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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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A flexible Chemical Evolution model
Recommended citation: Andrews, B. H., et al., 2017, ApJ, 835, 224.
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Data access, exploration, analysis, and visualization toolkit for the SDSS-IV MaNGA survey
Recommended citation: Cherinka, B., et al., 2019, AJ, 158, 74.
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Research boot camp for undergrads and early PhD students
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Python boot camp for undergrads and early PhD students
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Photometric Redshifts from SDSS Images with an Interpretable Deep Capsule Network
Radiation pressure from the absorption and scattering of starlight by dust grains, which are hydrodynamically coupled to the gas, sets a hard upper limit to the luminosity of star-forming regions.
PCA of elemental abundances of bulge stars revealing that the first two components are (1) correlated alpha elements and (2) correlated Na and Ni—two elements that have metallicity-dependent core-collapse supernova yields.
The direct method mass-metallicity relation has a steeper slope, a lower turnover mass, and a factor of 2-3 greater dependence on SFR than strong line mass-metallicity relations.
We use direct method metallicities of stacked SDSS galaxies to recalibrate strong line metallicity diagnostics.
Brown, J. S., Martini, P., & Andrews, B. H. 2016, MNRAS, 458, 1529.
We explore the trade-offs in parameters of chemical evolution models.
We construct an analytic chemical evolution model with a realistic SNIa delay time distribution to probe how model parameters affect equilibrium abundances and the effects of sudden changes to those parameters.
Weinberg, D. H., Andrews, B. H., & Freudenburg, J. 2017, ApJ, 837, 183.
We develop a data access, exploration, analysis, and visualization toolkit for the SDSS-IV MaNGA survey that seamlessly transitions between locally and remotely-stored data.
Cherinka, B., Andrews, B. H., Sanchez-Gallego, J., et al. 2019, AJ, 158, 74.
We use several machine learning algorithms to classify intermediate redshift emission line galaxies using only properties in optical spectra and ugriz photometry.
Zhang, K., Schlegal, D. J., Andrews, B. H., et al. 2019, ApJ, 883, 63.
MaNGA Milky Way Analogs consistently have younger stellar populations at large radii but a wide range of central stellar population ages; about half of the MWAs have central AGN or composite activity.
Boardman, N., Zasowski, G., Seth, A., Newman, J., Andrews, B. H., et al. 2020, MNRAS, 491, 3672.
We use Gaussian Process Regression trained on the fluxes of external galaxies to estimate the Milky Way’s UV-to-IR SED.
Fielder, C., Newman, J., Andrews, B. H., et al. 2021, arXiv:2106.14900.
We introduce a method for local calibration of photo-z PDFs.
Dey, B., Newman, J., Andrews, B. H., et al. 2021, arXiv:2110.15209.
We use a deep capsule network to achieve state-of-the-art photo-z’s for SDSS galaxies.
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Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.