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  • Machine learning galaxy classification. I. Project description

    This is the first in an eventual series of posts in which I will play with some simple machine learning tools to revisit an old project: assigning galaxy morphology classifications based on survey photometry. Today, I am just going to describe the project and its motivation.

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  • Inclination angles and a uniform distribution for isotropy

    Here’s a little math problem that I’ve done enough times to warrant writing down somewhere: how can we most efficiently represent and randomly draw isotropic inclination angles? (spoiler: they’re uniform in \(\cos{i}\).)

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  • Nyquist analysis and the pyquist module

    My collaborators and I recently published a paper in which we determined frequencies of stellar oscillations that were severely undersampled in time series data. That paper, Destroying Aliases from the Ground and Space: Super-Nyquist ZZ Cetis in K2 Long Cadence Data, thoroughly explores the ways that observational sampling affects the signatures of stellar pulsations, but I thought it might be useful to lay out some of the basic ideas and tools behind this type of analysis (including a python module: pyquist). My main website has a very short summary of the science, if you’re interested.

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  • Hello World

    Heading into the new year, I’m finally striking off something that has been on the to-do list for a long time: setting up a personal blog space. I don’t anticipate that this will be a particularly active place or of general interest, but I hope that the content is occasionally of use to other researchers. This will host digital excerpts of my physical research journal, general results that weren’t easy to find on Google, answers to StackExchange questions that no one is asking. Snore.

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