3 citations · 7 across the 7 of their papers we have counts for
7 papers
Improvement of Nuclide Detection through Graph Spectroscopic Analysis Framework and its Application to Nuclear Facility Upset Detection
Pedro Rodríguez Fernández, Christian Svinth, Alex Hagen
We present a method to improve the detection limit for radionuclides using spectroscopic radiation detectors and the arrival time of each detected radiation quantum. We enable this…
Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials
Marjolein Oostrom, Alex Hagen, Nicole LaHaye +1
Understanding the relationship between the evolution of microstructures of irradiated LiAlO2 pellets and tritium diffusion, retention and release could improve predictions of triti…
Sim-to-real supervised domain adaptation for radioisotope identification
Peter Lalor, Henry Adams, Alex Hagen
Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental data…
SDSS-IV from 2014 to 2016: A Detailed Demographic Comparison over Three Years
Amy M. Jones, Rachael L. Beaton, Brian A. Cherinka +24
The Sloan Digital Sky Survey (SDSS) is one of the largest international astronomy organizations. We present demographic data based on surveys of its members from 2014, 2015 and 201…
Synthesis parameter effect detection using quantitative representations and high dimensional distribution distances
Alex Hagen, Shane Jackson
Detection of effects of the parameters of the synthetic process on the microstructure of materials is an important, yet elusive goal of materials science. We develop a method for d…
Reduction of detection limit and quantification uncertainty due to interferent by neural classification with abstention
Alex Hagen, Ken Jarman, Jesse Ward +5
Many measurements in the physical sciences can be cast as counting experiments, where the number of occurrences of a physical phenomenon informs the prevalence of the phenomenon's…
DBCal: Density Based Calibration of classifier predictions for uncertainty quantification
Alex Hagen, Karl Pazdernik, Nicole LaHaye +1
Measurement of uncertainty of predictions from machine learning methods is important across scientific domains and applications. We present, to our knowledge, the first such techni…