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Found 50 papers, 26 papers with code
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics.
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics.
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions.
AI for Science: An Emerging Agenda
This report summarises the discussions from the seminar and provides a roadmap to suggest how different communities can collaborate to deliver a new wave of progress in AI and its application for scientific discovery.
Configurable calorimeter simulation for AI applications
A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator.
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing.
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant.
Flow-based sampling in the lattice Schwinger model at criticality
In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass.
Simulation Intelligence: Towards a New Generation of Scientific Methods
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
A neural simulation-based inference approach for characterizing the Galactic Center $γ$-ray excess
The nature of the Fermi gamma-ray Galactic Center Excess (GCE) has remained a persistent mystery for over a decade.
Flow-based sampling for multimodal distributions in lattice field theory
Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory.
Flow-based sampling for fermionic lattice field theories
Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact.
Exact and Approximate Hierarchical Clustering Using A*
In those cases, hierarchical clustering can be seen as a combinatorial optimization problem.
Introduction to Normalizing Flows for Lattice Field Theory
This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows.
Hierarchical clustering in particle physics through reinforcement learning
Particle physics experiments often require the reconstruction of decay patterns through a hierarchical clustering of the observed final-state particles.
Semi-parametric $γ$-ray modeling with Gaussian processes and variational inference
Mismodeling the uncertain, diffuse emission of Galactic origin can seriously bias the characterization of astrophysical gamma-ray data, particularly in the region of the Inner Milky Way where such emission can make up over 80% of the photon counts observed at ~GeV energies.
Simulation-based inference methods for particle physics
Our predictions for particle physics processes are realized in a chain of complex simulators.
Sampling using $SU(N)$ gauge equivariant flows
We develop a flow-based sampling algorithm for $SU(N)$ lattice gauge theories that is gauge-invariant by construction.
Secondary Vertex Finding in Jets with Neural Networks
Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers.
High Energy Physics - Experiment High Energy Physics - Phenomenology
Discovering Symbolic Models from Deep Learning with Inductive Biases
The technique works as follows: we first encourage sparse latent representations when we train a GNN in a supervised setting, then we apply symbolic regression to components of the learned model to extract explicit physical relations.
Flows for simultaneous manifold learning and density estimation
We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold.
Equivariant flow-based sampling for lattice gauge theory
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction.
Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
In contrast to existing methods, we present novel dynamic-programming algorithms for \emph{exact} inference in hierarchical clustering based on a novel trellis data structure, and we prove that we can exactly compute the partition function, maximum likelihood hierarchy, and marginal probabilities of sub-hierarchies and clusters.
Set2Graph: Learning Graphs From Sets
Many problems in machine learning can be cast as learning functions from sets to graphs, or more generally to hypergraphs; in short, Set2Graph functions.
Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributions in high dimensions.
The frontier of simulation-based inference
Many domains of science have developed complex simulations to describe phenomena of interest.
Hamiltonian Graph Networks with ODE Integrators
We introduce an approach for imposing physically informed inductive biases in learned simulation models.
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics.
MadMiner: Machine learning-based inference for particle physics
Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods.
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
Effective LHC measurements with matrix elements and machine learning
One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled.
Inferring the quantum density matrix with machine learning
We also introduce quantum flows, the quantum analog of normalizing flows, which can be used to increase the expressivity of this variational family.
Machine learning and the physical sciences
Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years.
Computational Physics Cosmology and Nongalactic Astrophysics Disordered Systems and Neural Networks High Energy Physics - Theory Quantum Physics
Likelihood-free inference with an improved cross-entropy estimator
We extend recent work (Brehmer, et.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Backdrop: Stochastic Backpropagation
We introduce backdrop, a flexible and simple-to-implement method, intuitively described as dropout acting only along the backpropagation pipeline.
Mining gold from implicit models to improve likelihood-free inference
Simulators often provide the best description of real-world phenomena.
Constraining Effective Field Theories with Machine Learning
We present powerful new analysis techniques to constrain effective field theories at the LHC.
A Guide to Constraining Effective Field Theories with Machine Learning
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments.
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data.
A Roadmap for HEP Software and Computing R&D for the 2020s
Particle physics has an ambitious and broad experimental programme for the coming decades.
Computational Physics High Energy Physics - Experiment
Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes
We demonstrate the application of this approach to modeling the background to searches for dijet resonances at the Large Hadron Collider and describe how the approach can be used in the search for generic localized signals.
Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology
Adversarial Variational Optimization of Non-Differentiable Simulators
We adapt the training procedure of generative adversarial networks by replacing the differentiable generative network with a domain-specific simulator.
QCD-Aware Recursive Neural Networks for Jet Physics
Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images.
Learning to Pivot with Adversarial Networks
Several techniques for domain adaptation have been proposed to account for differences in the distribution of the data used for training and testing.
Parameterized Machine Learning for High-Energy Physics
We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
This leads to a new machine learning-based approach to likelihood-free inference that is complementary to Approximate Bayesian Computation, and which does not require a prior on the model parameters.
RECAST: Extending the Impact of Existing Analyses
Searches for new physics by experimental collaborations represent a significant investment in time and resources.
High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability
Asymptotic formulae for likelihood-based tests of new physics
We describe likelihood-based statistical tests for use in high energy physics for the discovery of new phenomena and for construction of confidence intervals on model parameters.
Data Analysis, Statistics and Probability High Energy Physics - Experiment
