Search Results for author:
Found 9 papers, 3 papers with code
Neural Embedding: Learning the Embedding of the Manifold of Physics Data
In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic spaces.
Challenges for Unsupervised Anomaly Detection in Particle Physics
We then show that optimal transport distances to representative events in the background dataset can be used directly for anomaly detection, with performance comparable to the autoencoders.
Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio.
Image segmentation for analyzing galaxy-galaxy strong lensing systems
The goal of this paper is to develop a machine learning model to analyze the main gravitational lens and detect dark substructure (subhalos) within simulated images of strongly lensed galaxies.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology Data Analysis, Statistics and Probability
Mass Agnostic Jet Taggers
Searching for new physics in large data sets needs a balance between two competing effects---signal identification vs background distortion.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Cataloging Accreted Stars within Gaia DR2 using Deep Learning
The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ.
Dark Mesons at the LHC
In this paper we study dark meson production and decay at the LHC in theories that preserve a global SU(2) dark flavor symmetry.
High Energy Physics - Phenomenology
What is the Machine Learning?
Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency.
(Machine) Learning to Do More with Less
In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training.
