International Conference on Machine Learning for Astrophysics - ML4Astro

Session

Transients and Time Series

31 May 2022, 17:00

Catania

17. Finding stellar flares with recurrent deep neural networks

Attila Bodi (Konkoly Observatory)

31/05/2022, 17:00

Oral Presentation

At present, low-mass, cool M dwarfs are the prime targets of planet searches, since the habitable zone is much closer to the central object in cool stars than in the case of a solar-like star; thus, detecting a possibly habitable Earth-like planet is easier. However, the late spectral type of these stars and the magnetic activity associated with these could pose a threat to habitability. To...

19. Classifying Supernova Time-Series Photometry with Convolutional Neural Networks

Helen Qu

31/05/2022, 17:20

Oral Presentation

The use of type Ia supernovae (SNe Ia) as standardizable candles led to the Nobel Prize-winning discovery of the accelerating expansion of the universe and cemented their role in the quest to understand the nature of dark energy. Accurate cosmological parameter estimation requires a sample of pure SNe Ia with minimal non-Ia contamination, but spectroscopic confirmation of supernova type is...

164. An Unsupervised Dive Into Gamma-ray Burst Afterglow Classification

Eliot Ayache (The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691 Stockholm, Sweden)

31/05/2022, 17:40

Oral Presentation

The Neil Gehrels "Swift" Observatory has been detecting and measuring emission from gamma-ray bursts (GRBs) and their associated afterglow for the last 17 years. Today, over 1500 bursts have been observed, with light curves displaying different morphologies in the succession of decay regimes with time. We explore prospects for acquiring physical inference from machine-learning models by...

18. Data-driven Discovery of Supernovae in the New Era of Time-Domain Astronomy

Daniel Muthukrishna (Massachusetts Institute of Technology)

Oral Presentation

Time-domain astronomy has reached an incredible new era where unprecedented amounts of data are becoming available. New large-scale astronomical surveys such as the Legacy Survey of Space and Time (LSST) are going to revolutionise transient astronomy, providing opportunities to discover entirely new classes of transients while also enabling a deeper understanding of known supernovae. LSST is...

104. Data-driven Discovery of Supernovae in the New Era of Time-Domain Astronomy

Daniel Muthukrishna (Massachusetts Institute of Technology)

Oral Presentation

Time-domain astronomy has reached an incredible new era where unprecedented amounts of data are becoming available. New large-scale astronomical surveys such as the Legacy Survey of Space and Time (LSST) are going to revolutionise transient astronomy, providing opportunities to discover entirely new classes of transients while also enabling a deeper understanding of known supernovae. LSST is...

74. Finding stellar flares with recurrent deep neural networks

Dr Attila Bódi (Konkoly Observatory)

Oral Presentation

At present, low-mass, cool M dwarfs are the prime targets of planet searches, since the habitable zone is much closer to the central object in cool stars than in the case of a solar-like star; thus, detecting a possibly habitable Earth-like planet is easier. However, the late spectral type of these stars and the magnetic activity associated with these could pose a threat to habitability. To...

148. FORECASTING SOLAR CYCLE 25 AND NEXT SOLAR MINIMUM USING A MULTI-STEP BAYESIAN DEEP NEURAL NETWORK MODEL

Dr Ilaria Bizzarri (Dipartimento di Fisica, Università di Torino)

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...

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

Alexander Chaushev (University of California Irvine)

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...