Astronomical image processing and analysis using deep convolutional models

• Grant number: 18/25671-9
• Support Opportunities: Scholarships in Brazil - Master
• Start date: February 01, 2019
• End date: December 31, 2019
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: Microsoft Research
• Principal Investigator: Nina Sumiko Tomita Hirata
• Grantee: Ana Carolina Rodrigues Cavalcante Martinazzo
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Company: Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME)
• Associated research grant: 17/25835-9 - Understanding images and deep learning models, AP.PITE

Abstract

Astronomy has entered the era of large photometric surveys, transitioning from a relatively data-scarce field of study to a very data-rich one. The images being collected by these new surveys present challenging features such as large size (may be a order of magnitude larger than the images we are used to working with), high number of channels (may reach a few dozen), low signal-to-noise ratio, signal saturation, missing information, unknown objects, and so on. Besides that, there is an increasingly large amount of images available. In this scenario, deep-learning approaches emerge as a very suitable alternative for extracting information from data.Our main proposal is to explore structural information, both spatial and spectral, from astronomical images by making use of deep learning techniques. We expect to develop deep network models that generate rich and general representations of the images, appropriate for a variety of tasks such as object detection, outlier detection or clustering. This would enable the development of end-to-end models for large scale image processing and analysis, which in turn may lead to more precise results and new discoveries in astronomy. As case studies, we intend to explore the problems of automatic detection of objects such as stars and galaxies as well as their fine-grained classification.This project counts with the collaboration of researchers from the Institute of Astronomy, Geophysics and Atmospheric Sciences of the University of São Paulo (IAG-USP), who lead the Southern Photometric Local Universe Survey (S-PLUS), an ongoing sky survey.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

MARTINAZZO, ANA; ESPADOTO, MATEUS; HIRATA, NINA S. T.; IEEE COMP SOC. Self-supervised Learning for Astronomical Image Classification. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), v. N/A, p. 7-pg., 2021-01-01. (17/25835-9, 18/25671-9)

NAKAZONO, L.; DE OLIVEIRA, C. MENDES; HIRATA, N. S. T.; JERAM, S.; QUEIROZ, C.; EIKENBERRY, STEPHEN S.; GONZALEZ, A. H.; ABRAMO, R.; OVERZIER, R.; ESPADOTO, M.; et al. On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2. Monthly Notices of the Royal Astronomical Society, v. 507, n. 4, p. 5847-5868, NOV 2021. (18/09165-6, 19/06766-1, 19/10923-5, 15/22308-2, 19/26492-3, 18/20977-2, 14/10566-4, 19/01312-2, 18/25671-9, 19/23388-0, 17/25835-9, 15/11442-0, 16/12331-0)

MARTINAZZO, ANA; ESPADOTO, MATEUS; HIRATA, NINA S. T.; FARINELLA, GM; RADEVA, P; BRAZ, J. Deep Learning for Astronomical Object Classification: A Case Study. VISAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4: VISAPP, v. N/A, p. 9-pg., 2020-01-01. (17/25835-9, 18/25671-9, 15/22308-2)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

MARTINAZZO, Ana Carolina Rodrigues Cavalcante. A self-supervised learning approach for astronomical images. 2021. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME/SBI) São Paulo.