Engineering topics
Publications and source records attributed to Hagen, Alex.
The code takes expert-labeled segmented images of irradiated and unirradiated pellets and trains DeepConvolutional Neural Networks to segment these images into defects, backgrounds, and boundaries. The code calculated qualitative microstructural information from these segmented images to facilitate the comparison of unirradiated and irradiated pellets
We use broadband photometry extending from the rest-frame UV to the near-IR to fit the individual spectral energy distributions of 63 bright (L(Ly-alpha) greater than 10(exp 43) erg s(exp −1) Ly-alpha emitting galaxies (LAEs) in the redshift range 1.9 less than z less than 3.6. We find that these LAEs are quite heterogeneous, with stellar masses that span over three orders of magnitude, from 7.5 greater than logM/solar mass less than 10.5. Moreover, although most LAEs have small amounts of extinction, some high-mass objects have stellar reddenings as large as E(B − V ) is approximately 0.4. Interestingly, in dusty objects the optical depths for Ly-alpha and the UV continuum are always similar, indicating that Lyα photons are not undergoing many scatters before escaping their galaxy. In contrast, the ratio of optical depths in low-reddening systems can vary widely, illustrating the diverse nature of the systems. Finally, we show that in the star-formation-rate-log-mass diagram, our LAEs fall above the "main-sequence" defined by z is approximately 3 continuum selected star-forming galaxies. In this respect, they are similar to submillimeter-selected galaxies, although most LAEs have much lower mass.