Improving a Galaxy Generation algorithm

In summary: If you would like feedback on the believability of a specific star system, I would be happy to provide my opinion.In summary, the "Accrete" algorithm is a good starting point for generating realistic planetary systems, but it may require some adjustments to address limitations such as the number of planets generated and the inclusion of epistellar gas giants. Additionally, simplified models such as the Hill sphere concept can be used to estimate the likelihood of satellite capture. Ultimately, the goal is to create a believable and immersive experience for players, while also considering the scientific principles behind galaxy formation.
  • #1
AvengerDr
3
0
Hello there, I'm developing a 4x Space Opera game. For my galaxy generation algorithm I'm using an improved version of the "Accrete" one, which I'm sure some of you may have heard.

It generates fairly believable planets. Rocky ones are placed in the inner system, while gas giants in the outer zones. There are howewer three problems with that.

1) It is not possibile to influence the number of planets generated. While in a game this would be required. As it is now it generates an average of 9 planets per system. Players may want to play with a smaller amount of planets.
2) It doesn't work very well to simulate the accretion of satellites. So as a solution of the first problem I was trying to try to estabilish whether some planets would capture other, smaller, planets as satellites. Simulating a complex gravitational system would be ephemeral I think.. So I'd just need some way to simulate whether a planet could capture another. Is there some kind of calculation that I could use?
3) The algorithm does not generate epistellar gas giants. If I understood correctly those are gas giants who migrate inward. Is there some way to estimate whether one could do so?

Randomly? I was hoping for some pseudo-scientific calculations, so that my conscience would feel good :)

From current results, on 500 planets, only 3-4 on average of "Terrestrial class" (there are also some of "desrtic" or "ocean" types that are not counted as "terran"). Surprisingly (or maybe not so), there are more planets that could sustain ammonia based lifeforms that terran ones. Venusian like worlds also seemed to be rarer. Only 20 on average, with half of them being "wet" greenhouse system with vast oceans. More than 200 of them were uninteresting rocky planets completely frozen over. Another hundred, barren planets a-la Mars.

If someone is interested I can select some "interesting" star system, so that you can look at the details and give me an opinion of the degree of believability of it :)

Thanks in advance
 
Astronomy news on Phys.org
  • #2


Hello there,

I can offer some insight and suggestions for your galaxy generation algorithm. First of all, the "Accrete" algorithm is a well-known and widely used method for generating believable planetary systems. However, as you have pointed out, it does have some limitations.

To address your first problem, it is possible to influence the number of planets generated by adjusting the initial conditions of the algorithm. This can be done by varying the size and density of the protoplanetary disk, as well as the initial distribution of dust and gas. It may require some trial and error to find the right balance, but it is possible to generate a smaller number of planets if desired.

Regarding your second problem, simulating the accretion of satellites is a complex task and may not be necessary for a game. However, there are some simplified models that can estimate the likelihood of a planet capturing a satellite based on its mass and orbital distance. These models are based on the concept of the Hill sphere, which is the region around a planet where its gravitational influence dominates over that of the star. If a smaller object enters this region, it is likely to be captured as a satellite. I suggest researching more on the Hill sphere and using it as a guideline for determining the likelihood of satellite capture in your game.

Lastly, the algorithm may need some adjustments to generate epistellar gas giants. These are gas giants that have migrated inward and can be found orbiting very close to their star. The migration process is complex and not fully understood, but it is possible to estimate the likelihood of a gas giant migrating inward based on its mass, orbital distance, and the properties of the protoplanetary disk. Again, this may require some trial and error to find the right parameters for your game.

In terms of the distribution of different types of planets in your generated systems, it is important to keep in mind that the universe is a diverse and unpredictable place. While it may seem surprising that there are more planets capable of sustaining ammonia-based life forms than terrestrial ones, it is not entirely unrealistic. The conditions for life to exist are complex and can vary greatly from planet to planet. As for the rarity of Venusian-like worlds, it could be due to a combination of factors such as the properties of the protoplanetary disk and the distance of the planet from its star.

Overall, it is important to strike a balance between scientific accuracy and gameplay in your galaxy generation algorithm. While it may not be
 

Related to Improving a Galaxy Generation algorithm

What is a Galaxy Generation algorithm?

A Galaxy Generation algorithm is a set of rules and calculations used to create a simulated galaxy based on certain parameters and variables. It is typically used in computer programs or video games to generate a realistic or fictional galaxy for gameplay purposes.

Why is it important to improve a Galaxy Generation algorithm?

Improving a Galaxy Generation algorithm can lead to more realistic and diverse galaxies in simulations and games. It can also enhance the overall user experience and make the generated galaxies more visually appealing. Additionally, advancements in algorithm design can lead to more efficient and accurate results.

What factors should be considered when improving a Galaxy Generation algorithm?

Factors to consider when improving a Galaxy Generation algorithm include the desired level of realism, the computational resources available, and the specific goals for the generated galaxy. It's also important to consider the limitations and trade-offs of different algorithms and how they may affect the final result.

How can machine learning be incorporated into a Galaxy Generation algorithm?

Machine learning can be used to improve a Galaxy Generation algorithm by training the algorithm on existing data and patterns from real or simulated galaxies. This can lead to more accurate and diverse results, as the algorithm can learn from a larger and more diverse dataset.

What are some potential challenges when improving a Galaxy Generation algorithm?

Some potential challenges when improving a Galaxy Generation algorithm include balancing realism with efficiency, as more complex algorithms may require more computational resources. It can also be difficult to account for all the variables and factors that contribute to a galaxy's formation, as there is still much we don't know about the universe.

Similar threads

Replies
17
Views
2K
  • Astronomy and Astrophysics
Replies
2
Views
1K
Replies
2
Views
951
Replies
9
Views
5K
  • Astronomy and Astrophysics
Replies
7
Views
4K
  • Astronomy and Astrophysics
Replies
19
Views
2K
  • Sci-Fi Writing and World Building
Replies
21
Views
1K
  • Astronomy and Astrophysics
Replies
7
Views
2K
  • Sci-Fi Writing and World Building
Replies
19
Views
2K
  • Astronomy and Astrophysics
Replies
4
Views
4K
Back
Top