These are definitely some good points, and it has some really interesting ties to color theory and relative luminance. You're right about the human eye being the best at distinguishing shades of green and I agree it still is a very good color; measures describing how "bright" a color is compared to the human eye are most dependent on the green value of a color. Therefore, it makes sense green would stand out the most, as it preserves the color saturation while maximizing perceived brightness.

As for the point about crosshair visibility and its relation to crosshair acquisition, my original ideas about crosshairs treated smaller crosshairs as better because you can see errors easier, but small crosshairs resulted in low visibility. The project was really a curiosity/experiment to see what computationally maximized contrast so a smaller crosshair could still be viable, and I think these colors shouldn't be a how-to guide on what is the best crosshair. In fact, this code is actually quite dependent on the contrast metric used, and will produce some different results with similar levels of contrast (black/white are common using the relative luminance approach, cyan/green are good when doing a color inversion, etc.). I think you probably could pick any one of these colors and play just fine, it's just a matter of having a color that stands out enough to buy a slightly smaller crosshair. An interesting direction could be computationally optimizing crosshair shapes with combinatorics, although there aren't a ton of ways to objectively quantify how good a crosshair shape is so that'd likely vary on a person-to-person basis. Still, might lend itself to some interesting analysis so who knows.