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portfolio
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publications
Unraveling Hawking radiation
Published in Revista Brasileira de Ensino de Física, 2019
This paper comprehends a first part of my Masters.
Santi, Natali Soler Matubaro de, & Santarelli, Raphael. (2019). Desvendando a radiação Hawking. Revista Brasileira de Ensino de Física, 41(3), e20180312. Epub March 11, 2019.https://doi.org/10.1590/1806-9126-rbef-2018-0312 http://www.scielo.br/pdf/rbef/v41n3/1806-9126-RBEF-41-3-e20180312.pdf
Mass Evolution of Schwarzschild Black Holes
Published in Brazilian Journal of Physics, 2019
This paper contains the numerical results from my Masters.
de Santi, N.S.M., Santarelli, R. Mass Evolution of Schwarzschild Black Holes. Braz J Phys 49, 897–913 (2019). https://doi.org/10.1007/s13538-019-00708-y https://link.springer.com/article/10.1007%2Fs13538-019-00708-y
Mimicking the halo-galaxy connection using machine learning
Published in MNRAS, 2022
First approach: stacked methods and SMOGN
Natalí S. M. de Santi, Natália V. N. Rodrigues, Antonio D. Montero-Dorta, L. Raul Abramo, Beatriz Tucci, M. Celeste Artale, Mimicking the halo–galaxy connection using machine learning, Monthly Notices of the Royal Astronomical Society, Volume 514, Issue 2, August 2022, Pages 2463–2478, https://doi.org/10.1093/mnras/stac1469 https://doi.org/10.1093/mnras/stac1469
Improving cosmological covariance matrices with machine learning
Published in JCAP, 2022
Image denoising for a robust model
de Santi, N. S. M. and Abramo, L. R. 2022, DOI:10.1088/1475-7516/2022/09/013 https://iopscience.iop.org/article/10.1088/1475-7516/2022/09/013
Primeiros passos na obtenção de parâmetros cosmológicos utilizando matrizes de covariância cosmológicas sem ruído
Published in Blucher Physics Proceedings, 2022
First steps to improve cosmological covariance matrices
de Santi, N. S. M. and Abramo, L. R. 2022, DOI: 10.5151/astrocientistas2021-11 https://www.proceedings.blucher.com.br/article-details/primeiros-passos-na-obteno-de-parmetros-cosmolgicos-utilizando-matrizes-de-covarincia-cosmolgicas-sem-rudo-37453
Robust field-level likelihood-free inference with galaxies
Published in arXiv, 2023
First ML robust method to predict $\Omega_{m}$
Natalí S. M. de Santi et al 2023 ApJ 952 69 https://iopscience.iop.org/article/10.3847/1538-4357/acd1e2/meta
A universal equation to predict Ωm from halo and galaxy catalogues
Published in ApJ, 2023
Using symbolic regression to understand GNNs
Shao, H., de Santi, N. S. M., Villaescusa-Navarro, F., et al. 2023, ApJ, 956, 2, 149. doi:10.3847/1538-4357/acee6f https://iopscience.iop.org/article/10.3847/1538-4357/acee6f
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
Published in ApJ, 2023
Why does Astrid is so good for getting robust models?
Ni, Y., Genel, S., Anglés-Alcázar, D., et al. 2023, ApJ, 959, 2, 136. doi:10.3847/1538-4357/ad022a https://iopscience.iop.org/article/10.3847/1538-4357/ad022a
High-fidelity reproduction of central galaxy joint distributions with Neural Networks
Published in MNRAS, 2023
Regression to classification solving halo-galaxy scatter problem
Natália V N Rodrigues, Natalí S M de Santi, Antonio D Montero-Dorta, L Raul Abramo, High-fidelity reproduction of central galaxy joint distributions with neural networks, Monthly Notices of the Royal Astronomical Society, Volume 522, Issue 3, July 2023, Pages 3236–3247, https://doi.org/10.1093/mnras/stad1186 https://doi.org/10.1093/mnras/stad1186
A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship
Published in arXiv, 2023
NF for mapping the halo-galaxy connection using cosmological parameters and CAMELS
Lovell, C. C., Hassan, S., Anglés-Alcázar, D., et al. 2023, arXiv:2307.06967. doi:10.48550/arXiv.2307.06967 https://arxiv.org/abs/2310.15234](https://arxiv.org/abs/2310.15234
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
Published in JCAP, 2025
GNNs can deal with observational effects
de Santi, N. S. M., Villaescusa-Navarro, F., Raul Abramo, L., et al. 2025, JCAP, 2025, 1, 082. doi:10.1088/1475-7516/2025/01/082 https://arxiv.org/abs/2310.15234](https://arxiv.org/abs/2310.15234
Exploring the halo-galaxy connection with probabilistic approaches
Published in A&A, 2025
Normalizing flows, Gaussian Likelihood and regression to classification solving halo-galaxy scatter problem
Rodrigues, N. V. N., de Santi, N. S. M., Abramo, R., et al. 2025, AAP, 698, A3. doi:10.1051/0004-6361/202453284 https://www.aanda.org/articles/aa/full_html/2025/06/aa53284-24/aa53284-24.html
talks
Development of a magnetic levitation system
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Poster presentation, XIX Congresso de Iniciação Científica da UFSCar (XIX CIC), Federal University of São Carlos, São Carlos, SP, Brazil
Synthesis and characterization of BaTi0.9Zr0.1O3 powders
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Numerical studies on the classical and quantum harmonic oscillator
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Regularization techniques in the Casimir effect
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Observer dependence in the particle concept and the Unruh Effect
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Poster presentation, XIII Semana de Física (XIII SeFís), Federal University of São Carlos, São Carlos, SP, Brazil
Improving covariance matrices using machine learning
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Improving Covariance Matrices using Machine Learning
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First steps in the obtation of the cosmological parameters using denoised cosmological covariance matrices
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Using machine learning to mimic the halo-galaxy connection
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Virtual talk, Group Meeting: Prof. Francesco Shankar, University of Southampton, Southampton, UK
A machine learning suite to mimic the halo-galaxy connection
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Improving cosmological covariance matrices using machine learning
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Virtual talk, Journal Club: Astronomical Observatory of Trieste, I.N.A.F., Trieste, Italy
Improving cosmological covariance matrices with machine learning
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Virtual talk, AI in Astronomy, IAG, USP, São Paulo, SP, Brazil
Obtaining cosmological covariance matrices with machine learning
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Graph neural networks for robust parameter inference in cosmology: the first steps before real data
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Inferring cosmological parameters using galaxies
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Halo-galaxy connection and machine learning: an aerial view
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Virtual presentation, II mini-Workshop on the halo-galaxy connection for the South American community, Universidad Técnica Federico Santa María, Santiago, Chile
Obtaining cosmological information through machine learning
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Constraining Ωm using galaxies
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Talk, Galaxy Formation Group Meeting, Flatiron Institute, New York, NY, US
Can we use galaxies to constrain 𝛀m?
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Virtual talk, Journal club: Berkeley Cosmology Group, UC Berkeley, Berkeley, CA, US
Constraining Ωm with phase-space information of galaxies
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Using graph neural networks to constrain Ωm - A robust model
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Talk, Cosmology Lunch Talks, Institute of Advanced Studies (IAS), Princeton, NJ, US
Simulation-based inference: constraining Ωm with galaxies phase-space information
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Talk, Tri-state Cosmology X Data Science Meeting, Flatiron Institute
Doing Cosmology with galaxies
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Implicit likelihood inference: a robust model to constrain 𝛀m
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Talk, Group Seminars - Prof. Bhuvnesh Jain, UPenn, Philadelphia, PA, US
A robust model to constrain 𝛀m with implicit likelihood free-inference
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Implicit likelihood inference using graph neural networks: a robust model to constrain 𝛀m
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Talk, Data-Science X Astro seminar, Yale University, Yale University, New Haven, CT, US
Field-level likelihood-free inference with galaxies: a robust model
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Unraveling Cosmology from field-level likelihood-free inference - A robust model
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Talk, Renoir Science Seminar, Recherche Energie NOIRe (RENOIR), Marseille, France
Cosmology from galaxy phase-space information and a bit of halo-galaxy connection
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Field-level simulation-based inference: using GNNs to get a robust model
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Talk, CITA cosmology discussion, University of Toronto, Toronto, Canada
Cosmology from field-level likelihood-free inference: The first steps towards real data
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How to use Machine Learning (to try) to understand the Universe
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Before real data: pressing graph neural networks to do field-level simulation-based inference with galaxies
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The Influence of Systematics on Galaxy Catalogs - looking for a robust model to do field-level likelihood-free inference
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Talk, Seminários do CBPF/COTEC, CBPF, Rio de Janeiro, Brazil
A proper probabilistic approach for halo-galaxy connection and likelihood-free parameter inference
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Measuring the impact of systematics on the predictions of 𝛀m on simulated galaxy catalogs using graph neural networks
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Virtual talk, Yale Astronomy Data Science Seminar, Yale
Measuring the impact of systematics on the predictions of 𝛀m on simulated galaxy catalogs using graph neural networks
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Virtual talk, ML Session of DoA, Tsinghua University, Beijing, China
Graph neural networks for Cosmology
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Talk, First Learning on Graphs (LOG) conference in South America – ICMC, São Carlos, Brazil
Cosmology with graph neural networks
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Talk, SP Research Group meetings in Astro & Cosmo – Principia Institute, São Paulo, Brazil
The use of machine learning and artificial intelligence in astrophysics, cosmology, and gravitation researches
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Panelist, As astrocientistas: III Encontro Brasileiro de Meninas e Mulheres da Astrofísica, Gravitação e Cosmologia - Remote.
How to use graph neural networks to understand the Universe?
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Talk, How to use graph neural networks to understand the Universe?, Universidad Técnica Federico Santa María, Chile - Remote
teaching
Tutor Experience
Tutoring, Tutores Educação Multidisciplinar, 2018
Tutoring in Physics, elementary school II level.
Private teacher
Undergraduate revision lectures, Flexus Educacional LTDA-ME, 2018
Review of the discipline LE201 - General Physics I, of the University of Campinas (UNICAMP), campus of Limeira.
Introdução à Relatividade
Tutorial, XIX Physics Week, 2019
- Event: XIX Physics Week
- Tutorial: material
Teaching Assistant of Basic Physics III
Teaching Assistant, University of São Paulo (USP), 2020
I helped undergrad engineering students with problems, concepts and theory related to Eletromagnetism.
Teaching Assistant of Classical Electrodynamics I
Teaching Assistant, University of São Paulo (USP), 2021
I helped graduated physics students with problems, concepts and theory related to Classical Electrodynamics I. Besides, together with the professor, we proposed the problem sets and I solved and made available the solution of them to the students.
Tutorial on Halo-galaxy connection
Tutorial, Galaxy Formation and Evolution in the Data Science Era, 2023
- Event: Galaxy Formation and Evolution in the Data Science Era
- Tutorial: code
- Video: video.
Pytorch and GNNs tutorial
Tutorial, AI in Astronomy, 2023
- Event: AI in Astronomy
- Tutorial: code
Machine Learning for Cosmology and Cosmological Simulations
Guest Lecturer, Universidad Andres Bello, 2024
Tutorial: code