A5106126327- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Distributed and Parallel Computing Systems
- Particle Detector Development and Performance
- Quantum Chromodynamics and Particle Interactions
- Computational Physics and Python Applications
- Particle Accelerators and Free-Electron Lasers
- Gaussian Processes and Bayesian Inference
- Quantum Information and Cryptography
- Superconducting Materials and Applications
- Particle accelerators and beam dynamics
- Quantum Mechanics and Applications
- Scientific Computing and Data Management
- Atomic and Subatomic Physics Research
- Big Data Technologies and Applications
- Anomaly Detection Techniques and Applications
- Computer Graphics and Visualization Techniques
- Dark Matter and Cosmic Phenomena
- Astrophysics and Cosmic Phenomena
- Physics of Superconductivity and Magnetism
- Quantum Computing Algorithms and Architecture
- Big Data and Business Intelligence
- Gamma-ray bursts and supernovae
- Machine Learning and Data Classification
- Geophysical and Geoelectrical Methods
Massachusetts Institute of Technology
2017-2024
The NSF AI Institute for Artificial Intelligence and Fundamental Interactions
2024
Harvard University
2019-2020
Moscow Institute of Thermal Technology
2019
Center for Theoretical Biological Physics
2019
Institute for Basic Science
2014-2016
University of Waterloo
2016
Harvard University Press
2014-2016
Brookhaven National Laboratory
2014-2015
Korea Advanced Institute of Science and Technology
2014-2015
A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event intrinsically a variable-length unordered set of particles, we build upon recent efforts directly sets features or "point clouds". Adapting specializing the "Deep Sets" framework physics, introduce Energy Flow Networks, which respect infrared collinear safety by construction. We also develop Particle allow general energy dependence inclusion additional...
Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these are trained on imperfect simulations due a lack of truth-level information in the data, which risks model artifacts simulation. this paper, we introduce paradigm classification without labels (CWoLa) classifier is distinguish statistical mixtures classes, common physics. Crucially, neither individual nor class proportions required, yet...
A bstract We introduce the energy flow polynomials: a complete set of jet substructure observables which form discrete linear basis for all infrared- and collinear-safe observables. Energy polynomials are multiparticle correlators with specific angular structures that direct consequence infrared collinear safety. establish powerful graph-theoretic representation allows us to design efficient algorithms their computation. Many common exact combinations polynomials, we demonstrate spanning...
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon paradigm that a jet be treated as an image, intensity given local calorimeter deposits. We supplement this construction adding color images, red, green blue intensities...
Collider data must be corrected for detector effects (``unfolded'') to compared with many theoretical calculations and measurements from other experiments. Unfolding is traditionally done individual, binned observables without including all information relevant characterizing the response. We introduce OmniFold, an unfolding method that iteratively reweights a simulated dataset, using machine learning capitalize on available information. Our approach unbinned, works arbitrarily...
Quantum key distribution (QKD) allows for communication with security guaranteed by quantum theory. The main theoretical problem in QKD is to calculate the secret rate a given protocol. Analytical formulas are known protocols symmetries, since symmetry simplifies analysis. However, experimental imperfections break hence effect of on rates difficult estimate. Furthermore, it an interesting question whether (intentionally) asymmetric could outperform symmetric ones. Here, we develop robust...
A new experiment is described to detect a permanent electric dipole moment of the proton with sensitivity $10^{-29}e\cdot$cm by using polarized "magic" momentum $0.7$~GeV/c protons in an all-electric storage ring. Systematic errors relevant are discussed and techniques address them presented. The measurement sensitive physics beyond Standard Model at scale 3000~TeV.
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range modern machine learning approaches. Unlike most methods they rely low-level input, for instance calorimeter output. While their network architectures are vastly different, performance is comparatively similar. In general, find that these new approaches extremely powerful and great fun.
When are two collider events similar? Despite the simplicity and generality of this question, there is no established notion distance between events. To address we develop a metric for space based on earth mover's distance: "work" required to rearrange radiation pattern one event into another. We expose interesting connections structure infrared- collinear-safe observables, providing novel technique quantify modifications due hadronization, pileup, detector effects. showcase how metrization...
A persistent challenge in practical classification tasks is that labeled training sets are not always available. In particle physics, this surmounted by the use of simulations. These simulations accurately reproduce most features data, but cannot be trusted to capture all complex correlations exploitable modern machine learning methods. Recent work weakly supervised has shown simple, low-dimensional classifiers can trained using only impure mixtures present data. Here, we demonstrate...
We introduce jet topics: a framework to identify underlying classes of jets from collider data. Because close mathematical relationship between distributions observables in and emergent themes sets documents, we can apply recent techniques "topic modeling" extract topics data with minimal or no input simulation theory. As proof concept parton shower samples, determine separate quark gluon for constituent multiplicity. also rapidity spectra mixed Z-plus-jet sample. While are defined directly...
While "quark" and "gluon" jets are often treated as separate, well-defined objects in both theoretical experimental contexts, no precise, practical, hadron-level definition of jet flavor presently exists. To remedy this issue, we develop advocate for a data-driven, operational quark gluon that is readily applicable at colliders. Rather than specifying per-jet label, aggregately define the distribution level terms measured hadronic cross sections. Intuitively, emerge two maximally separable...
Pileup involves the contamination of energy distribution arising from primary collision interest (leading vertex) by radiation soft collisions (pileup). We develop a new technique for removing this using machine learning and convolutional neural networks. The network takes as input charged leading vertex particles, pileup all neutral particles outputs coming alone. PUMML algorithm performs remarkably well at eliminating distortion on wide range simple complex jet observables. test robustness...
We explore the metric space of jets using public collider data from CMS experiment. Starting 2.3/fb 7 TeV proton-proton collisions collected at Large Hadron Collider in 2011, we isolate a sample 1,690,984 central with transverse momentum above 375 GeV. To validate performance detector reconstructing energy flow jets, compare Open Data to corresponding simulated samples for variety jet kinematic and substructure observables. Even without unfolding, find very good agreement track-based...
A bstract We establish that many fundamental concepts and techniques in quantum field theory collider physics can be naturally understood unified through a simple new geometric language. The idea is to equip the space of events with metric, from which other objects rigorously defined. Our analysis based on energy mover’s distance, quantifies “work” required rearrange one event into another. This operates purely at level observable flow information, allows for clarified definition infrared...
Understanding jets initiated by quarks and gluons is of fundamental importance in collider physics. Efficient robust techniques for quark versus gluon jet discrimination have consequences new physics searches, precision $\alpha_s$ studies, parton distribution function extractions, many other applications. Numerous machine learning analyses attacked the problem, demonstrating that good performance can be obtained but generally not providing an understanding what properties are responsible...
Multiparticle correlators are mathematical objects frequently encountered in quantum field theory and collider physics. By translating multiparticle into the language of graph theory, we can gain new insights their structure as well identify efficient ways to manipulate them. We highlight power this graph-theoretic approach by ``cutting open'' vertices edges graphs, allowing us systematically classify linear relations among develop faster methods for computation. The naive computational...
A common setting for scientific inference is the ability to sample from a high-fidelity forward model (simulation) without having an explicit probability density of data. We propose simulation-based maximum likelihood deconvolution approach in this called OmniFold. Deep learning enables be naturally unbinned and (variable-, and) high-dimensional. In contrast parameter estimation, goal remove detector distortions order enable variety down-stream tasks. Our deep generalization Richardson-Lucy...
Analytic expressions for the potentials and fields of flat cylindrical plates, including fringe fields, are given. The present analysis extends simplifies current expression plates develops in terms polar coordinates. development a fortran program to output field strength at given location within Proton Electric Dipole Moment (Proton EDM) ring is then described. Fourth-order Runge-Kutta integration used investigate effect on particle spin dynamics with precision tracking proposed EDM experiment.
A bstract Jet grooming is an important strategy for analyzing relativistic particle collisions in the presence of contaminating radiation. Most jet techniques introduce hard cutoffs to remove soft radiation, leading discontinuous behavior and associated experimental theoretical challenges. In this paper, we Pileup Infrared Radiation Annihilation (P iranha ), a paradigm continuous that overcomes discontinuity infrared sensitivity hard-cutoff procedures. We motivate P from perspective optimal...