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Autoencoding Galaxy Spectra II: Redshift Invariance and Outlier Detection
Yan Liang, Peter Melchior, Sicong Lu +2
We present an unsupervised outlier detection method for galaxy spectra based on the spectrum autoencoder architecture spender, which reliably captures spectral features and provide…
Autoencoding Galaxy Spectra I: Architecture
Peter Melchior, Yan Liang, ChangHoon Hahn +1
We introduce the neural network architecture SPENDER as a core differentiable building block for analyzing, representing, and creating galaxy spectra. It combines a convolutional e…
Second Data Release of the Hyper Suprime-Cam Subaru Strategic Program
Hiroaki Aihara, Yusra AlSayyad, Makoto Ando +62
This paper presents the second data release of the Hyper Suprime-Cam Subaru Strategic Program, a wide-field optical imaging survey on the 8.2 meter Subaru Telescope. The release in…
The Role of Machine Learning in the Next Decade of Cosmology
Michelle Ntampaka, Camille Avestruz, Steven Boada +27
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmologic…
The Hyper Suprime-Cam SSP Survey: Overview and Survey Design
H. Aihara, N. Arimoto, R. Armstrong +139
Hyper Suprime-Cam (HSC) is a wide-field imaging camera on the prime focus of the 8.2m Subaru telescope on the summit of Maunakea in Hawaii. A team of scientists from Japan, Taiwan…
Filling the gaps: Gaussian mixture models from noisy, truncated or incomplete samples
Peter Melchior, Andy D. Goulding
Astronomical data often suffer from noise and incompleteness. We extend the common mixtures-of-Gaussians density estimation approach to account for situations with a known sample i…