Engineering topics

Hjorth-Jensen, M.

Publications and source records attributed to Hjorth-Jensen, M..

3 records

DOE OSTI2023

Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning

The Beam Energy Scan Theory (BEST) collaboration's equation of state (EoS) incorporates a three-dimensional Ising model critical point into the quantum chromodynamics (QCD) equation of state from lattice simulations. Furthermore, it contains four free parameters related to the size and location of the critical region in the QCD phase diagram. Certain combinations of the free parameters lead to acausal or unstable realizations of the EoS that should not be considered. In this work, we use an active learning framework to rule out pathological EoS efficiently. We find that checking stability and causality for a small portion of the parameters' range is sufficient to construct algorithms that perform with > 96 % accuracy across the entire parameter space. Though in this work we focus on a specific case, our approach can be generalized to any EoS containing a parameter space-class correspondence.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Unsupervised learning for identifying events in active target experiments

This article presents novel applications of unsupervised machine learning methods to the problem of event separation in an active target detector, the Active-Target Time Projection Chamber (AT-TPC). The overarching goal is to group similar events in the early stages of the data analysis, thereby improving efficiency by limiting the computationally expensive processing of unnecessary events. The application of unsupervised clustering algorithms to the analysis of two-dimensional projections of particle tracks from a resonant proton scattering experiment on 46 Ar is introduced. We explore the performance of autoencoder neural networks and a pre-trained VGG16 Simonyan and Zisserman (2015) convolutional neural network. We study clustering performance on both data from a simulated 46 Ar experiment, and real events from the AT-TPC detector. We find that a -means algorithm applied to simulated data in the VGG16 latent space forms almost perfect clusters. Additionally, the VGG16+-means approach finds high purity clusters of proton events for real experimental data. Here, we also explore the application of clustering the latent space of autoencoder neural networks for event separation. While these networks show strong performance, they suffer from high variability in their results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Exploring the role of high- j configurations in collective observables through the Coulomb excitation of 106 Cd

In this work, the shape and collectivity of 106 Cd was investigated via a sub-barrier-energy Coulomb excitation experiment performed at the NSCL ReA3 facility using the JANUS setup. Transition matrix elements between low-lying states were found to agree with adopted values, and information on the shape and collectivity of higher-lying states was extracted for the first time. Locally-optimized large-scale shell-model calculations were found to describe well the B(E2) transition strengths but failed to reproduce the spectroscopic quadrupole moments Q s . An analysis of the E2 rotational invariants and the normalized quadrupole moment q s indicates that this may be due to a significant degree of triaxiality in 106 Cd which is not captured by the present shell-model calculations. Analogous calculations for the Fe isotopes (two protons below the Z = 28 magic number) reveal the critical role of high-j neutron configurations for the description of quadrupole moments in the heavy Fe and Cd isotopes (two protons below magic Z = 50), but this effect is insufficient to explain the shape of 106 Cd, posing a puzzle for the understanding of nuclear structure towards N = 50.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗