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Stellar Masses of Giant Clumps in CANDELS and Simulated Galaxies Using Machine Learning

Autor: Huertas-Company, M., Guo, Y., Ginzburg, O., Lee, C. T., Mandelker, N., Metter, M., Primack, J. R., Dekel, A., Ceverino, D., Faber, S. M., Koo, D. C., Koekemoer, A., Snyder, G., Giavalisco, M., Zhang, H.

A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work we first present a new method based on neural networks to detect clump

Externí odkaz: http://arxiv.org/abs/2006.14636

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The Implementation of the Colored Abstract Simplicial Complex and its Application to Mesh Generation

Autor: Lee, C. T., Moody, J. B., Amaro, R. E., McCammon, J. A., Holst, M.

Publikováno v: ACM Trans. Math. Softw. 45, 3, Article 28 (August 2019)

We introduce CASC: a new, modern, and header-only C++ library which provides a data structure to represent arbitrary dimension abstract simplicial complexes (ASC) with user-defined classes stored directly on the simplices at each dimension. This is a

Externí odkaz: http://arxiv.org/abs/1807.01417

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Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range

Autor: Huertas-Company, M., Primack, J. R., Dekel, A., Koo, D. C., Lapiner, S., Ceverino, D., Simons, R. C., Snyder, G. F., Bernardi, M., Chen, Z., Domínguez-Sánchez, H., Lee, C. T., Margalef-Bentabol, B., Tuccillo, D.

We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the central regi

Externí odkaz: http://arxiv.org/abs/1804.07307

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Implications for Post-Processing Nucleosynthesis of Core-Collapse Supernova Models with Lagrangian Particles

Autor: Harris, J. Austin, Hix, W. Raphael, Chertkow, Merek A., Lee, C. -T., Lentz, Eric J., Messer, O. E. Bronson

Publikováno v: ApJ, 843, 21 (2017)

We investigate core-collapse supernova (CCSN) nucleosynthesis with self-consistent, axisymmetric (2D) simulations performed using the radiation-hydrodynamics code Chimera. Computational costs have traditionally constrained the evolution of the nuclea

Externí odkaz: http://arxiv.org/abs/1701.08876

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