Revolutionizing Neutron Star Parameter Inference through Machine Learning

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

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Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Observations of neutron stars provide estimates of their mass and radius—key parameters for constraining their still uncertain equation of state. However, the accuracy of parameter inference is limited by the complexity and computational cost of current models, with more accurate models becoming prohibitively expensive. To make parameter inference feasible, we have developed a transposed convolutional neural network (NN) that serves as a surrogate for the physics-based model within our MCMC algorithm. We test and validate this approach in a simple static vacuum regime using millisecond pulsar PSR J0030+0451 as a case study. The NN achieves a speed-up of over 400× which enables the algorithm to converge on a solution for the first time. We outline our progress towards incorporating more realistic and complex regimes, where the neural network becomes an increasingly vital component of the inference process.

Expected benefits

The developed neural network surrogate speeds up the computation of model pulsar X-ray and gamma-ray light curves within Markov chain Monte Carlo and multi-nested suites by a factor of ~400 for vacuum magnetic fields and ~1,000,000 for realistic force-free magnetic fields. This allows the derivation of posterior parameter distributions that otherwise would be impossible.

System outputs

Posterior distributions

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

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Custom-developed code

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Public source code

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Sourcing

How it was built

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Vendor (as reported)

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Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

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Privacy Impact Assessment

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Authorization to Operate

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Demographic features

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Training and evaluation data

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Federal Data Catalog

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Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

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