Computer Discovers New Recipes for Galaxies express

Original: "The functional form of galaxy and halo luminosity and mass functions"
arXiv:2604.23236 · 2026-04-25 · CC BY 4.0 · 1 min
A program independently discovered simple formulas that more accurately describe how many galaxies of different brightness and mass are in the Universe.

📄 Showing the "Simple" version — "Popular" is not ready yet. Add it to favorites to help prioritize it.

Astronomers, like chefs, have long been preparing a description of the Universe using the same cumbersome recipes. These recipes — complex mathematical formulas — estimated how many galaxies of different luminosity and mass fill the cosmos, as well as how the invisible frameworks of dark matter are distributed. But the dishes lacked accuracy. So, a digital head chef was called into the kitchen — the ESR algorithm. It tested millions of variations from simple operations: addition, multiplication, exponentiation.

The ESR algorithm, like a tireless experimenter, tried millions of combinations of elementary mathematical operations to find the most accurate and concise descriptions of cosmic distributions.

The result was stunning: it turned out that bells and whistles aren't needed for the perfect outcome. Simple relationships — for example, 'the brighter the galaxy, the fewer there are' — work more accurately if artificial corrections are removed. It's like discovering that a croissant only needs three ingredients, and it tastes better than a complex recipe. Now astronomers can piece together the puzzle of galaxy history faster and refine the standard cosmological model.

Small, dim galaxies vastly outnumber giant bright ones in the Universe — like pebbles on a beach compared to boulders.

Just like in 'The Hitchhiker's Guide to the Galaxy' a supercomputer sought the Answer to the ultimate question, this algorithm seeks the perfect recipe for the cosmos — except instead of philosophy, it yields precise equations.

Scientists
Fritz ZwickyJames JeansSubrahmanyan ChandrasekharAdam RiessJohn Archibald WheelerNancy Grace Roman
Concepts
initial mass function symbolic regression star formation rate point spread function functional analysis sdss