2026A&A...707A.125L

Context. The search for radio signals from exoplanets or star-planet interactions is a topic of major scientific interest, as it is likely the best way to detect and measure a planetary magnetic field and, therefore, to probe the inner structure of exoplanets. However, detecting these radio emissions is challenging, since they are anisotropic by nature, sporadic, and of low intensity because of their great distances, and because the sky cannot be monitored continuously.
Aims. The aim of this article is to demonstrate the relevance of using statistical tools to detect periodic radio signals in unevenly spaced observations and to identify the implications of the measured period.
Methods. The identification of periodic radio signals was achieved here through a Lomb-Scargle analysis. The technique was first applied to simulated astrophysical observations with controlled simulated noise. This allowed us to characterise the origin of spurious detection peaks in the resulting periodograms - as well as to identify peaks corresponding to real periods in the studied system - and to combination or beat periods.
Results. The method was validated using a real signal, with ∼1400 hours of data from observations of Jupiter's radio emissions by the NenuFAR radio telescope over more than six years, in order to detect the periodicities of Jovian radio emissions (auroral and induced by the Galilean moons).
Conclusions. We demonstrate with the simulation that the Lomb-Scargle periodogram allows us to correctly identify periodic radio signals, even in a diluted signal. On real measurements, it correctly detects the rotation period of the strong signal produced by Jupiter and the beat period of the emission triggered by the interaction between Jupiter and its Galilean moon Io, but also possibly weaker signals, such as those produced by the interaction between Jupiter and Europa or between Jupiter and Ganymede. It is important to note that secondary peaks in the Lomb-Scargle periodogram appear at the beat and combination periods among all the detected signal periodicities (i.e. real signals, but also periodicities due to regular observation intervals). These secondary peaks can then be used to strengthen the detection of weak signals. Finally, the importance of the number of observation windows used in the Lomb-Scargle analysis is discussed, as well as the data's time and frequency resolutions in increasing its efficiency.