Showing papers by "Jake Vanderplas published in 2012"

Scikit-learn: Machine Learning in Python

Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.

Interpolating masked weak-lensing signal with karhunen-loève analysis

Abstract: We explore the utility of Karhunen-Lo?ve (KL) analysis in solving practical problems in the analysis of gravitational shear surveys Shear catalogs from large-field weak-lensing surveys will be subject to many systematic limitations, notably incomplete coverage and pixel-level masking due to foreground sources We develop a method to use two-dimensional KL eigenmodes of shear to interpolate noisy shear measurements across masked regions We explore the results of this method with simulated shear catalogs, using statistics of high-convergence regions in the resulting map We find that the KL procedure not only minimizes the bias due to masked regions in the field, it also reduces spurious peak counts from shape noise by a factor of ~3 in the cosmologically sensitive regime This indicates that KL reconstructions of masked shear are not only useful for creating robust convergence maps from masked shear catalogs, but also offer promise of improved parameter constraints within studies of shear peak statistics