Mining for Dark Matter substructure: Learning from lenses without a likelihood
- 2020年2月17日14:00 - 15:30 (JST)
- Dr. Johann Brehmer (Postdoctoral Researcher New York University) Edit
Dr. Brehmer gives us a talk about a method to deduce DM small structures. Please join us!
The subtle imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the small-scale distribution of dark matter and, consequently, about the underlying particle physics. However, teasing out this effect is challenging since the likelihood function for realistic simulations of population-level parameters is intractable. Structurally similar problems appear in many other scientific fields ranging from particle physics to neuroscience to epidemiology, which has prompted the development of powerful simulation-based inference techniques based on machine learning. We give a broad overview over these methods, and then apply them to the problem of substructure inference in galaxy-galaxy strong lenses. In this proof-of-principle application to simulated data, we show that these methods can provide an efficient and principled way to simultaneously analyze an ensemble of strong lenses, and can be used to mine the large sample of lensing images deliverable by near-future surveys for signatures of dark matter substructure.