Dr. Lilianne Nakazono

Posdoctoral Researcher in Cosmology,

Universidade de São Paulo (USP)

I hold a Ph.D. in Astronomy from Instituto de Astronomia, Geofísica e Ciências Atmosféricas of Universidade de São Paulo. Currently, I am a postdoctoral researcher at Instituto de Física of Universidade de São Paulo. I am member of the S-PLUS and COIN collaborations. For S-PLUS, I have been involved in the data releases development since 2021 with the S-PLUS DR2. I am leading the search for quasar candidates (including anomalous quasars) and estimation of photometric redshifts for S-PLUS using a variety of Machine Learning and Deep Learning techniques. I am former director (2022) and former vice-director (2023) of Research and Development of Turing USP, a student organization focused on artificial intelligence. I see myself as a problem-solver, I enjoy working with any type of data or astronomical object and I love new challenges.

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Education

PhD in Astronomy, 2024

Universidade de São Paulo
BSc in Astronomy, 2016

Universidade de São Paulo
BSc in Statistics, 2013

Universidade de São Paulo

Interests

Extragalactic and Stellar Astrophysics
High-redshift Universe
Machine Learning and Deep Learning
Astrostatistics
Software Development

Research

A brief summary of my academic experience

Postdoctoral program at Instituto de Física of Universidade de São Paulo

Supervisor: prof. Luis Raul Abramo

2024 – Present Seattle, US

1-year internship at University of Washington (Doctorate)

Supervisor: prof. Željko Ivezić

2022 – 2023 Seattle, US

Doctorate program in Astronomy at Universidade de São Paulo

Supervisor: prof. Claudia Mendes de Oliveira (IAG-USP)

2018 – 2024 São Paulo, BR

Thesis title: “Classification and redshift estimation of new quasars in photometric surveys, using S-PLUS as study case”

Our object classifier is the current primary source to photometrically select stars, quasars or galaxies in S-PLUS DR2, DR3, and DR4. During these years, I had experience with querying, cross-matching and/or analyzing data from S-PLUS, SDSS, WISE, GAIA, GALEX, GAMA, and ALHAMBRA surveys. I have been working on this project since my first year of undergraduate in Astronomy (2014), being supervised by Prof. Nina T. S. Hirata from the Computer Science department of IME-USP at that time.

Proposal’s awarded observing time (PI)

2018 – 2018

With the goal of doing spectroscopic follow-up of quasar candidates, we were awarded:

8 hours of observing time with SOAR telescope using Goodman spectrograph
20 hours of observing time with GEMINI telescope using GMOS-S spectrograph

Undergraduate projects (Statistics)

2013 – 2013

During my final year, I worked on two projects coordinated by researchers in Medicine and in Archeology:

Analyses of the risk factors for mortality of patients after liver transplant
Classification of excavated stones from the archaeological site of Boqueirão da Pedra Furada: natural breakage or human-modified?

Accomplishments

Courses and workshops

Neural Networks and Deep Learning

Coursera 2020

See certificate

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Coursera 2020

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First School on Data Science and Machine Learning

ICTP-SAIFR 2019

Introduction to Deep Learning in Astronomy

IAG-USP 2019

Design and Evaluation of Extension Programs

IAG-USP 2019

Featured Publications

The Quasar Catalogue for S-PLUS DR4 (QuCatS) and the estimation of photometric redshifts

Nakazono et al.

April 2024 MNRAS, 531, 327-339

This paper provides a catalogue of photometrically selected quasars with the photo-z predictions from three machine learning methods: Random Forest, Flexible Conditional Density Estimation (FlexCoDE), and Bayesian Mixture Density Network (BMDN). The catalogue provided with this work covers the S-PLUS DR4 area (∼3000 square degrees), containing 645 980, 244 912, 144 991 sources with the probability of being a quasar higher than, 80 per cent, 90 per cent, 95 per cent up to r < 21.3 and good photometry quality in the detection image. More quasar candidates can be retrieved from the S-PLUS data base by considering less restrictive selection criteria.

DOI

On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2

Nakazono et al.

November 2021 MNRAS, 507, 5847-5868

This paper provides a catalogue of stars, quasars, and galaxies for S-PLUS DR2 in the Stripe 82 region. We show that narrow-band photometry improve the classification performance. Moreover, we show that our classification is robust against missing values. In this paper, one can find a direct link to download the classification and probabilities for Stripe 82 S-PLUS DR. Classification and probabilities for all released S-PLUS fields up to this date are avaialble through https://splus.cloud (the documentation page in this website reports the updated classification performance for S-PLUS DR4).

DOI

List of peer-reviewed publications

Nakazono et al. (2024). The Quasar Catalogue for S-PLUS DR4 (QuCatS) and the estimation of photometric redshifts. MNRAS, 531, 327-339.

DOI

Pessi et al. (2024). ELEPHANT: ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients. A&A, in press.

Herpich et al. (2024). The Fourth S-PLUS Data Release: 12-filter photometry covering ~3000 square degrees in the southern hemisphere. A&A, in press.

Lima et al. (2022). Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?. Astronomy and Computing, 38, 100510.

DOI

Almeida-Fernandes et al. (2022). Data Release 2 of S-PLUS: Accurate template-fitting based photometry covering 1000 deg2 in 12 optical filters. MNRAS, 511, 4590-4618.

DOI

Nakazono et al. (2021). On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2. MNRAS, 507, 5847-5868.

DOI

Molino et al. (2020). Assessing the photometric redshift precision of the S-PLUS survey: the Stripe-82 as a test-case. MNRAS, 499, 3884-3908.

DOI

Mendes de Oliveira et al. (2020). The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies, and redshifts with 12 optical filters. MNRAS, 489, 241-267.

DOI

Jeram et al. (2020). An Extremely Bright QSO at z = 2.89. The Astrophysical Journal, 99, 1, 76.

DOI

Gallery

Outreach activities and workshops