| Speaker: | Leander Thiele (Princeton University) |
|---|---|
| Title: | Advancing Cosmology with Robust ML |
| Date (JST): | Thu, Sep 21, 2023, 11:00 - 12:00 |
| Place: | Lecture Hall |
| Abstract: |
Upcoming cosmological data sets will constitute a qualitative leap in statistical power and, consequently, sensitivity to complicated systematics. The potential scientific return could not be more exciting: from neutrino mass to dark energy to our universe's primordial matter distribution, cosmological information could bear on fundamental physics across a wide range of energy scales. Unlocking this potential, however, will hinge on our ability to accurately extract the richest information from the measurements. The non-linear character of the late-time density field calls for novel analysis methods, often involving analytically intractable probability distributions. Furthermore, great care will be required in modeling the formation and evolution of galaxies as well as the phenomenology of violent small-scale energy release.I will demonstrate how the past decade of rapid advancements in machine learning sets the stage for performant and robust methods that will be instrumental in meeting these challenges. |
| Remarks: | Lecture Hall + Zoom |
