Phenomenology 2022 Symposium: From Virtual to Real

Session

Tools II

10 May 2022, 14:00

Lawrence Hall 203

Conveners

Tools II

• Yiming Zhong (University of Chicago)

207. Classifying Anomalies THrough Outer Density Estimation

Anna Hallin

10/05/2022, 14:00

Despite countless searches at the Large Hadron Collider (LHC), new physics remains elusive. The majority of these searches are highly model specific, requiring both background and signal simulation. In recent years, many anomaly detection methods have been proposed that use machine learning to enhance resonance searches without specifying a particular signal hypothesis. In this talk, I will...

187. I - Machine-Learning quantum entanglement with top quark pair production at the LHC

Zhongtian Dong

10/05/2022, 14:15

The top quark spin information is highly correlated with the final state lepton polarization, making the dileptonic $t\bar{t}$ events good candidates to study quantum entanglement at the LHC. The $t\bar{t}$ momentum reconstruction is a key ingredient to accurately assessing such measurements. I will be comparing the strengths and weaknesses of different top-quark momentum reconstruction...

192. II - Machine-Learning quantum entanglement with top quark pair production at the LHC

Alberto Navarro

10/05/2022, 14:30

We present the projections to probe quantum entanglement in top pair events at the LHC. We discuss a necessary and sufficient condition to define entanglement for the $t\bar t$ events, focusing on the dileptonic final state. This study scrutinizes the performance of different reconstruction algorithms, including some machine learning-based methods, in searching for entanglement. We show that...

185. MADderHAT: Weighting the Model-Agnostic Dark Halo Analysis Tool

Mr Zachary Carter (University of Utah)

10/05/2022, 14:45

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,...

183. Machine learning the Higgs-top CP Measurement

Rahool Kumar Barman (Oklahoma State University)

10/05/2022, 15:00

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...

176. Jet SIFT-ing: A Scale Invariant Jet Clustering Algorithm for the Substructure Era

Prof. Joel Walker (Sam Houston State University)

10/05/2022, 15:15

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...

175. Jet tagging with deep sets of subjets

Mr Dimitrios Athanasakos (YITP, Stony Brook)

10/05/2022, 15:30

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...