ML4GW Classification of BBH & BNS

• Arif Chu (University of Washington)
• Xinyi Liu

Room: PAB 6th floor Gravitational waves are a fundamental prediction of Einstein’s theory of General Relativity, in which the fabric of spacetime (x, y, z, t) is altered due to masses. These waves are generated by accelerating massive objects, such as two merging black holes (BBH) or neutron stars (BNS). When the objects merge they create disturbances in spacetime that propagate as gravitational waves. Our objective is to develop and optimize a 4-layer ResNet model that can quickly and accurately identify BBH and BNS signals from typical detector noise.

Machine Learning for Top Tagging

• Tristan Kay
• Yuuki Sawanoi (University of Washington (US))

Room: PAB 6th floor We survey top jet classification perforamcne in a variety of deep learning models, Convolutional Neural Network, Particle Flow Network, Dense Neural Network and ParticleNet.

Semi-Visible Jet Classification with Boosted Decision Tree

• Hebu Patil
• Hebu Patil

Room: PAB 6th floor Particle colliders record large amounts of data on jets produced from proton- proton collisions. Some jets are described by the Standard Model, but some jets contain Dark Matter that cannot be described. Goal: Achieve higher ratio of signal to background by discriminating Semi- Visible Jets from Standard Model jets using the Boosted Decision Tree.

Sparse Point-Voxel CNN

• Lydia Maximovna Mazeeva (University of Washington (US))
• Yuuki Sawanoi (University of Washington (US))
• William Yao Ze Lai Feng
• Lydia Mazeeva

Room: PAB 6th floor Collisions in a collider create multiple types of particles, each of which creates a shower (in the EM calorimeter, Bremsstrahlung). These showers are measured in calorimeters. We want to associate each shower particle with a tau lepton source (or other noise). The [ast approaches. Point-cloud Network, wasted runtime. We studied combination of Voxel-based neural network and Sparse Convolution to optimize information loss with point-based branch.

Using cVAE for Anomaly Detection

• Vincent Chen
• Vincent Chen

Room: PAB 6th floor Wr are looking for model-independent way to search for new physics. Since we don’t know which laws new physics will follow. We are Interested in over-density anomalies. We use a model that tries to learn probability density of certain features to look for anomalies from the expected distribution. We will use LHC Olympics 2020 dataset to study this approach.

Cleaning gravitational waves - Old and New Data Analysis

• Gaurang Pendharkar (Interlake High School)
• Ahad Ather

Room: PAB 6th floor The first gravitation wave (GW150914) was detected in 2015 by LIGO & Virgo interferometers. The GW detectors measure strain and auxiliary data to output time-series waves description disturbances/distortions in space-time. However, it encounters high background noise, low gain GW signal from interferometer strain output. We studied new machine learning based method, DeepClean, for denoising. We will show effectiveness of this method in comparison to conventional matched filtering method.