Combining experimental data to test models of new physics that explain dark matter (Vol. 49, No. 1)

The most statistically consistent and versatile tool to date is designed to gain insights into dark matter from models that extend the standard model of particle physics, rigorously comparing them with the latest experimental data.

In chess, a gambit refers to a move in which a player risks one piece to gain an advantage. The quest to explain dark matter, a missing ingredient from the minimal model that can describe the fundamental particles we have observed (referred to as the standard model of particle physics), has left many physicists eager to gain an advantage when comparing theoretical models to as many experiments as possible. In particular, maintaining calculation speed without sacrificing the number of parameters involved is a priority. Now the GAMBIT collaboration, an international group of physicists, has just published a series of papers that offer the most promising approach to date to understanding dark matter. The collaboration has developed the eponymous GAMBIT software, designed to combine the growing volume of experimental data from multiple sources—a process referred to as a global fit—in a statistically consistent manner. Such data typically comes from astrophysical observations and experiments that collide subatomic particles, such as those involving the Large Hadron Collider (LHC), based at CERN in Geneva, Switzerland.

The GAMBIT Collaboration, Status of the scalar singlet dark matter model, Eur. Phys. J. C 77, 568 (2017)
[Abstract]