Research – David Shih

Interests

Machine learning applications to fundamental physics; physics beyond the Standard Model; dark matter; collider phenomenology; astroparticle physics

My research aims to answer longstanding questions which remain unanswered by the Standard Model. Questions such as what is the nature of dark matter? Why is gravity the weakest force? Why are there three generations of quarks and leptons?

In my work, I have approached these questions from a variety of angles, including model building and phenomenology. More recently, I have become interested in the power of modern machine learning to greatly enhance the search for new physics.

Below you will find my recent papers on machine learning for HEP and astronomy, organized into various topics.

Machine Learning Applications to HEP

Full Phase Space Resonant Anomaly Detection

Erik Buhmann, Cedric Ewen, Gregor Kasieczka, Vinicius Mikuni, Benjamin Nachman, David Shih
Combining Resonant and Tail-based Anomaly Detection

Gerrit Bickendorf, Manuel Drees, Gregor Kasieczka, Claudius Krause, David Shih
Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection

Thorben Finke, Marie Hein, Gregor Kasieczka, Michael Krämer, Alexander Mück, Parada Prangchaikul, Tobias Quadfasel, David Shih, Manuel Sommerhalder
The Interplay of Machine Learning–based Resonant Anomaly Detection Methods

Tobias Golling, Gregor Kasieczka, Claudius Krause, Radha Mastandrea, Benjamin Nachman, John Andrew Raine, Debajyoti Sengupta, David Shih, Manuel Sommerhalder
Resonant anomaly detection without background sculpting

Anna Hallin, Gregor Kasieczka, Tobias Quadfasel, David Shih, Manuel Sommerhalder
Anomaly Detection under Coordinate Transformations

Gregor Kasieczka, Radha Mastandrea, Vinicius Mikuni, Benjamin Nachman, Mariel Pettee, David Shih
Online-compatible Unsupervised Non-resonant Anomaly Detection

Vinicius Mikuni, Benjamin Nachman, David Shih
Classifying Anomalies THrough Outer Density Estimation (CATHODE)

Anna Hallin, Joshua Isaacson, Gregor Kasieczka, Claudius Krause, Benjamin Nachman, Tobias Quadfasel, Matthias Schlaffer, David Shih, Manuel Sommerhalder
Comparing Weak and Unsupervised Methods for Resonant Anomaly Detection

Jack H. Collins, Pablo Martín-Ramiro, Benjamin Nachman, David Shih
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

Gregor Kasieczka, Benjamin Nachman, David Shih (eds) et al
Anomaly Detection with Density Estimation

Benjamin Nachman, David Shih
Simulation Assisted Likelihood-free Anomaly Detection

Anders Andreassen, Benjamin Nachman, David Shih

EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion

Erik Buhmann, Cedric Ewen, Darius A. Faroughy, Tobias Golling, Gregor Kasieczka, Matthew Leigh, Guillaume Quétant, John Andrew Raine, Debajyoti Sengupta, David Shih
SuperCalo: Calorimeter shower super-resolution

Ian Pang, John Andrew Raine, David Shih
How to Understand Limitations of Generative Networks

Ranit Das, Luigi Favaro, Theo Heimel, Claudius Krause, Tilman Plehn, David Shih
Inductive CaloFlow

Matthew R. Buckley, Claudius Krause, Ian Pang, David Shih
L2LFlows: Generating High-Fidelity 3D Calorimeter Images

Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Claudius Krause, Imahn Shekhzadeh, David Shih
CaloFlow for CaloChallenge Dataset 1

Claudius Krause, Ian Pang, David Shih
CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows

Claudius Krause, David Shih
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows

Claudius Krause, David Shih
DCTRGAN: Improving the Precision of Generative Models with Reweighting

Sascha Diefenbacher, Engin Eren, Gregor Kasieczka, Anatolii Korol, Benjamin Nachman, David Shih

ABCDisCo: Automating the ABCD Method with Machine LearningGregor Kasieczka, Benjamin Nachman, Matthew D. Schwartz, David Shih
DisCo Fever: Robust Networks Through Distance CorrelationGregor Kasieczka, David Shih

Feature Selection with Distance Correlation

Ranit Das, Gregor Kasieczka, David Shih

Machine Learning for Astronomy

Weakly-Supervised Anomaly Detection in the Milky WayMariel Pettee, Sowmya Thanvantri, Benjamin Nachman, David Shih, Matthew R. Buckley, Jack H. Collins
Via Machinae 2.0: Full-Sky, Model-Agnostic Search for Stellar Streams in Gaia DR2David Shih, Matthew R. Buckley, Lina Necib
Via Machinae: Searching for Stellar Streams using Unsupervised Machine LearningDavid Shih, Matthew R. Buckley, Lina Necib, John Tamanas

Mapping Dark Matter in the Milky Way using Normalizing Flows and Gaia DR3

Sung Hak Lim, Eric Putney, Matthew R. Buckley, David Shih
Measuring Galactic Dark Matter through Unsupervised Machine Learning

Matthew R Buckley, Sung Hak Lim, Eric Putney, David Shih

GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Gaia Mock Catalogs

Sung Hak Lim, Kailash A. Raman, Matthew R. Buckley, David Shih

Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows

David Shih, Marat Freytsis, Stephen R. Taylor, Jeff A. Dror, Nolan Smyth