150 citations · 970 across the 31 of their papers we have counts for
32 papers
Neural Embedding: Learning the Embedding of the Manifold of Physics Data
Sang Eon Park, Philip Harris, Bryan Ostdiek
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 sp…
Revealing the Milky Way's Most Recent Major Merger with a Gaia EDR3 Catalog of Machine-Learned Line-of-Sight Velocities
Adriana Dropulic, Hongwan Liu, Bryan Ostdiek +1
Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. In this paper, we apply a neural network to Gaia Ea…
Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure
Layne Bradshaw, Spencer Chang, Bryan Ostdiek
Anomaly detection with convolutional autoencoders is a popular method to search for new physics in a model-agnostic manner. These techniques are powerful, but they are still a "bla…
Substructure Detection Reanalyzed: Dark Perturber shown to be a Line-of-Sight Halo
Atınç Çağan Şengül, Cora Dvorkin, Bryan Ostdiek +1
Observations of structure at sub-galactic scales are crucial for probing the properties of dark matter, which is the dominant source of gravity in the universe. It will become incr…
Challenges for Unsupervised Anomaly Detection in Particle Physics
Katherine Fraser, Samuel Homiller, Rashmish K. Mishra +2
Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use au…
Deep Set Auto Encoders for Anomaly Detection in Particle Physics
Bryan Ostdiek
There is an increased interest in model agnostic search strategies for physics beyond the standard model at the Large Hadron Collider. We introduce a Deep Set Variational Autoencod…
Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations
Adriana Dropulic, Bryan Ostdiek, Laura J. Chang +3
The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D p…
Parameter Inference from Event Ensembles and the Top-Quark Mass
Forrest Flesher, Katherine Fraser, Charles Hutchison +2
One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the…
Image segmentation for analyzing galaxy-galaxy strong lensing systems
Bryan Ostdiek, Ana Diaz Rivero, Cora Dvorkin
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 len…
Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
Bryan Ostdiek, Ana Diaz Rivero, Cora Dvorkin
Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally require…
On the ATLAS Top Mass Measurements and the Potential for Stealth Stop Contamination
Timothy Cohen, Stephanie Majewski, Bryan Ostdiek +1
The discovery of the stop - the Supersymmetric partner of the top quark - is a key goal of the physics program enabled by the Large Hadron Collider. Although much of the accessible…
Mass Agnostic Jet Taggers
Layne Bradshaw, Rashmish K. Mishra, Andrea Mitridate +1
Searching for new physics in large data sets needs a balance between two competing effects---signal identification vs background distortion. In this work, we perform a systematic s…
Chasing Accreted Structures within Gaia DR2 using Deep Learning
Lina Necib, Bryan Ostdiek, Mariangela Lisanti +3
In previous work, we developed a deep neural network classifier that only relies on phase-space information to obtain a catalog of accreted stars based on the second data release o…
Evidence for a Vast Prograde Stellar Stream in the Solar Vicinity
Lina Necib, Bryan Ostdiek, Mariangela Lisanti +6
Massive dwarf galaxies that merge with the Milky Way on prograde orbits can be dragged into the disk plane before being completely disrupted. Such mergers can contribute to an accr…
Cataloging Accreted Stars within Gaia DR2 using Deep Learning
Bryan Ostdiek, Lina Necib, Timothy Cohen +6
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…
Dark Mesons at the LHC
Graham D. Kribs, Adam Martin, Bryan Ostdiek +1
A new, strongly-coupled dark sector could be accessible to LHC searches now. These dark sectors consist of composites formed from constituents that are charged under the electrowea…
Magnifying the ATLAS Stealth Stop Splinter: Impact of Spin Correlations and Finite Widths
Timothy Cohen, Walter Hopkins, Stephanie Majewski +1
In this paper, we recast a "stealth stop" search in the notoriously difficult region of the stop-neutralino Simplified Model parameter space for which $m(\tilde{t}) - m(\tildeχ) \s…
What is the Machine Learning?
Spencer Chang, Timothy Cohen, Bryan Ostdiek
Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we e…
(Machine) Learning to Do More with Less
Timothy Cohen, Marat Freytsis, Bryan Ostdiek
Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervis…
Don't be left in the dark: Improving LHC searches for dark photons using lepton-jet substructure
G. Barello, Spencer Chang, Christopher A. Newby +1
Collider signals of dark photons are an exciting probe for new gauge forces and are characterized by events with boosted lepton jets. Existing techniques are efficient in searching…
Physics at a 100 TeV pp collider: beyond the Standard Model phenomena
T. Golling, M. Hance, P. Harris +111
This report summarises the physics opportunities in the search and study of physics beyond the Standard Model at a 100 TeV pp collider.
750 GeV Diphotons from Supersymmetry with Dirac Gauginos
Timothy Cohen, Graham D. Kribs, Ann E. Nelson +1
Motivated by the recent excess in the diphoton invariant mass near 750 GeV, we explore a supersymmetric extension of the Standard Model that includes the minimal set of superpartne…
Towards the Final Word on Neutralino Dark Matter
Joseph Bramante, Nishita Desai, Patrick Fox +3
We present a complete phenomenological prospectus for thermal relic neutralinos. Including Sommerfeld enhancements to relic abundance and halo annihilation calculations, we obtain…
Constraining the minimal dark matter fiveplet with LHC searches
Bryan Ostdiek
Adding a fermion to the standard model particle content which is a fiveplet under gives a dark matter candidate. The lightest state in the multiplet is neutral and automa…