Scientists at the Relativistic Heavy Ion Collider (RHIC) have developed a new method to analyze atomic nuclei shapes through high-energy particle collisions. The study, published in Nature, provides insights into nuclear structure and is expected to enhance understanding of visible matter.
Jiangyong Jia, a professor at Stony Brook University and co-author of the study, explained: “In this new measurement, we not only quantify the overall shape of the nucleus — whether it’s elongated like a football or squashed down like a tangerine — but also the subtle triaxiality.” This research is relevant for understanding nuclear fission, neutron star collisions, and exotic particle decay discoveries.
The technique involves analyzing data from RHIC's STAR detector. Chunjian Zhang noted that “the high-energy imaging method...is orders of magnitude faster,” allowing for detailed snapshots of nuclear structures. Dean Lee added that each collision provides unique data due to quantum variations in atomic nuclei.
The method was tested using gold and uranium nuclei. Shengli Huang described how gold collisions produced consistent patterns due to their spherical nature, while uranium exhibited more variability. The analysis allowed scientists to infer uranium's complex nuclear shape.
Chun Shen highlighted computational challenges faced during this research: over 20 million CPU hours were used on the Open Science Grid for modeling collision events. Zhang emphasized that these computations helped quantify differences between uranium and gold nuclei shapes accurately.
This approach promises applications beyond current findings. It may improve understanding of initial conditions in quark-gluon plasma (QGP) formation at RHIC and Europe's Large Hadron Collider (LHC). Jia mentioned interdisciplinary benefits as well: “Nuclear physics has many subfields...workshops, meetings, and conferences were organized.”
Supported by the U.S. Department of Energy Office of Science and National Science Foundation among others, this work utilized resources from Brookhaven Lab's Scientific Data and Computing Center as well as NERSC at Lawrence Berkeley National Laboratory.