Search Results for author: David Shih

Found 24 papers, 14 papers with code

Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows

1 code implementation30 May 2024 Thorsten Buss, Frank Gaede, Gregor Kasieczka, Claudius Krause, David Shih

In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial frontier.

Unifying Simulation and Inference with Normalizing Flows

1 code implementation29 Apr 2024 Haoxing Du, Claudius Krause, Vinicius Mikuni, Benjamin Nachman, Ian Pang, David Shih

There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators.


Residual ANODE

no code implementations18 Dec 2023 Ranit Das, Gregor Kasieczka, David Shih

We present R-ANODE, a new method for data-driven, model-agnostic resonant anomaly detection that raises the bar for both performance and interpretability.

Anomaly Detection Inductive Bias

Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows

no code implementations18 Oct 2023 David Shih, Marat Freytsis, Stephen R. Taylor, Jeff A. Dror, Nolan Smyth

Pulsar timing arrays (PTAs) perform Bayesian posterior inference with expensive MCMC methods.

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

no code implementations29 Sep 2023 Erik Buhmann, Cedric Ewen, Darius A. Faroughy, Tobias Golling, Gregor Kasieczka, Matthew Leigh, Guillaume Quétant, John Andrew Raine, Debajyoti Sengupta, David Shih

In addition, we introduce \epcfm, the first permutation equivariant continuous normalizing flow (CNF) for particle cloud generation.

Calorimeter shower superresolution

1 code implementation22 Aug 2023 Ian Pang, John Andrew Raine, David Shih

In this work, we introduce SuperCalo, a flow-based superresolution model, and demonstrate that high-dimensional fine-grained calorimeter showers can be quickly upsampled from coarse-grained showers.


Inductive Simulation of Calorimeter Showers with Normalizing Flows

no code implementations19 May 2023 Matthew R. Buckley, Claudius Krause, Ian Pang, David Shih

Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline.

Weakly-Supervised Anomaly Detection in the Milky Way

1 code implementation5 May 2023 Mariel Pettee, Sowmya Thanvantri, Benjamin Nachman, David Shih, Matthew R. Buckley, Jack H. Collins

Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches.

Supervised Anomaly Detection Weakly-supervised Anomaly Detection

Feature Selection with Distance Correlation

no code implementations30 Nov 2022 Ranit Das, Gregor Kasieczka, David Shih

Choosing which properties of the data to use as input to multivariate decision algorithms -- a. k. a.

Automated Feature Engineering Feature Engineering +1

CaloFlow for CaloChallenge Dataset 1

no code implementations25 Oct 2022 Claudius Krause, Ian Pang, David Shih

CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows.

Machine Learning in the Search for New Fundamental Physics

no code implementations7 Dec 2021 Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz, Benjamin Nachman, David Shih

Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics.

BIG-bench Machine Learning

Online-compatible Unsupervised Non-resonant Anomaly Detection

1 code implementation11 Nov 2021 Vinicius Mikuni, Benjamin Nachman, David Shih

There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner.

Anomaly Detection

CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows

2 code implementations21 Oct 2021 Claudius Krause, David Shih

Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows.

Speech Synthesis

New Methods and Datasets for Group Anomaly Detection From Fundamental Physics

no code implementations6 Jul 2021 Gregor Kasieczka, Benjamin Nachman, David Shih

The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications.

Group Anomaly Detection

CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows

2 code implementations9 Jun 2021 Claudius Krause, David Shih

We introduce CaloFlow, a fast detector simulation framework based on normalizing flows.

Model Selection

Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection

no code implementations5 Apr 2021 Jack H. Collins, Pablo Martín-Ramiro, Benjamin Nachman, David Shih

We examine the ability of the two methods to identify a new physics signal at different cross sections in a fully hadronic resonance search.

Anomaly Detection

DCTRGAN: Improving the Precision of Generative Models with Reweighting

2 code implementations3 Sep 2020 Sascha Diefenbacher, Engin Eren, Gregor Kasieczka, Anatolii Korol, Benjamin Nachman, David Shih

We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (DCTR) protocol.

Anomaly Detection with Density Estimation

1 code implementation14 Jan 2020 Benjamin Nachman, David Shih

By estimating the probability density of the data in a signal region and in sidebands, and interpolating the latter into the signal region, a likelihood ratio of data vs. background can be constructed.

Density Estimation Unsupervised Anomaly Detection

Simulation Assisted Likelihood-free Anomaly Detection

1 code implementation14 Jan 2020 Anders Andreassen, Benjamin Nachman, David Shih

For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands.

Anomaly Detection MORPH

DisCo Fever: Robust Networks Through Distance Correlation

1 code implementation13 Jan 2020 Gregor Kasieczka, David Shih

While deep learning has proven to be extremely successful at supervised classification tasks at the LHC and beyond, for practical applications, raw classification accuracy is often not the only consideration.

High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability

Pulling Out All the Tops with Computer Vision and Deep Learning

2 code implementations28 Feb 2018 Sebastian Macaluso, David Shih

We apply computer vision with deep learning -- in the form of a convolutional neural network (CNN) -- to build a highly effective boosted top tagger.

High Energy Physics - Phenomenology High Energy Physics - Experiment

Digging Deeper for New Physics in the LHC Data

2 code implementations18 Jul 2017 Pouya Asadi, Matthew R. Buckley, Anthony DiFranzo, Angelo Monteux, David Shih

In this paper we describe a novel, model-independent technique of "rectangular aggregations" for mining the LHC data for hints of new physics.

High Energy Physics - Phenomenology High Energy Physics - Experiment

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