International Conference on Machine Learning for Astrophysics - ML4Astro

Session

Transients and Time Series

31 May 2022, 15:50

Catania

Conveners

Transients and Time Series: Session 1

• Giuliano Taffoni (University of Pisa and INFN-Pisa)

20. Forecasting solar cycle 25 and next solar minimum using a multi-step Bayesian deep neural network model

Ilaria Bizzarri (Università di Torino)

31/05/2022, 15:50

Oral Presentation

The latest advances in deep learning techniques have provided new effective prediction models that allow forecasting in detail the evolution of cosmogeophysical time series such as the solar activity, which is also crucial to anticipate potentially adverse space weather effects on the Earth’s environment.
Because of the underlying complexity of the quasi-periodic solar dynamo mechanism, the...

152. Upgrading PlaNET: A Deep Neural Network for Searching for Transiting Exoplanets with the Next Generation Transit Survey

Alexander Chaushev (University of California Irvine)

31/05/2022, 16:10

Oral Presentation

Exoplanet transit surveys produce flux time-series for
hundreds of thousands of stars to search for the tell-tale signs of a transiting planet. In the process, they provide a rich dataset for the application of machine learning (ML) methods. One focus so far has been the classification of exoplanet signals as genuine or instrumental false positives, particularly by using deep neural networks....