Rutgers University Astrophysics Seminar

Time domain astronomy will face big data challenges with operation of big surveys such as Rubin LSST and Roman. These challenges are being addressed by data science methods some of which are also applicable to other fields of science with large volumes of data. Having that big picture in mind, I am going to talk about two main projects that I have worked on involving developing methods to analyze astronomical time series. In the first project, I have developed automated algorithms to analyze and classify simulated stellar microlensing light curves for the future Roman Space mission. In the second project, I have used Gaussian Processes to generate photometric templates for striped-envelope supernovae collecting all the photometry that is available online. Finally, I will briefly talk about the work I am going to do as a LSSTC Catalyst Fellow for which I will use data science and artificial intelligence methods to address an important problem in the field of quasar microlensing.