Physicists at the Perimeter Institute have developed a novel simulation method to study self-interacting dark matter, a type of dark matter that collides with itself but not with ordinary matter, potentially triggering dramatic collapses within dark matter halos. This research, unveiled on January 19, 2026, offers new insights into how these collisions could heat and densify the cores of dark matter halos, influencing galaxy formation and potentially seeding black holes.

The new simulation code addresses a critical gap in cosmological modeling. Previously, accurately simulating the behavior of self-interacting dark matter in the crucial middle ground between weak and strong interactions was computationally prohibitive. The improved code is faster, more precise, and accessible, even runnable on a standard laptop, according to researchers at the Perimeter Institute.

Dark matter, an invisible substance that makes up approximately 85% of the universe's mass, has been a subject of intense scientific inquiry for nearly a century. Its gravitational influence is crucial in shaping galaxies and the large-scale structure of the cosmos, yet its fundamental nature remains a mystery. Self-interacting dark matter is one proposed explanation, suggesting that dark matter particles can interact with each other through forces other than gravity.

The implications of this research extend to our understanding of galaxy formation and evolution. If dark matter particles can collide and exchange energy, it could alter the density profiles of dark matter halos, the invisible scaffolding around which galaxies form. These changes could, in turn, affect the distribution of stars and gas within galaxies.

"Understanding the dynamics of dark matter halos is crucial for understanding how galaxies form and evolve," said one of the physicists at the Perimeter Institute involved in the research. "This new simulation allows us to explore a wider range of possibilities and test different models of dark matter."

The ability to model these interactions more accurately also has implications for the search for dark matter itself. By comparing the predictions of these simulations with observations of galaxies and galaxy clusters, scientists can potentially constrain the properties of dark matter particles and narrow down the search for their direct detection.

The development of this new simulation code represents a significant step forward in the field of dark matter research. It provides a powerful tool for exploring the complex dynamics of self-interacting dark matter and its impact on the universe. Future research will focus on refining the simulation and comparing its predictions with observational data to further test the self-interacting dark matter hypothesis. The researchers plan to make the code publicly available, fostering collaboration and accelerating progress in the field.