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Machine Learning for Observational Cosmology
Kana Moriwaki, Takahiro Nishimichi, Naoki Yoshida
An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver…
A-SLOTH: Ancient Stars and Local Observables by Tracing Halos
Mattis Magg, Tilman Hartwig, Li-Hsin Chen +1
Galaxies are thought to reside inside of large gravitationally bound structures of dark matter, so-called haloes. While the smallest of these haloes host no or only a few stars, th…
Large format imaging spectrograph for the Large Submillimeter Telescope (LST)
K. Kohno, R. Kawabe, Y. Tamura +19
We present a conceptual study of a large format imaging spectrograph for the Large Submillimeter Telescope (LST) and the Atacama Large Aperture Submillimeter Telescope (AtLAST). Re…
Photometric classification of HSC transients using machine learning
Ichiro Takahashi, Nao Suzuki, Naoki Yasuda +5
The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through…
Single-epoch supernova classification with deep convolutional neural networks
Akisato Kimura, Ichiro Takahashi, Masaomi Tanaka +3
Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accur…
Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey
Mikio Morii, Shiro Ikeda, Nozomu Tominaga +8
We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with Hyper Suprime-Cam (HSC) on the Subaru telescope. Our goa…