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ACME - RNS and Universal Relations with Machine Learning

→ Europe/Paris
Gregory Papigkiotis (Aristotle University of Thessaloniki), Nikolaos Stergioulas (Aristotle University of Thessaloniki)
Description

The first part of the tutorial will focus on using the public domain RNS code for constructing highly-accurate rotating neutron star models.  The second part will discuss quasi-universal properties of rotating neutron star models. Users will be able to learn a new software package that is used to construct quasi-universal relations with machine-learning methods.

Registration: register by May 19th at this page (zoom link will be email before start of traning session). 

All training material for this session is available at this repository 

    • 10:00 → 10:25
      Introduction to Rotating Neutron Stars 25m

      The main theoretical results for Rotating Neutron Star spacetimes will be introduced.

      Speaker: Nikolaos Stergioulas (Aristotle University of Thessaloniki)
      2026-ACME-RNS.pdf
      Paper I
      Paper II
      Paper III
      RNS code repository (v1 & v2)
      Rotating Stars in Relativity (review article)
    • 10:25 → 11:20
      Hands-on training with RNS 55m

      Several practical examples of different sequences of rotating neutron star models will be discussed. We will use a Python notebook to call RNS and construct figures and tables for test results.

      Speaker: Nikolaos Stergioulas (Aristotle University of Thessaloniki)
      Tutorials Repository
    • 11:20 → 11:40
      Break 20m
    • 11:40 → 12:05
      Introduction to Universal Relations 25m

      The main universal relations occurring between fundamental properties of rotating neutron stars will be discussed.

      Speaker: Gregory Papigkiotis (Aristotle University of Thessaloniki)
      Paper I
      Paper II
      Paper III
    • 12:05 → 13:00
      Hands-on training on Universal Relations with Machine Learning 55m

      The construction of universal relations between fundamental properties of rotating neutron stars will be presented using a new machine-learning approach. An open source code will be introduced and many examples will be worked out in the form of a python notebook.

      Speaker: Gregory Papigkiotis (Aristotle University of Thessaloniki)
      Tutorials Repository