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Peterson, Matthew

Publications and source records attributed to Peterson, Matthew.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Feature Pathway Graphs using Random Forest Regressors

SAND2025-04671O Feature Pathway Graphs using Random Forest Regressors is a software tool that uses machine learning to determine pathways of influence between features in data sets. It can be used as a surrogate method for casual discovery. The output creates pathway graphs between features of interest. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Peterson, Matthew↗
DOE OSTI2023

Detectable Changes in U1a Data [Slides]

This is an update presentation on U1a data analytics for the Advanced Data Analytics for Proliferation Detection Tech Exchange Meeting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Causal Evaluations for Identifying Differences between Observations and Earth System Models

We use a nascent data-driven causal discovery method to find and compare causal relationships in observed data and climate model output. We consider ten different features in the Arctic climate collected from public databases on observational and Energy Exascale Earth System Model (E3SM) data. In identifying and analyzing the resulting causal networks, we make meaningful comparisons between observed and climate model interdependencies. This work demonstrates our ability to apply the PCMCI causal discovery algorithm to Arctic climate data, that there are noticeable similarities between observed and simulated Arctic climate dynamics, and that further work is needed to identify specific areas for improvement to better align models with natural observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Adaptive Self Tuning

The AST software includes numeric methods to 1) adjust STA/LTA signal detector trigger level (TL) values and 2) filter detections for a network of sensors. AST adapts TL values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: TL values are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The AST algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest.

Peterson, Matthew↗