International Conference on Machine Learning for Astrophysics - ML4Astro

Session

Anomaly Detection

31 May 2022, 11:35

Catania

Conveners

Anomaly Detection

• Claudio Gheller (Istituto Nazionale di Astrofisica (INAF))

16. In Search of the Peculiar: An Unsupervised Approach to Anomaly Detection in the Transient Universe

Dennis Crake (Institute for Astronomy, University of Edinburgh)

31/05/2022, 11:35

Oral Presentation

The era of big data time-domain Astronomy is here, and with planned projects such as the Vera-Rubin Telescope, the scale of the data available is escalating at an astonishing pace. Perhaps, the most scientifically promising aspect of these surveys is their potential for discovery across the transient universe. Nonetheless, current methods are restricting the potential for discovery due to...

15. Searching for changing-state AGNs in massive datasets with anomaly detection

Paula Sanchez Saez (ESO)

31/05/2022, 11:55

Oral Presentation

The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods...

163. Predicting and detecting very-high-energy blazar flares with deep learning

Hermann Stolte (Humboldt University Berlin)

31/05/2022, 12:15

Oral Presentation

Blazars are a subclass of active galactic nuclei (AGNs) with relativistic jets pointing toward the observer. They are notable for their flux variability at all observed wavelengths and time scales. The very-high-energy (VHE) emission observed during blazar flares may be used to probe the population of accelerated particles, together with simultaneous measurements at lower energies. However,...

14. Galaxy Zoo: Practical Methods for Large-Scale Learning

Mike Walmsley (University of Manchester)

31/05/2022, 12:35

Oral Presentation

Deep learning is fundamental to creating Galaxy Zoo’s latest catalogs. In this talk, we explore the methods we’ve developed to best exploit large-scale human labels and how other researchers can benefit from them.

We open by presenting Galaxy Zoo LegS - new deep-learning-powered detailed morphology measurements for 8 million galaxies imaged by the DESI Legacy Surveys. Our models are trained...

59. Galaxy Zoo: Practical Methods for Large-Scale Learning

Mike Walmsley (University of Manchester)

Oral Presentation

Deep learning is fundamental to creating Galaxy Zoo’s latest catalogs. In this talk, we explore the methods we’ve developed to best exploit large-scale human labels and how other researchers can benefit from them.

We open by presenting Galaxy Zoo LegS - new deep-learning-powered detailed morphology measurements for 8 million galaxies imaged by the DESI Legacy Surveys. Our models are trained...

89. In Search of the Peculiar: An Unsupervised Approach to Anomaly Detection in the Transient Universe.

Mr Dennis Crake (Institute for Astronomy, University of Edinburgh)

Oral Presentation

The era of big data time-domain Astronomy is here, and with planned projects such as the Vera-Rubin Telescope, the scale of the data available is escalating at an astonishing pace. Perhaps, the most scientifically promising aspect of these surveys is their potential for discovery across the transient universe. Nonetheless, current methods are restricting the potential for discovery due to...

100. Predicting and detecting very-high-energy blazar flares with deep learning

Hermann Stolte (Humboldt University Berlin)

Oral Presentation

Blazars are a subclass of active galactic nuclei (AGNs) with relativistic jets pointing toward the observer. They are notable for their flux variability at all observed wavelengths and time scales. The very-high-energy (VHE) emission observed during blazar flares may be used to probe the population of accelerated particles, together with simultaneous measurements at lower energies. However,...

50. Searching for changing-state AGNs in massive datasets with anomaly detection

Paula Sanchez Saez (ESO)

Oral Presentation

The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods...