Astronomy Tea Talk

Enhancing X-ray Astronomy with machine learning

Speaker: Maggie Lieu (Univ of Nottingham, UK)

Title: Enhancing X-ray Astronomy with machine learning

Abstract:

Machine learning has been transformative in tackling complex problems in astrophysics, particularly within the realm of X-ray spectral analysis. Traditionally, like many other tasks in astronomy, analysing X-ray spectra from astronomical objects has heavily relied on manual processing and resource-intensive simulations. Here I will delve into how machine learning, particularly through the creation of sophisticated emulators, can sidestep the need for extensive simulations, thereby saving time without compromising the precision essential for astrophysical research. I will illustrate the application of deep learning techniques to augment existing datasets, enhancing the quality and utility of the data at our disposal. Furthermore, I will extend the discussion to the implementation of other applications of machine learning in X-ray astronomy.

Event Sponsors:

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For more information, please contact Peter Boorman by phone at [email protected] or visit Zoom link.