Photometric time series of stars with planets detected by the TESS satellite

• Grant number: 25/23921-1
• Support Opportunities: Scholarships in Brazil - Program to Stimulate Scientific Vocations
• Start date: January 12, 2026
• End date: February 21, 2026
• Field of knowledge: Physical Sciences and Mathematics - Astronomy
• Principal Investigator: José Renan de Medeiros
• Grantee: Gabriela Zardini Bartolomeu
• Host Institution: Centro de Ciências Exatas e da Terra (CCET). Universidade Federal do Rio Grande do Norte (UFRN). Natal , SP, Brazil

Abstract

The activities to be developed by Fellow Gabriela Zardini Bartolomeu, at the Center for Observational Astronomy and Instrumentation at the Federal University of Rio Grande do Norte, will focus directly on characterizing the temporal variability of spectral type M stars, hosts of planets discovered via photometric transit within the TESS Space Mission (Transiting Exoplanet Survey Satellite; Ricker et al. 2015, JATIS, 1, 014003). This mission, launched in April 2018, has discovered approximately 7,100 planets to date, orbiting nearby bright stars.The Fellow will focus on the processing and analysis of stellar photometric time series (light curves), using a multi-procedure consisting of the Lomb-Scargle periodogram technique, the Fast Fourier Transform (FFT), and the Wavelet Transform.The FFT, a computationally faster version of the discrete Fourier transform, will be used, and we will use an algorithm based on Cooley & Tukey (1965) and Sanchis-Ojeda et al. (2013, 2014), but optimized for the time series collected by TESS. The Lomb-Scargle technique (Lomb 1976; Scargle 1982) is a well-known algorithm that can provide Fourier-like periodograms of real (discrete) data. Its main advantage over the FFT is that it can handle time series that are not equally spaced in time. Periodograms can therefore be obtained directly from observational time series with their original temporal sampling, without the need for any rebinning or interpolation. The wavelet transform (e.g., Grossmann & Morlet 1984) is a powerful tool for analyzing time series in the time-frequency domain-that is, decomposing periodicities as sections of power spectra throughout the temporal window of the data. The time-frequency diagram of a wavelet transform-that is, the wavelet map or local wavelet spectrum-decomposes a signal into all frequencies naturally, within a confidence region, without the need to define a length for the interval under analysis. Furthermore, a global power spectrum can also be obtained by integrating the wavelet map along the time axis. This global wavelet spectrum provides insight into the main periodicities present in a time series, which can be compared with other power spectra, such as FFT and Lomb-Scargle. In general, the wavelet technique is a useful tool for analyzing non-stationary and non-periodic signals, revealing features that can vary in both time and frequency (Burrus et al. 1998).