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Astronomical transients and advancements using machine learning

Dr Anais Möller 

CAASTRO Postdoctoral Fellow in Dark Energy

ANU College of Physical and Mathematical Sciences

Astronomical transients are phenomena that can last from seconds to months. Due to their time-dependent nature, dedicated surveys require fast data processing for their discovery and classification. Recently, many advancements have been strongly  linked to machine learning applications. First, for the discovery of transient we require not only quick and efficient processing of telescope images but also distinguishing real events from artefacts and noise. Second, once objects are detected we must classify them in different astrophysical events with the available information for further study and use. For example, there are many types of exploding stars called supernovae but type Ia supernovae are used as cosmological probes for the expansion of the Universe. Due to the large quantity of supernovae we are finding, efforts are being done for improving current classification methods using spectra and developing new methods for classification with partial information, so-called photometric classification. In this talk we will explore the role machine learning is playing on the field of optical transients, in particular with supernovae, and the challenges we have and expect for the future.


2015: PhD / Doctor de physique de l'Univers. Université Paris Diderot (Paris 7) & Cea Saclay. France. 

2012: Master Noyaux, Particules, Astroparticules et Cosmologie (NPAC). Université Paris Diderot (Paris 7). France.

2011: Licenciada en física. Univerdad Simón Bolívar. Venezuela.

Date & time

4–5.30pm 10 May 2017


Room: N101 seminar room


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