1 code implementation • 29 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.
no code implementations • 18 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.
1 code implementation • 30 Nov 2023 • Joschka Birk, Erik Buhmann, Cedric Ewen, Gregor Kasieczka, David Shih
We introduce the first generative model trained on the JetClass dataset.
no code implementations • 18 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.
no code implementations • 29 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.
1 code implementation • 22 Aug 2023 • Ian Pang, John Andrew Raine, David Shih
In this work, we introduce SuperCalo, a flow-based super-resolution model, and demonstrate that high-dimensional fine-grained calorimeter showers can be quickly upsampled from coarse-grained showers.
no code implementations • 19 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.
1 code implementation • 5 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
no code implementations • 30 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.
no code implementations • 25 Oct 2022 • Claudius Krause, Ian Pang, David Shih
CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows.
no code implementations • 15 Mar 2022 • Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis, Michael Kagan, Gregor Kasieczka, Claudius Krause, David Shih, Vinicius Mikuni, Benjamin Nachman, Kevin Pedro, Daniel Winklehner
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
no code implementations • 7 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.
1 code implementation • 11 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.
2 code implementations • 21 Oct 2021 • Claudius Krause, David Shih
Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows.
no code implementations • 6 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.
2 code implementations • 9 Jun 2021 • Claudius Krause, David Shih
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows.
no code implementations • 5 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.
2 code implementations • 3 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.
1 code implementation • 14 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.
1 code implementation • 14 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.
1 code implementation • 13 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
2 code implementations • 28 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
2 code implementations • 18 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