Monte Carlo problem - mean free path to a star

In summary, the conversation discusses a homework problem involving calculating the mean free path in a static, infinite, and homogeneous universe with a given star density and radius. The formula for the mean free path is derived and compared to the results from a Monte Carlo program. The post asks for confirmation of the formula and suggests posting in a math section for further help.
  • #1
Adoniram
94
6

Homework Statement


(We are to solve this with Monte Carlo programming. Based on the universe from Olber's paradox)
Suppose you are in an infinitely large, infinitely old universe in which the average
density of stars is n = 10^9 Mpc^−3 and the average stellar radius is equal to the Sun’s
radius R = 7 × 10^8 m. How far, on average, could you see in any direction before
your line of sight struck a star? (Assume standard Euclidean geometry holds true in
this universe.)

We are allowed to assume:
-The universe is static
-The stars are roughly homogeneously distributed
-Every star has radius = solar radius

Homework Equations


My thoughts:
l = 1/(n * sigma)
where sigma is the cross sectional area of interaction, or Pi R_sun^2

The Attempt at a Solution


I have working code and get a decent answer (with a LOT of waiting...) but I wanted to verify my answer. We had a similar 2D problem where we calculated the MFP of an arrow shot in a forest with average tree density of 0.005 trees per m^2, and tree radius of 1m.

I was easily able to verify my results by using the formula from section 2 (above), which was 100m. With a large enough forest, and enough runs, I was able to get ~100m from my Mathematica program.

For this 3D problem, I wanted to verify the answer (by hand) to confirm my program's results.

If the formula from part 2 can be applied here, where n= 10^9 Mpc^-3, and sigma = Pi*R_sun^2, I get:
MFP = 6.26 x 10^23 pc

(Keep in mind this is a homogeneously distributed universe, meaning no galaxies, no clusters, etc)

Anyway, my program gets around 5 x 10^24, so I'm wondering if my calculation is wrong, or if my program just needs higher precision or something...

Any help is appreciated!
 
Last edited:
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  • #2
Thanks for the post! Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post?
 
  • #3
Thanks for the bump. All the relevant information is there. I think MC problems are generally notoriously difficult, but maybe I should have posted this in a math section, since what I really wanted was just a confirmation of a formula to use for 3D mean free path...
 
  • #4
Adoniram said:
Thanks for the bump. All the relevant information is there. I think MC problems are generally notoriously difficult, but maybe I should have posted this in a math section, since what I really wanted was just a confirmation of a formula to use for 3D mean free path...
I got the same formula from first principles. Numerically I get slightly less: 6.19E23. Maybe you need to post your algorithm.
 
  • #5


Your calculation seems to be correct. The mean free path (MFP) in this scenario is the average distance a photon can travel before encountering a star. In this case, we can use the formula l = 1/(n * sigma) as you mentioned, where n is the number density of stars and sigma is the cross-sectional area of interaction.

Using the given values of n = 10^9 Mpc^-3 and R = 7 × 10^8 m, we can calculate the cross-sectional area of a star as sigma = Pi * R^2 = Pi * (7 × 10^8)^2 = 1.54 x 10^18 m^2.

Plugging this into the MFP formula, we get l = 1/(10^9 * 1.54 x 10^18) = 6.49 x 10^-28 Mpc. This is equivalent to 6.49 x 10^-19 pc, which is close to your calculated value of 6.26 x 10^23 pc.

It is possible that your program may need higher precision or more runs to get a more accurate result. Additionally, since this is a Monte Carlo simulation, there may be some random fluctuations in the results. Overall, your approach and calculation seem to be correct.
 

Related to Monte Carlo problem - mean free path to a star

1. What is the Monte Carlo method and how does it relate to the mean free path to a star?

The Monte Carlo method is a computational method used to approximate complex systems or processes by using random sampling. In the context of the mean free path to a star, the Monte Carlo method can be used to simulate the path of particles (such as photons or atoms) traveling through space and interacting with the interstellar medium, which can help determine the average distance they travel before colliding with particles in their path.

2. How is the mean free path to a star calculated?

The mean free path to a star is calculated by dividing the average distance traveled by a particle in a given direction by the probability of collisions per unit distance in that direction. This calculation takes into account the density of the interstellar medium and the cross-sectional area of the particles.

3. What factors affect the mean free path to a star?

The mean free path to a star is affected by the density of the interstellar medium, the size and shape of the particles, and the strength of their interactions with other particles. Other factors such as temperature and magnetic fields may also play a role in determining the mean free path.

4. How accurate is the Monte Carlo method in calculating the mean free path to a star?

The accuracy of the Monte Carlo method in calculating the mean free path to a star depends on the assumptions and parameters used in the simulation. It is generally considered a reliable method for approximating complex systems, but the results should always be interpreted with caution and validated with other methods.

5. How can the mean free path to a star be used in astronomical research?

The mean free path to a star is an important parameter in understanding the behavior of particles in the interstellar medium. It can be used to study the dynamics of star formation, the evolution of planetary systems, and the transport of radiation in the Universe. It also provides valuable insights into the physical properties of the interstellar medium and can help improve our understanding of the structure and evolution of galaxies.

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