Posts
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Spherical harmonic animations with matplotlib and cartopy
This post demonstrates three Python tricks: computing spherical harmonics, plotting map projections with Cartopy, and saving animations in matplotlib. I bring these together to generate animated gifs of spherical harmonics like the one below. These are useful for visualizing stellar pulsation geometries, and I often display these in talks or lectures. If you want to skip right ahead to using the script I wrote to generate these animations, you can find it here.
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Generating citable paper names with a recurrent neural network
Disappointed that mine are not among the most cited papers in the area of stellar astrophysics, I thought I might use machine learning to identify more promising topics for future works that would be of greater value to the field. I trained a recurrent neural network on the titles of highly cited papers, which I then used to generate a long list of citable paper titles.
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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).
Astronomy, statistics, programming in Python, data visualization, data science, and other great icebreakers.
- Bootstrapping a significance threshold for periodogram analysis
- How data sampling affects the Fourier transform periodogram
- Three statistical tests for average spacing among numbers
- Confidence intervals for 2D Gaussian mixture models with contours
- What's the expected average value of a noisy amplitude spectrum?