Michael Snow’s article “Exotic physics with slow neutrons” (Physics Today, March 2013, page 50) was both enjoyable and informative. I especially appreciated reading about how neutrons can be used to probe fundamental physics. The author mentioned areas of physics in which meV and colder neutrons have played an important role. I wish he had also mentioned the very rich contributions made by the study of the interaction of low-energy neutrons with heavy nuclei. (“Low-energy” refers to neutron energies from roughly 1 eV to tens of keV, greater by factors of 40 to 1011 than those discussed by Snow.)

The absence of a net charge enables a low-energy neutron to interact with heavy nuclei and probe the resonant excited states near the binding energy of a neutron in the resulting compound nucleus. The study of those resonances over decades bore great fruit. I offer a few examples: the formulation of the low-energy optical model, a reaction theory designed in part to characterize those resonances, and level-density calculations based on thermodynamic models. In addition, the excited nuclear states are highly complicated, as implied by their high excitation energy (around 6 or 7 MeV), their huge numbers (many tens of thousands or more of a given spin and parity per MeV), and their seemingly random energy widths and spacings. Their nature could not be realistically described by the application of the shell or collective nuclear models and thus forced a statistical view of the behavior of their energy widths and spacing distributions.

The statistical view ultimately led to the Porter–Thomas distribution of reduced widths and to Wigner’s and Dyson’s theories of level-spacing distributions—that is, random matrix theory.1 Remarkably, random matrix physics has found application as a signature of chaos in simple systems and is used in other areas of physics, even particle physics. Bravo to this strong neutral particle with a gentle decay.

1.
C. E.
Porter
,
Statistical Theories of Spectra: Fluctuations
,
Academic Press
,
New York
(
1965
);
M. L.
Mehta
,
Random Matrices
,
3rd ed.
,
Academic Press/Elsevier, San Diego, CA
(
2004
).