1}{r}\ln!\left(1+\frac{r}{r_s}\right) $$

Compute $f'(r)$ using product rule with $f(r)=\ln(1+r/r_s)\cdot r^{-1}$:

$$ f'(r) = \left(\frac{1}{1+r/r_s}\cdot \frac{1}{r_s}\right)\cdot r^{-1} + \ln!\left(1+\frac{r}{r_s}\right)\cdot (-r^{-2}) $$

Simplify the first term:

$$ \frac{1}{1+r/r_s}\cdot \frac{1}{r_s} = \frac{1}{r+r_s} $$

So:

$$ f'(r) = \frac{1}{r(r+r_s)} - \frac{1}{r^2}\ln!\left(1+\frac{r}{r_s}\right) $$

Then:

$$ \frac{d\Phi_{\mathrm{halo}}}{dr} = -G M_h f'(r) $$

So:

$$ \mathbf{a}_{\mathrm{halo}}(r) = -\left(-G M_h f'(r)\right)\frac{\mathbf{r}}{r} = G M_h f'(r)\frac{\mathbf{r}}{r} $$

Factor into the common form used in the code:

$$ \mathbf{a}_{\mathrm{halo}}(r)

-\frac{G M_h}{r^3} \left[ \ln!\left(1+\frac{r}{r_s}\right) - \frac{r}{r+r_s} \right]\mathbf{r} $$

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5) Energy

The code defines the specific energy (energy per unit mass):

$$ E(\mathbf{x},\mathbf{v}) = T(\mathbf{v}) + \Phi(\mathbf{x}) $$

Kinetic energy

For a unit-mass particle:

$$ T(\mathbf{v}) = \frac{1}{2}|\mathbf{v}|^2 = \frac{1}{2}(v_x^2+v_y^2+v_z^2) $$

This is what totalEnergy computes as kinetic = 0.5 * v2.

Potential energy

The gravitational potential (specific) is additive across components:

Conservation of Energy

If $\Phi$ is time-independent, then along the true solution of:

$$ \dot{\mathbf{x}}=\mathbf{v},\quad \dot{\mathbf{v}}=\mathbf{a}(\mathbf{x})=-\nabla\Phi(\mathbf{x}) $$

the derivative of $E$ is:

$$ \frac{dE}{dt}

\frac{d}{dt}\left(\frac{1}{2}\mathbf{v}\cdot\mathbf{v} + \Phi(\mathbf{x})\right)

\mathbf{v}\cdot\dot{\mathbf{v}} + \nabla\Phi\cdot\dot{\mathbf{x}} $$

Substitute $\dot{\mathbf{v}}=-\nabla\Phi$ and $\dot{\mathbf{x}}=\mathbf{v}$:

$$ \frac{dE}{dt}

\mathbf{v}\cdot(-\nabla\Phi) + \nabla\Phi\cdot\mathbf{v} = 0 $$

So energy is conserved analytically. Numerically, leapfrog is symplectic, so energy typically oscillates with small bounded error rather than drifting rapidly.

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6) Leapfrog integrator

Leapfrog is a second-order symplectic scheme for Hamiltonian systems.

Given state $(\mathbf{x}_n,\mathbf{v}_n)$ and timestep $\Delta t$:

Half-kick:

$$ \mathbf{v}_{n+\tfrac{1}{2}} = \mathbf{v}_n + \frac{\Delta t}{2}\mathbf{a}(\mathbf{x}_n) $$

Drift:

$$ \mathbf{x}_{n+1} = \mathbf{x}n + \Delta t,\mathbf{v}{n+\tfrac{1}{2}} $$

Half-kick:

$$ \mathbf{v}{n+1} = \mathbf{v}{n+\tfrac{1}{2}} + \frac{\Delta t}{2}\mathbf{a}(\mathbf{x}_{n+1}) $$

Functions map this one to one:

• halfVelocity computes $\mathbf{v}_{n+1/2}$
• nextPos computes $\mathbf{x}_{n+1}$
• nextVel computes $\mathbf{v}_{n+1}$
• leapfrogStep executes the sequence and returns the updated state

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Examples

Compute acceleration

from apside.acceleration import acceleration

a = acceleration("8.0", "0.0", "0.0")
print(a)

Compute energy

from apside.energy import totalEnergy

E = totalEnergy("8.0", "0.0", "0.0", "0.0", "220.0", "0.0")
print(E)

Integrate one timestep

from apside.leapfrog import leapfrogStep

x,y,z = "8.0","0.0","0.0"
vx,vy,vz = "0.0","220.0","0.0"
dt = "0.001"

x,y,z,vx,vy,vz = leapfrogStep(x,y,z,vx,vy,vz,dt)
print(x,y,z,vx,vy,vz)

Energy conservation test

from apside.leapfrog import leapfrogStep
from apside.energy import totalEnergy

x,y,z = "8.0","0.0","0.0"
vx,vy,vz = "0.0","220.0","0.0"
dt = "0.001"

E0 = totalEnergy(x,y,z,vx,vy,vz)

for _ in range(10000):
    x,y,z,vx,vy,vz = leapfrogStep(x,y,z,vx,vy,vz,dt)

E1 = totalEnergy(x,y,z,vx,vy,vz)
print("DeltaE =", E1 - E0)

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Author

Created by Venkata Siddharth Pendyala
December 21st 2025