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Celebrating A-LIFT’s Outstanding Pre-College Volunteer: Hannah Parrilla

A group of people standing holding awards.

Episode 3: The Invisible Cannonball: Tracking Alpha Therapy for the Future of Cancer Treatment

Fast Machine Learning-Enabled Uncertainty Quantification for Radiological Mapping

the image shows the radiological mapping and image reconstruction process. A detector system carried by an unmanned aerial vehicle scans the field to measure the counts due to environmental background and potential radioactive sources. The data collected during the mapping is processed on an edge computer. The major results output from the computer is a map showing the estimated radioactivity intensity.

Offline Reinforcement Learning for VENUS Control

An icon/illustration of a computer screen (on the left) 'interacts' through "Action" (with an arrow pointing from left to right), as well as "Observation" and "Reward" (with arrows pointing right to left), with a representation of the VENUS Ion Source (on the right).

After Dark: Modern Methods of Visualizing Nuclear Radiation

3 people standing in front of multiple displays showing radiation imaging technologies.

Quest for Tritium Detection with Scientific CCDs Fueled by Deep Learning

A large metal item with wires

Complex analyses, uncertainty quantification, and machine learning

A visual representation of the process that the Rare Event Surrogate Model uses.

LBNL develops gamma spectroscopy optimization code for international nuclear safeguards

A graph displaying the optimal labels

3Q4: Hannah Parrilla and Autism Acceptance Month

Researchers Demonstrate Robotic Inspection for Nuclear Safeguards

A Bayesian Approach to Free-Moving Radiation Mapping with a Single Detector

Empowering Detector Development: Introducing the Upgraded Scintillator Library

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