Title: Enhancing X-ray Analysis with machine learning

Speaker@ Dr Maggie Lieu, Nottingham University

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, analyzing 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 the existing datasets, enhancing the quality and utility of the data at our disposal. Furthermore, this discussion extends to the implementation of neural networks in analyzing X-ray spectra. This approach, as opposed to conventional fitting methods, enables the direct extraction of crucial physical parameters from astrophysical objects. With this, we are able to not only achieve accuracies comparable to traditional methods but also significantly accelerates the analysis process. The efficiency is critical for navigating the vast data landscapes anticipated in forthcoming X-ray surveys, an important step towards addressing the imminent big data challenges in astrophysics.