DEV Community

masoomjethwa
masoomjethwa

Posted on

A Chemical Brew on Mars: The Multi-Species Ionosphere

#algorithms #science #tutorial

Hello, future astronauts and fellow cosmic explorers! We've previously built a simple model of the Martian ionosphere, but as any chemist will tell you, the Red Planet's atmosphere is far more complex than a single gas. It's a subtle mix, a delicate chemical brew of carbon dioxide, nitrogen, argon, and a few other trace gases that have a powerful effect on how the ionosphere behaves.

Today, we're going to upgrade our algorithm to reflect this reality, drawing inspiration once again from the foundational work of Professor R.P. Singhal. His Analytical Yield Spectrum (AYS) method, as detailed in his book Elements of Space Physics (2022), provides the perfect framework for this kind of detailed analysis. We’ll show how each gas contributes its own unique flavour to the ionisation process, making our model far more accurate.

The Recipe: A Multi-Species Approach

The key to this upgrade is to stop treating the Martian atmosphere as a single entity. Instead, we'll model each of the major gases—carbon dioxide ($CO_2$), nitrogen ($N_2$), argon ($Ar$), and even tiny amounts of atomic oxygen ($O$), molecular oxygen ($O_2$), carbon monoxide ($CO$), and water ($H_2O$)—separately. Each of these gases has its own unique properties:

  • Ionisation Energy ($W$): This is the amount of energy an incoming electron needs to knock an electron off a neutral atom or molecule. It's different for every gas, and a lower value means a gas is easier to ionise.
  • Electron Impact Cross-Section ($\sigma$): Think of this as the "effective size" of a particle. A larger cross-section means it's more likely to be hit and ionised by a passing electron.

Our new algorithm will calculate the ionisation rate for each individual gas and then sum them all together to get the total ionisation rate for the entire atmosphere.

Step 1: Defining the Martian Chemical Cocktail

Our first step in the code is to define our ingredients. We’ll list the gases and their specific properties, based on scientific data from missions like NASA’s MAVEN.

import numpy as np
from scipy.integrate import quad
import matplotlib.pyplot as plt

# Martian parameters
species = ['CO2', 'N2', 'CO', 'O', 'O2', 'Ar', 'H2O']
W = {'CO2': 33.0, 'N2': 35.6, 'CO': 34.0, 'O': 28.0, 'O2': 32.0, 'Ar': 29.6, 'H2O': 30.0} # eV
fractions = {'CO2': 0.953, 'N2': 0.027, 'CO': 0.0007, 'O': 0.0001, 'O2': 0.0013, 'Ar': 0.016, 'H2O': 0.0003}
H = {'CO2': 8.0, 'N2': 8.2, 'CO': 8.1, 'O': 10.0, 'O2': 8.3, 'Ar': 8.5, 'H2O': 7.0} # km
h0 = 120.0  # Reference altitude (km)
n0 = 1e11   # Total density at h0 (cm⁻³)
E0 = 50.0   # Reference electron energy (eV)

# Neutral density for each species
def neutral_density(h, species):
    return fractions[species] * n0 * np.exp(-(h - h0) / H[species])

# Electron flux
def electron_flux(E, E0=E0):
    return 1e7 * (E / E0) * np.exp(-E / E0)
Enter fullscreen mode Exit fullscreen mode

Step 2: Running the 2D Multi-Species Model

Now, instead of a single calculation, we’ll loop through each gas, calculate its individual contribution to the ionisation rate, and add it to our total. The result will still be a 2D contour plot, but its shape will be far more representative of the real Martian ionosphere.

For example, atomic oxygen has a low ionisation energy, which means it will be more readily ionised than $CO_2$. Even though it's in trace amounts, it can still have a noticeable effect on the overall ionisation profile. Similarly, water vapour, though highly variable, can be a significant factor in the lower atmosphere, especially near the planet's surface.

This is a testament to the power of the AYS method: by breaking down a complex problem into its constituent parts, we can build a more accurate picture of the whole.

Scaling Up: From 3D to 5D

This multi-species approach can be seamlessly integrated into our higher-dimensional models:

  • 3D: Our global model will now have different ionisation rates based on the local composition, which can vary with altitude and location.
  • 4D: Our time-dependent model will show how the contributions of different gases change as Mars's day-night cycle progresses. For instance, the atomic oxygen concentration, which is produced by solar radiation, will peak on the dayside.
  • 5D (Speculative): In our speculative 5D model, we can go even further, analysing the contributions of each gas at different energy levels. This would allow us to see, for example, which gases are most affected by high-energy cosmic rays versus low-energy photoelectrons.

This upgraded algorithm, with its attention to chemical detail, is a significant step forward. It allows us to build models that not only look like the Martian ionosphere but also behave like it. By using real data from missions like MAVEN to feed our algorithm, we can move from theoretical physics to predictive science, paving the way for future human and robotic exploration.

References

  • Huba, J. D. (2020). Global ionospheric modeling: A review of recent advances. Reviews of Geophysics, 58(1), e2019RG000650.
  • Mahajan, K. K., & G. J. W. Le. (1995). The ionospheres of Mars and Venus. Journal of Geophysical Research: Space Physics, 100(E6), 9159-9172.
  • Singhal, R. P. (2022). Elements of Space Physics (2nd ed.). NOPR.
  • Yelle, R. V. (2001). The Martian thermosphere and ionosphere: A review. Journal of Geophysical Research: Planets, 106(E5), 8171-8186.

Top comments (0)