Dimensional regularization
Imagine you have a box of different sized shapes (like squares, circles, and triangles) and you want to count how many shapes are in the box. But some of the shapes are a bit weird - they might have parts sticking out of them or be stretched weirdly. So it's hard to count them in the normal way.
Dimensional regularization is like a special way to count these shapes that works even when they're weird. Instead of trying to count the shapes exactly, you can imagine them in a different number of dimensions. For example, you might imagine that a circle is really a flat disc that's curved in three dimensions, or that a triangle is really like a pyramid that's squished down in one dimension.
When you imagine the shapes this way, you can use special math tricks to easily count them, without worrying about how weird they are. And then you can use the results of this math to figure out things like how much energy particles have or how they interact with each other.
So, in short, dimensional regularization is a way to count weird shapes by imagining them in different dimensions and using special math tricks.
Dimensional regularization is like a special way to count these shapes that works even when they're weird. Instead of trying to count the shapes exactly, you can imagine them in a different number of dimensions. For example, you might imagine that a circle is really a flat disc that's curved in three dimensions, or that a triangle is really like a pyramid that's squished down in one dimension.
When you imagine the shapes this way, you can use special math tricks to easily count them, without worrying about how weird they are. And then you can use the results of this math to figure out things like how much energy particles have or how they interact with each other.
So, in short, dimensional regularization is a way to count weird shapes by imagining them in different dimensions and using special math tricks.