Performance analysis of the Karhunen–Loève Transform for artificial and astrophysical transmissions: denoizing and detection

In this work, we propose a new method of computing the Karhunen–Loève Transform (KLT) applied to complex voltage data for the detection and noise level reduction in astronomical signals. We compared this method with the standard KLT techniques based on the Toeplitz correlation matrix and we conducted a performance analysis for the detection and extraction of astrophysical and artificial signals via Monte Carlo (MC) simulations. We applied our novel method to a real data study-case: the Voyager 1 telemetry signal. We evaluated the KLT performance in an astrophysical context: our technique provides a remarkable improvement in computation time and MC simulations show significant reconstruction results for signal-to-noise ratio (SNR) down to −10 dB and comparable results with standard signal detection techniques. The application to artificial signals, such as the Voyager 1 data, shows a notable gain in SNR after the KLT

Performance analysis of the Karhunen–Loève Transform for artificial and astrophysical transmissions: denoizing and detection

In this work, we propose a new method of computing the Karhunen–Loève Transform (KLT) applied to complex voltage data for the detection and noise level reduction in astronomical signals. We compared this method with the standard KLT techniques based on the Toeplitz correlation matrix and we conducted a performance analysis for the detection and extraction of astrophysical and artificial signals via Monte Carlo (MC) simulations. We applied our novel method to a real data study-case: the Voyager 1 telemetry signal. We evaluated the KLT performance in an astrophysical context: our technique provides a remarkable improvement in computation time and MC simulations show significant reconstruction results for signal-to-noise ratio (SNR) down to −10 dB and comparable results with standard signal detection techniques. The application to artificial signals, such as the Voyager 1 data, shows a notable gain in SNR after the KLT

Numerical uncertainty propagation to the number of possible Galactic Habitable Islands in the Milky Way

The model to calculate the average age of potential intelligent life in the Milky Way, that we presented at the IAC 2021, depends on different inputs, like gas surface density, star total mass, initial mass function, supernovae explosions and metallicity. The value of each input comes with an uncertainty due to the amount of different measurements and their complexity. Those uncertainties can be propagated to estimate the uncertainty on the average age of potential intelligent life in the Milky Way; it will thus be possible to tell the inputs that mostly contribute to the final uncertainty and what measurement must be refined first to obtain a more precise estimate. In addition, the model will be integrated to take into account a variance with respect to a 2D radial symmetric model