NAYARA FONSECA
PUBLICATIONS and PREPRINTS
[arXiv]
[Google Scholar]
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Y. Nam*, N. Fonseca*, S. H. Lee, C. Mingard, A. A. Louis, An exactly solvable model for emergence and scaling laws. NeurIPS 2024; ICML 2024 Workshop on High-dimensional Learning Dynamics [HiLD] [arXiv]
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N. Fonseca*, V. Guidetti*, W. Trojak*, Probing optimisation in physics-informed neural networks.
ICLR 2023 Workshop on Physics for Machine Learning [Physics4ML] [arXiv]
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N. Fonseca* and V. Guidetti*, Generalizing similarity in noisy setups: the DIBS phenomenon.
European Conference on Artificial Intelligence 2023 [ECAI 2023] [arXiv]
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N. Fonseca and E. Morgante, Probing photophobic (rel)axion dark matter. [Phys. Rev. D] [arXiv]
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N. Fonseca, E. Morgante, R. Sato, G. Servant, Relaxion Fluctuations (Self-stopping Relaxion) and Overview of Relaxion Stopping mechanism. [JHEP] [arXiv]
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N. Fonseca, E. Morgante, R. Sato, G. Servant, Axion Fragmentation. [JHEP] [arXiv]
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N. Fonseca, B. von Harling, L. de Lima, C. S. Machado, Super-Planckian axions from near-conformality. [Phys. Rev. D] [arXiv]
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N. Fonseca and E. Morgante, Relaxion Dark Matter. [Phys. Rev. D] [arXiv]
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N. Fonseca, E. Morgante, and G. Servant, Higgs relaxation after inflation. [JHEP] [arXiv]
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N. Fonseca, B. von Harling, L. de Lima, and C. S. Machado, A warped relaxion. [JHEP] [arXiv]
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N. Bernal, C. S. Fong, and N. Fonseca, Sharing but not caring: Dark Matter and the Baryon Asymmetry of the Universe. [JCAP] [arXiv]
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N. Fonseca, L. de Lima, C. S. Machado, and R. D. Matheus, Large field excursions from a few site relaxion model (N-Relaxion). [Phys. Rev. D] [arXiv]
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N. Fonseca, L. Necib and J. Thaler, Dark Matter, Shared Asymmetries, and Galactic Gamma Ray Signals. [JCAP] [arXiv]
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N. Fonseca, R. Z. Funchal, A. Lessa, and L. Lopez-Honorez, Dark Matter Constraints on Composite Higgs Models. [JHEP] [arXiv]
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G. Burdman, N. Fonseca, and G. Lichtenstein, Resonances from Quiver Theories at the LHC. [Phys. Rev. D] [arXiv]
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G. Burdman, N. Fonseca, and L. Lima, Full-hierarchy Quiver Theories of Electroweak Symmetry Breaking and Fermion Masses. [JHEP] [arXiv]
In the machine learning papers, an asterisk (*) denotes equal contribution. In theoretical high energy physics, the convention is to order the author list alphabetically.