Search Results for author:
Found 22 papers, 12 papers with code
Residual ANODE
We present R-ANODE, a new method for data-driven, model-agnostic resonant anomaly detection that raises the bar for both performance and interpretability.
Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
We introduce the first generative model trained on the JetClass dataset.
Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows
Pulsar timing arrays (PTAs) perform Bayesian posterior inference with expensive MCMC methods.
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
In addition, we introduce \epcfm, the first permutation equivariant continuous normalizing flow (CNF) for particle cloud generation.
SuperCalo: Calorimeter shower super-resolution
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.
Inductive Simulation of Calorimeter Showers with Normalizing Flows
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
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
Choosing which properties of the data to use as input to multivariate decision algorithms -- a. k. a.
CaloFlow for CaloChallenge Dataset 1
CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows.
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
Machine Learning in the Search for New Fundamental Physics
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics.
Online-compatible Unsupervised Non-resonant Anomaly Detection
There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner.
CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows.
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications.
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows.
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
We examine the ability of the two methods to identify a new physics signal at different cross sections in a fully hadronic resonance search.
DCTRGAN: Improving the Precision of Generative Models with Reweighting
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
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.
Simulation Assisted Likelihood-free Anomaly Detection
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.
DisCo Fever: Robust Networks Through Distance Correlation
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
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
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
