Calypso is a parameter-conditioned stochastic surrogate model for circumbinary accretion flows using PCA and multivariate Gaussian modeling, released as open-source software with a closed-form likelihood for parameter inference from time series.
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Transfer learning with a Zoobot CNN on SDSS DR18 data identifies 3,679 lopsided spiral galaxies at 87% test accuracy, with lopsided systems showing higher star formation, bluer colors, lower mass and concentration.
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.
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\texttt{calypso}: a Parameter-Conditioned Stochastic Surrogate Model for Circumbinary Accretion Time-Series
Calypso is a parameter-conditioned stochastic surrogate model for circumbinary accretion flows using PCA and multivariate Gaussian modeling, released as open-source software with a closed-form likelihood for parameter inference from time series.
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Identifying lopsidedness in spiral galaxies using a Deep Convolutional Neural Network
Transfer learning with a Zoobot CNN on SDSS DR18 data identifies 3,679 lopsided spiral galaxies at 87% test accuracy, with lopsided systems showing higher star formation, bluer colors, lower mass and concentration.
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Inferring the properties of a population of compact binaries in presence of selection effects
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.