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Latest Other Publications

Exploring gravitational-wave detection and parameter inference using Deep Learning methods, João D. Álvares, José A. Font, Felipe F. Freitas, Osvaldo G. Freitas, António P. Morais, Solange Nunes, Antonio Onofre, and Alejandro Torres-Forné; arXiv:2011.10425[gr-qc].

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SO(2) gauged Skyrmions in 4+1 dimension, Francisco Navarro-Lerida, Eugen Radu, D. H. Tchrakian; Phys. Rev. D (2020) 125014, arXiv:2003.05889 [hep-th].

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On the inexistence of self-gravitating soiitons in generalised axion-electrodynamics, C. A. R. Herdeiro and J. M. S. Oliveira; Phys. Lett. B 800 (2020) 135076, arXiv:1909.08915 [gr-qc].

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On the inexistence of solitons in Einstein–Maxwell-scalar models, C. A. R. Herdeiro and J. M. S. Oliveira; Class. Quant. Grav. 36 (2019) no.10, 105015, arXiv:1902.07721 [gr-qc].

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Gravitating solitons and black holes with synchronised hair in the four dimensional O(3) sigma-model, C. Herdeiro, I. Perapechka, E. Radu and Y. Shnir; JHEP 1902 (2019) 111, arXiv:1811.11799 [gr-qc].

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Latest Other News & Events

Interpretable Machine Learning for Astrophysics, using Symbolic Regression and Graph Neural Networks

Speaker
Miles Cranmer (Princeton University)
Event date
Venue
online (only)
Event type
Seminars

In this talk I will argue two points. 1) Symbolic regression, a machine learning technique that fits data by iteratively searching the space of all possible analytic equations, should be a standard machine learning algorithm in astrophysics. 2) Symbolic regression can be extended to high-dimensional spaces, such as to models for N-body simulations, using the method we have developed.

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