Investigating all-sky Frequency Hough performances for neutron stars

Between the estimated population of Neutron Stars (NSs) and the actual number present in the catalogs, there is a huge gap: O(10${}^{8-9}$) vs O(10${}^3$). Among the different search techniques for Continuous gravitational waves (CWs), the all-sky could help to reduce the discrepancy. We focus on the all-sky CW pipeline Frequency Hough (FH), which operates without prior knowledge of the source parameters ($f,\dot{f},\lambda ,\beta$). Here, we present a Machine Learning strategy, diverging from the standard follow-up(FU) of the FH pipeline. We study the performance with real interferometer data, until reaching $h$ value subthreshold for the standard FU procedure ($C{R}_{thr}=5$), with encouraging classification results.