Conveners
Tools II
- Yiming Zhong (University of Chicago)
MADHAT (Model-Agnostic Dark Halo Analysis Tool) is a computational tool that processes data from Fermi Gamma-ray Space Telescope observations of dwarf galaxies and dwarf-like objects. MADHAT calculates the probability that some number of photons from each target object could be coming from non-standard astrophysics, including dark matter, and produces bounds on dark matter properties,...
We explore the direct Higgs-top CP structure via the $pp \to t\bar{t}h$ channel with machine learning techniques, considering the clean $h \to \gamma\gamma$ final state at the high luminosity LHC~(HL-LHC). We show that a combination of a comprehensive set of observables, that includes the $t\bar{t}$ spin-correlations, with mass minimization strategies to reconstruct the $t\bar{t}$ rest frame...
We describe a new jet clustering algorithm (SIFT: Scale-Invariant Filter Tree) that does not impose a fixed cone size or associated scale on the event. This construction maintains excellent object discrimination for very collimated partonic systems, tracks accrued mass, and asymptotically recovers favorable behaviors of both the standard KT and anti-KT algorithms. It is intrinsically suitable...
We introduce a minimal and complete basis of subjets for machine learning-based jet tagging. The momenta, relative angles, and masses of the identified subjets are taken as input to a neural network. By adjusting the subjet radius, we can control the sensitivity to nonperturbative physics. We construct permutation invariant neural networks, Jet Flow Networks (JFN), which take as input the...
