240 citations · 670 across the 17 of their papers we have counts for
18 papers
A Quenched and Relatively Isolated Dwarf Galaxy in the Local Volume
Tehreem N. Hai, Kristen B. W. McQuinn, Yao-Yuan Mao +7
An increasing number of discoveries of isolated and quenched dwarf galaxies are challenging the idea that the present-day local environment of low-mass systems is the main determin…
SkyCURTAINs: Model agnostic search for Stellar Streams with Gaia data
Debajyoti Sengupta, Stephen Mulligan, David Shih +2
We present SkyCURTAINs, a data driven and model agnostic method to search for stellar streams in the Milky Way galaxy using data from the Gaia telescope. SkyCURTAINs is a weakly su…
Combining Resonant and Tail-based Anomaly Detection
Gerrit Bickendorf, Manuel Drees, Gregor Kasieczka +2
In many well-motivated models of the electroweak scale, cascade decays of new particles can result in highly boosted hadronic resonances (e.g. ). This can make these models…
Calorimeter shower superresolution
Ian Pang, John Andrew Raine, David Shih
Calorimeter shower simulation is a major bottleneck in the Large Hadron Collider computational pipeline. There have been recent efforts to employ deep-generative surrogate models t…
Artificial Intelligence for the Electron Ion Collider (AI4EIC)
C. Allaire, R. Ammendola, E. -C. Aschenauer +92
The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune t…
The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
Tobias Golling, Gregor Kasieczka, Claudius Krause +6
Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD metho…
Weakly-Supervised Anomaly Detection in the Milky Way
Mariel Pettee, Sowmya Thanvantri, Benjamin Nachman +3
Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searc…
How to Understand Limitations of Generative Networks
Ranit Das, Luigi Favaro, Theo Heimel +3
Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustr…
Inductive Simulation of Calorimeter Showers with Normalizing Flows
Matthew R. Buckley, Claudius Krause, Ian Pang +1
Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline. Recently it was shown that normalizing flows can accele…
Via Machinae 2.0: Full-Sky, Model-Agnostic Search for Stellar Streams in Gaia DR2
David Shih, Matthew R. Buckley, Lina Necib
We present an update to Via Machinae, an automated stellar stream-finding algorithm based on the deep learning anomaly detector ANODE. Via Machinae identifies stellar streams withi…
L2LFlows: Generating High-Fidelity 3D Calorimeter Images
Sascha Diefenbacher, Engin Eren, Frank Gaede +4
We explore the use of normalizing flows to emulate Monte Carlo detector simulations of photon showers in a high-granularity electromagnetic calorimeter prototype for the Internatio…
Feature Selection with Distance Correlation
Ranit Das, Gregor Kasieczka, David Shih
Choosing which properties of the data to use as input to multivariate decision algorithms -- a.k.a. feature selection -- is an important step in solving any problem with machine le…
Resonant anomaly detection without background sculpting
Anna Hallin, Gregor Kasieczka, Tobias Quadfasel +2
We introduce a new technique named Latent CATHODE (LaCATHODE) for performing "enhanced bump hunts", a type of resonant anomaly search that combines conventional one-dimensional bum…
CaloFlow for CaloChallenge Dataset 1
Claudius Krause, Ian Pang, David Shih
CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows. Applying CaloFlow to the photon and charged pion Geant4 showers of Dataset 1 of…
Anomaly Detection under Coordinate Transformations
Gregor Kasieczka, Radha Mastandrea, Vinicius Mikuni +3
There is a growing need for machine learning-based anomaly detection strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at the Large Hadron Collider (LHC)…
Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka +4
The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set…
Classifying Anomalies THrough Outer Density Estimation (CATHODE)
Anna Hallin, Joshua Isaacson, Gregor Kasieczka +6
We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at the LHC, based on a novel application of neural density estimation to anomaly detecti…
The Machine Learning Landscape of Top Taggers
G. Kasieczka, T. Plehn, A. Butter +24
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established metho…