Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning

Mroczek, D.; Hjorth-Jensen, M.; Noronha-Hostler, J.; Parotto, P.; Ratti, C.; Vilalta, R.

doi:10.48550/arxiv.2203.13876

Cited by 3 publications

(3 citation statements)

References 50 publications

(72 reference statements)

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“…Finally, Mroczek et al (2022) used active learning (Cohen, 2018) to reduce sampling requirements for training classifiers in searches for acceptable EOS parameters.…”

Section: Other Applications Of ML Methodsmentioning

confidence: 99%

Colloquium: Machine learning in nuclear physics

et al. 2022

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TABLE I. Table of ML methods discussed in this Colloquium with an indication of the main type of learning (S, supervised; U, unsupervised; semi-S, semisupervised). Acronym Method Description Learning type AE, VAE Autoencoders, Variational autoencoders ANN capable of learning efficient representations of the input data without any supervision U ANN Artificial neural network Models for learning defined by connected units (or nodes) and hidden layers with well-defined inputs and outputs S BED Bayesian experimental design Bayesian inference for experimental design S BM Boltzmann machine Generative ANN that can learn a probability distribution from sets of changing inputs U BMA, BMM Bayesian model averaging, Bayesian model mixing Bayesian inference applied to model selection or the combined estimation, or performed over a mixture model S BNN Bayesian neural network ANN where the parameters of the network are represented by probabilities learned by Bayesian inference S BO Bayesian optimization Optimization of functions without an a priori knowledge of functional forms. S and semi-S CNN Convolutional neural network ANN where convolution is used to reduce dimensionalities S EMB Ensemble methods and boosting Methods based on collections of decision trees as simple learners S GAN Generative adversarial network System of two ANNs where a generative network generates outputs while a discriminative network evaluates them U GP Gaussian process Collection of random variables that have a joint Gaussian distribution used in Bayesian inference Semi-S KNN k-nearest neighbors Nonparametric method where inputs consist of the k closest training examples in a dataset S KR Kernel regression Extension of linear regression methods to include nonlinear function kernels S LR Logistic regression Convex optimization method based on maximum likelihood estimate for classification problems S LSTM Long short-term memory RNN capable of learning long-term dependencies S PCA Principal component analysis Dimensionality reduction technique based on retaining the largest eigenvalues of the covariance matrix U REG Linear regression Linear algebra methods used for modeling continuous functions in terms of their explanatory variables S RL Reinforcement learning Learning achieved by trial and error of desired and undesired events Neither S nor U RNN Recurrent neural network ANN where connections between nodes allow for temporal dynamic behavior S SVM Support vector machine Convex optimization techniques with efficient ways to distinguish features in datasets S

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“…Finally, Mroczek et al (2022) used active learning (Cohen, 2018) to reduce sampling requirements for training classifiers in searches for acceptable EOS parameters.…”

Section: Other Applications Of ML Methodsmentioning

confidence: 99%

Colloquium: Machine learning in nuclear physics

et al. 2022

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“…2 Attempts to constrain the equation of state directly from hadronic observables have shown promise, but as of yet still have significant re-maining uncertainty [70,71]. Active learning techniques are also being applied to the efforts to characterize the equation of state [72].…”

Section: B Viscous Hydrodynamics -Musicmentioning

confidence: 99%

Bayesian quantification of strongly-interacting matter with color glass condensate initial conditions

Heffernan¹,

Gale²,

Jeon³

et al. 2023

Preprint

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A global Bayesian analysis of relativistic Pb + Pb collisions at √ s NN = 2.76 TeV is performed, using a multistage model consisting of an IP-Glasma initial state, a viscous fluid dynamical evolution, and a hadronic transport final state. The observables considered are from the soft sector hadronic final state. Posterior and Maximum a Posteriori parameter distributions that pertain to the IP-Glasma and hydrodynamic phases are obtained, including the shear and bulk specific viscosity of strong interacting matter. The first use of inference with transfer learning in heavy-ion analyses is presented, together with Bayes Model Averaging.

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“…Active learning was used to find the most informative parameter combinations before labeling them [92]. In active learning, the network is first trained using a small amount of labeled data.…”

Section: Active Learning For Qcd Eosmentioning

confidence: 99%

High-energy nuclear physics meets machine learning

Pang

et al. 2023

NUCL SCI TECH

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Although seemingly disparate, high-energy nuclear physics (HENP) and machine learning (ML) have begun to merge in the last few years, yielding interesting results. It is worthy to raise the profile of utilizing this novel mindset from ML in HENP, to help interested readers see the breadth of activities around this intersection. The aim of this mini-review is to inform the community of the current status and present an overview of the application of ML to HENP. From different aspects and using examples, we examine how scientific questions involving HENP can be answered using ML.

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Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning

Cited by 3 publications

References 50 publications

Colloquium: Machine learning in nuclear physics

Colloquium: Machine learning in nuclear physics

Bayesian quantification of strongly-interacting matter with color glass condensate initial conditions

High-energy nuclear physics meets machine learning

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