Multi-wavelength multi-direction laser light scattering for cell characterization using machine learning-based methods.

Cell identification and analysis play a crucial role in many biology- and health-related applications. The internal and surface structures of a cell are complex and many of the features are sub-micron in scale. Well-resolved images of these features cannot be obtained using optical microscopy.

Direct Measurement of the Return Current Instability in a Laser-Produced Plasma.

Measurements were made of the return current instability growth rate, demonstrating its concurrence with nonlocal transport. Thomson scattering was used to measure a maximum growth rate of 5.1×10^{9}  Hz, which was 3 times less than classical Spitzer-Härm theory predicts.

Quantitative assessment of fitting errors associated with streak camera noise in Thomson scattering data analysis.

Thomson scattering measurements in high energy density experiments are often recorded using optical streak cameras. In the low-signal regime, noise introduced by the streak camera can become an important and sometimes the dominant source of measurement uncertainty. In this paper, we present a formal method of accounting for the presence of streak camera noise in our measurements.

Measurements of Non-Maxwellian Electron Distribution Functions and Their Effect on Laser Heating.

Electron velocity distribution functions driven by inverse bremsstrahlung heating are measured to be non-Maxwellian using a novel angularly resolved Thomson-scattering instrument and the corresponding reduction of electrons at slow velocities results in a ∼40% measured reduction in inverse bremsstrahlung absorption. The distribution functions are measured to be super-Gaussian in the bulk (v/v_{th}<3) and Maxwellian in the tail (v/v_{th}>3) when the laser heating rate dominates over the electron-electron thermalization rate. Simulations with the particle code quartz show the shape of the tail is dictated by the uniformity of the laser heating.

Integration of light scattering with machine learning for label free cell detection.

Light scattering has been used for label-free cell detection. The angular light scattering patterns from the cells are unique to them based on the cell size, nucleus size, number of mitochondria, and cell surface roughness. The patterns collected from the cells can then be classified based on different image characteristics.