AI & Machine Learning

Projects

The search for axion dark matter at Yale

Baker, Barrett, Brown, Heeger, Lamoreaux, Lehnert, Maruyama

Experiments: ALPHA, HAYSTAC, RAY

Science Goal: Search for axion dark matter using quantum and microwave technologies.

Heeger Group Involvement: The Yale group uses AI & ML for handling large amounts of data, categorization and validation for data selection, pattern recognition, noise reduction, and data processing.

Sponsors: DOE QuantiSED, Simons Foundation, John Templeton Foundation, Knut & Alice Wallenberg Foundation, NSF, Swedish National Space Agency, Swedish Research Council

Publications: Phys. Rev. Lett. 134, 151006 (2025); Phys Rev D. 109 032009 (2024); Nature 590, 238–242 (2021)

Three people installing a dilution fridge.

Directly measuring the mass of the neutrino

Heeger

Experiment: Project 8

Science Goal: Utilize a novel technique (CRES) to perform a precision measurement of the yet unknown neutrino mass.

Heeger Group Involvement: The Heeger group uses AI and ML for data analysis and event reconstruction in neutrino experiments. The Yale group is collaborating with Pacific Northwest National Laboratory (PNNL) to build a data management framework for the Project 8 experiment.

Sponsor: DOE-NP

Publications: Mach. Learn. Sci. Tech. 5, 025026 (2024); JINST 19 P05073 (2024)

Project 8 Phase II experimental setup.

Searching for neutrinoless double beta decay

Heeger, Maruyama

Experiments: CUORE, CUPID

Science Goal: Search for neutrinoless double beta decay, which could answer why we live in a Universe of matter, not antimatter.

WL Involvement: The group uses AI and ML for data analysis and event reconstruction in neutrino experiments.

Sponsor: DOE-NP

Publications: J PPNP 122, 103902 (2022); arXiv:2504.14369

2 scientists in clean room garb looking inside CUORE detector.