Publications (131)
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…
Cornering Natural SUSY at LHC Run II and Beyond
Matthew R. Buckley, David Feld, Sebastian Macaluso +2
We derive the latest constraints on various simplified models of natural SUSY with light higgsinos, stops and gluinos, using a detailed and comprehensive reinterpretation of the mo…
Precision Corrections to Fine Tuning in SUSY
Matthew R. Buckley, Angelo Monteux, David Shih
Requiring that the contributions of supersymmetric particles to the Higgs mass are not highly tuned places upper limits on the masses of superpartners -- in particular the higgsino…
Generator Based Inference (GBI)
Chi Lung Cheng, Ranit Das, Runze Li +5
Statistical inference in physics is often based on samples from a generator (sometimes referred to as a ``forward model") that emulate experimental data and depend on parameters of…
DCTRGAN: Improving the Precision of Generative Models with Reweighting
Sascha Diefenbacher, Engin Eren, Gregor Kasieczka +3
Significant advances in deep learning have led to more widely used and precise neural network-based generative models such as Generative Adversarial Networks (GANs). We introduce a…
Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows
David Shih, Marat Freytsis, Stephen R. Taylor +2
Pulsar timing arrays (PTAs) perform Bayesian posterior inference with expensive MCMC methods. Given a dataset of ~10-100 pulsars and O(10^3) timing residuals each, producing a post…
General Gauge Mediation at the Weak Scale
Simon Knapen, Diego Redigolo, David Shih
We completely characterize General Gauge Mediation (GGM) at the weak scale by solving all IR constraints over the full parameter space. This is made possible through a combination…
SIGMA: Single Interpolated Generative Model for Anomalies
Ranit Das, David Shih
A key step in any resonant anomaly detection search is accurate modeling of the background distribution in each signal region. Data-driven methods like CATHODE accomplish this by t…
General Neutralino NLSPs at the Early LHC
Joshua T. Ruderman, David Shih
Gauge mediated supersymmetry breaking (GMSB) is a theoretically well-motivated framework with rich and varied collider phenomenology. In this paper, we study the Tevatron limits an…
CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
Claudius Krause, David Shih
Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows. Here, we present CaloFlow v2, an improvement…
Digging Deeper for New Physics in the LHC Data
Pouya Asadi, Matthew R. Buckley, Anthony DiFranzo +2
In this paper we describe a novel, model-independent technique of "rectangular aggregations" for mining the LHC data for hints of new physics. A typical (CMS) search now has hundre…
Pulling Out All the Tops with Computer Vision and Deep Learning
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. Previous work (the "DeepTop" t…
An Update on the LHC Monojet Excess
Pouya Asadi, Matthew R. Buckley, Anthony DiFranzo +2
In previous work, we identified an anomalous number of events in the LHC jets+MET searches characterized by low jet multiplicity and low-to-moderate transverse energy variables. He…
Resolving Combinatorial Ambiguities in Dilepton Event Topologies with Neural Networks
Haider Alhazmi, Zhongtian Dong, Li Huang +3
We study the potential of deep learning to resolve the combinatorial problem in SUSY-like events with two invisible particles at the LHC. As a concrete example, we focus on dilepto…
Learning to Unscramble: Simplifying Symbolic Expressions via Self-Supervised Oracle Trajectories
David Shih
We present a new self-supervised machine learning approach for symbolic simplification of complex mathematical expressions. Training data is generated by scrambling simple expressi…
Prompt Decays of General Neutralino NLSPs at the Tevatron
Patrick Meade, Matthew Reece, David Shih
Recent theoretical developments have shown that gauge mediation has a much larger parameter space of possible spectra and mixings than previously considered. Motivated by this, we…
Singularities of N=1 Supersymmetric Gauge Theory and Matrix Models
David Shih
In N=1 supersymmetric U(N) gauge theory with adjoint matter and polynomial tree-level superpotential , the massless fluctuations about each quantum vacuum are generical…
Notes on SUSY and R-Symmetry Breaking in Wess-Zumino Models
Zohar Komargodski, David Shih
We study aspects of Wess-Zumino models related to SUSY and R-symmetry breaking at tree-level. We present a recipe for constructing a wide class of tree-level SUSY and R-breaking mo…
Measuring Galactic Dark Matter through Unsupervised Machine Learning
Matthew R Buckley, Sung Hak Lim, Eric Putney +1
Measuring the density profile of dark matter in the Solar neighborhood has important implications for both dark matter theory and experiment. In this work, we apply autoregressive…
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…
General Gauge Mediation
Patrick Meade, Nathan Seiberg, David Shih
We give a general definition of gauge mediated supersymmetry breaking which encompasses all the known gauge mediation models. In particular, it includes both models with messengers…
Machine Learning in the Search for New Fundamental Physics
Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz +2
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new…
Strange Jet Tagging
Yuichiro Nakai, David Shih, Scott Thomas
Tagging jets of strongly interacting particles initiated by energetic strange quarks is one of the few largely unexplored Standard Model object classification problems remaining in…
Anomaly detection with flow-based fast calorimeter simulators
Claudius Krause, Benjamin Nachman, Ian Pang +2
Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using CaloFlow as an example, we show that t…
125 GeV Higgs from Tree-Level -terms
Aria Basirnia, Daniel Egana-Ugrinovic, Simon Knapen +1
We present a new mechanism to generate large -terms at tree-level in the MSSM through the use of superpotential operators. The mechanism trivially resolves the problem w…
Anomalous Dimensions of Non-Chiral Operators from AdS/CFT
A. Liam Fitzpatrick, David Shih
Non-chiral operators with positive anomalous dimensions can have interesting applications to supersymmetric model building. Motivated by this, we develop a new method for obtaining…
ClearPotential: Revealing Local Dark Matter in Three Dimensions
Eric Putney, David Shih, Sung Hak Lim +1
We present ClearPotential, a data-driven, three-dimensional measurement of the gravitational potential of the local Milky Way using unsupervised machine learning, without the symme…
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…
Online-compatible Unsupervised Non-resonant Anomaly Detection
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. Most proposals for new methods focus exclusively on…
General Messenger Gauge Mediation
Thomas T. Dumitrescu, Zohar Komargodski, Nathan Seiberg +1
We discuss theories of gauge mediation in which the hidden sector consists of two subsectors which are weakly coupled to each other. One sector is made up of messengers and the oth…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
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…
SURFing to the Fundamental Limit of Jet Tagging
Ian Pang, Darius A. Faroughy, David Shih +2
Beyond the practical goal of improving search and measurement sensitivity through better jet tagging algorithms, there is a deeper question: what are their upper performance limits…
Minimal String Theory
Nathan Seiberg, David Shih
We summarize recent progress in the understanding of minimal string theory, focusing on the worldsheet description of physical operators and D-branes. We review how a geometric int…
It's all right(-handed neutrinos): a new model for the anomaly
Pouya Asadi, Matthew R. Buckley, David Shih
The measured -meson semi-leptonic branching ratios and have long-standing deviations between theory and experiment. We introduce a model which explains both an…
Status and Implications of BSM Searches at the LHC
Eva Halkiadakis, George Redlinger, David Shih
The LHC has collided protons on protons at center-of-mass energies of 7 and 8 TeV between 2010-2012, referred to as the Run I period. We review the current status of searches for n…
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…
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…
Exploring General Gauge Mediation
Matthew Buican, Patrick Meade, Nathan Seiberg +1
We explore various aspects of General Gauge Mediation(GGM). We present a reformulation of the correlation functions used in GGM, and further elucidate their IR and UV properties. A…
Exact vs. Semiclassical Target Space of the Minimal String
Juan Maldacena, Gregory Moore, Nathan Seiberg +1
We study both the classical and the quantum target space of (p,q) minimal string theory, using the FZZT brane as a probe. By thinking of the target space as the moduli space of FZZ…
Look everywhere effects in anomaly detection
Marie Hein, Benjamin Nachman, David Shih
Machine learning-based anomaly detection methods are able to search high-dimensional spaces for hints of new physics with much less theory bias than traditional searches. However,…
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…
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis +8
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge,…
VBF vs. GGF Higgs with Full-Event Deep Learning: Towards a Decay-Agnostic Tagger
Cheng-Wei Chiang, David Shih, Shang-Fu Wei
We study the benefits of jet- and event-level deep learning methods in distinguishing vector boson fusion (VBF) from gluon-gluon fusion (GGF) Higgs production at the LHC. We show t…
Mapping Dark Matter in the Milky Way using Normalizing Flows and Gaia DR3
Sung Hak Lim, Eric Putney, Matthew R. Buckley +1
We present a novel, data-driven analysis of Galactic dynamics, using unsupervised machine learning -- in the form of density estimation with normalizing flows -- to learn the under…
Enhancing next token prediction based pre-training for jet foundation models
Joschka Birk, Anna Hallin, Gregor Kasieczka +3
Next token prediction is an attractive pre-training task for jet foundation models, in that it is simulation free and enables excellent generative capabilities that can transfer ac…
Searching for New Physics with Deep Autoencoders
Marco Farina, Yuichiro Nakai, David Shih
We introduce a potentially powerful new method of searching for new physics at the LHC, using autoencoders and unsupervised deep learning. The key idea of the autoencoder is that i…
Unifying Simulation and Inference with Normalizing Flows
Haoxing Du, Claudius Krause, Vinicius Mikuni +3
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 si…
Neural Scaling Laws for Jet Generation
Oz Amram, Darius A. Faroughy, Tjarko Gerdes +5
Recently observed empirical scaling laws describe the performance of foundation-type models as three independent key quantities -- dataset size, compute, and model parameters -- ar…
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…
Implications of a 125 GeV Higgs for the MSSM and Low-Scale SUSY Breaking
Patrick Draper, Patrick Meade, Matthew Reece +1
Recently, the ATLAS and CMS collaborations have announced exciting hints for a Standard Model-like Higgs boson at a mass of approximately 125 GeV. In this paper, we explore the pot…
Slepton co-NLSPs at the Tevatron
Joshua T. Ruderman, David Shih
We study the Tevatron signatures of promptly-decaying slepton co-NLSPs in the context of General Gauge Mediation (GGM). The signatures consist of trileptons plus MET and same-sign…
Surveying Pseudomoduli: the Good, the Bad and the Incalculable
Kenneth Intriligator, David Shih, Matthew Sudano
We classify possible types of pseudomoduli which arise when supersymmetry is dynamically broken in infrared-free low-energy theories. We show that, even if the pseudomoduli potenti…
Anomaly Detection with Density Estimation
Benjamin Nachman, David Shih
We leverage recent breakthroughs in neural density estimation to propose a new unsupervised anomaly detection technique (ANODE). By estimating the probability density of the data i…
Chiral Flavor Violation from Extended Gauge Mediation
Jared A. Evans, David Shih, Arun Thalapillil
Models of extended gauge mediation, in which large A-terms arise through direct messenger-MSSM superpotential couplings, are well-motivated by the discovery of the 125 GeV Higgs. H…
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka, Benjamin Nachman, David Shih +44
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…
ABCDisCo: Automating the ABCD Method with Machine Learning
Gregor Kasieczka, Benjamin Nachman, Matthew D. Schwartz +1
The ABCD method is one of the most widely used data-driven background estimation techniques in high energy physics. Cuts on two statistically-independent classifiers separate signa…
Recounting Dyons in N=4 String Theory
David Shih, Andrew Strominger, Xi Yin
A recently discovered relation between 4D and 5D black holes is used to derive weighted BPS black hole degeneracies for 4D N=4 string theory from the well-known 5D degeneracies. Th…
Boosted Tagging with Jet Charge and Deep Learning
Yu-Chen Janice Chen, Cheng-Wei Chiang, Giovanna Cottin +1
We demonstrate that the classification of boosted, hadronically-decaying weak gauge bosons can be significantly improved over traditional cut-based and BDT-based methods using deep…
FormFlavor Manual
Jared A. Evans, David Shih
This manual describes the usage and structure of FormFlavor, a Mathematica-based tool for computing a broad list of flavor and CP observables in general new physics models. Based o…
Pegasus W: An Ultra-Faint Dwarf Galaxy Outside the Halo of M31 Not Quenched by Reionization
Kristen. B. W. McQuinn, Yao-Yuan Mao, Matthew R. Buckley +3
We report the discovery of an ultrafaint dwarf (UFD) galaxy, Pegasus W, located on the far side of the Milky Way-M31 system and outside the virial radius of M31. The distance to th…
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…
Long-Lived Neutralino NLSPs
Patrick Meade, Matthew Reece, David Shih
We investigate the collider signatures of heavy, long-lived, neutral particles that decay to charged particles plus missing energy. Specifically, we focus on the case of a neutrali…
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
Erik Buhmann, Cedric Ewen, Darius A. Faroughy +7
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two nov…
Supersymmetry Breaking, R-Symmetry Breaking and Metastable Vacua
Kenneth Intriligator, Nathan Seiberg, David Shih
Models of spontaneous supersymmetry breaking generically have an R-symmetry, which is problematic for obtaining gaugino masses and avoiding light R-axions. The situation is improve…
Maximizing the Impact of New Physics in Anomalies
Pouya Asadi, David Shih
We develop a rigorous, semi-analytical method for maximizing any observable in the full 20-real-dimensional parameter space of the dimension 6 effective Hamiltonian, g…
Flux Vacua and Branes of the Minimal Superstring
Nathan Seiberg, David Shih
We analyze exactly the simplest minimal superstring theory, using its dual matrix model. Its target space is one dimensional (the Liouville direction), and the background fields in…
How to pick the best anomaly detector?
Marie Hein, Gregor Kasieczka, Michael Krämer +3
Anomaly detection has the potential to discover new physics in unexplored regions of the data. However, choosing the best anomaly detector for a given data set in a model-agnostic…
Learning to Unscramble Feynman Loop Integrals with SAILIR
David Shih
Integration-by-parts (IBP) reduction of Feynman integrals to master integrals is a key computational bottleneck in precision calculations in high-energy physics. Traditional approa…
DisCo Fever: Robust Networks Through Distance Correlation
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…
Dark Matter and the Higgs in Natural SUSY
Aria Basirnia, Sebastian Macaluso, David Shih
Null results from dark matter (DM) direct detection experiments and the 125 GeV Higgs both pose serious challenges to minimal supersymmetry. In this paper, we propose a simple exte…
Pseudomoduli Dark Matter
David Shih
We point out that pseudomoduli -- tree-level flat directions that often accompany dynamical supersymmetry breaking -- can be natural candidates for TeV-scale dark matter in models…
Full Phase Space Resonant Anomaly Detection
Erik Buhmann, Cedric Ewen, Gregor Kasieczka +3
Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strateg…
Counting Dyons in N=8 String Theory
David Shih, Andrew Strominger, Xi Yin
A recently discovered relation between 4D and 5D black holes is used to derive exact (weighted) BPS black hole degeneracies for 4D N=8 string theory from the exactly known 5D degen…
Revealing Compressed Stops Using High-Momentum Recoils
Sebastian Macaluso, Michael Park, David Shih +1
Searches for supersymmetric top quarks at the LHC have been making great progress in pushing sensitivity out to higher mass, but are famously plagued by gaps in coverage around low…
Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
Darius A. Faroughy, Sofia Palacios Schweitzer, Ian Pang +2
Autonomous language-model agents are increasingly evaluated on long-horizon tool-use tasks, but existing benchmarks rarely capture the complexity and nuance of real scientific work…
Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection
Thorben Finke, Marie Hein, Gregor Kasieczka +6
Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC). While these methods have shown remarkable perform…
Searching for muonic forces with the ATLAS detector
Iftah Galon, Enrique Kajamovitz, David Shih +2
The LHC copiously produces muons via different processes, and the muon sample will be large at the high-luminosity LHC (HL-LHC). In this work we propose to leverage this large muon…
Discovery and Characterization of Two Ultra Faint-Dwarfs Outside the Halo of the Milky Way: Leo M and Leo K
Kristen B. W. McQuinn, Yao-Yuan Mao, Erik J. Tollerud +4
We report the discovery of two ultra-faint dwarf galaxies, Leo M and Leo K, that lie outside the halo of the Milky Way. Using Hubble Space Telescope imaging of the resolved stars,…
Higgs Mediation with Strong Hidden Sector Dynamics
Simon Knapen, David Shih
We present a simple model that achieves GeV in the MSSM with large -terms and TeV-scale stops through a combination of gauge mediation and Higgs-messenger inter…
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
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. However, it has re…
(Extra)Ordinary Gauge Mediation
Clifford Cheung, A. Liam Fitzpatrick, David Shih
We study models of "(extra)ordinary gauge mediation," which consist of taking ordinary gauge mediation and extending the messenger superpotential to include all renormalizable coup…
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
Claudius Krause, David Shih
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce many-channel calorim…
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 3.0: A search for stellar streams in Gaia with the CATHODE algorithm
Anna Hallin, David Shih, Claudius Krause +1
We apply the model-agnostic anomaly detection method Cathode - originally developed for particle physics - to search for stellar streams in Gaia data. We combine Cathode with Via M…
Annulus Amplitudes and ZZ Branes in Minimal String Theory
David Kutasov, Kazumi Okuyama, Jongwon Park +2
We study the annulus amplitudes of (p,q) minimal string theory. Focusing on the ZZ-FZZT annulus amplitude as a target-space probe of the ZZ brane, we use it to confirm that the ZZ…
Spontaneous R-symmetry Breaking with Multiple Pseudomoduli
David Curtin, Zohar Komargodski, David Shih +1
We examine generalized O'Raifeartaigh models that feature multiple tree-level flat directions and only contain fields with R-charges 0 or 2. We show that spontaneous R-breaking at…
GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Mock Stellar Catalogs
Sung Hak Lim, Kailash A. Raman, Matthew R. Buckley +1
Cosmological N-body simulations of galaxies operate at the level of "star particles" with a mass resolution on the scale of thousands of solar masses. Turning these simulations int…
A Complete Model of Low-Scale Gauge Mediation
Nathaniel Craig, Simon Knapen, David Shih +1
Recent signs of a Standard Model-like Higgs at 125 GeV point towards large A-terms in the MSSM. This presents special challenges for gauge mediation, which by itself predicts vanis…
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…
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…
Sweeping the Dust Away -- Correcting the Phase Space Density of the Milky Way with Unsupervised Machine Learning
Eric Putney, David Shih, Sung Hak Lim +1
The Boltzmann equation relates the equilibrium phase space distribution of stars in the Milky Way to the Galaxy's gravitational potential. However, observations of stellar populati…
Accurate and robust methods for direct background estimation in resonant anomaly detection
Ranit Das, Thorben Finke, Marie Hein +4
Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality backgrou…
Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning
David Shih, Matthew R. Buckley, Lina Necib +1
We develop a new machine learning algorithm, Via Machinae, to identify cold stellar streams in data from the Gaia telescope. Via Machinae is based on ANODE, a general method that u…
Dynamical SUSY and R-symmetry breaking in SQCD with massive and massless flavors
Amit Giveon, Andrey Katz, Zohar Komargodski +1
We show that supersymmetry and R-symmetry can be dynamically broken in a long-lived metastable vacuum of SQCD with massive and massless flavors. The vacuum results from a competiti…
Dark Photons and Displaced Vertices at the MUonE Experiment
Iftah Galon, David Shih, Isaac R. Wang
MUonE is a proposed experiment designed to measure the hadronic vacuum polarization contribution to muon through elastic scattering. As such it employs an extremely hi…
Normalizing Flows for High-Dimensional Detector Simulations
Florian Ernst, Luigi Favaro, Claudius Krause +2
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimete…
Branes, Rings and Matrix Models in Minimal (Super)string Theory
Nathan Seiberg, David Shih
We study both bosonic and supersymmetric (p,q) minimal models coupled to Liouville theory using the ground ring and the various branes of the theory. From the FZZT brane partition…
Asymmetry Observables and the Origin of Anomalies
Pouya Asadi, Matthew R. Buckley, David Shih
The anomalies are among the longest-standing and most statistically significant hints of physics beyond the Standard Model. Many models have been proposed to explain…
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…