https://foojayio.github.io/website/today/author/gagik-gavalian/Articles written by Gagik Gavalian on foojay.io - Friends of OpenJDKHugoen-usThu, 16 Jun 2022 09:01:44 +0000https://foojayio.github.io/website/today/deep-learning-in-java-for-nuclear-physics-using-deep-netts/Thu, 16 Jun 2022 09:01:44 +0000https://foojayio.github.io/website/today/deep-learning-in-java-for-nuclear-physics-using-deep-netts/<p><img src="https://foojayio.github.io/website/today/deep-learning-in-java-for-nuclear-physics-using-deep-netts/CLAS12-side.png" alt="" loading="lazy"> </p> <h2 id="h2-0-background">Background</h2> <p>The CLAS12 detector at Jefferson Lab conducts nuclear physics experiments to study the structure of nucleons and new particle states.</p> <p>At the core of detector setup are drift chambers that measure charged particles resulting from the interaction of an electron beam with a liquid hydrogen target.</p> <p>The charged particles are reconstructed by combining segments of the track measured in 6 separate drift chambers along the particle trajectory.</p> <p>In high-intensity experiments, detector systems produce noise segments increasing the combinatorics of track candidates to be analyzed by the conventional algorithm, which leads to decreased track reconstruction efficiency.</p>