On the detectability of ultra-compact binary pulsar systems
Nihan Pol, Maura McLaughlin, Duncan R. Lorimer, Nathan Garver-Daniels

TL;DR
This paper uses neural networks to model and analyze the detectability of ultra-compact binary pulsar systems, providing estimates of their population and detection probabilities with current and future radio surveys.
Contribution
It introduces a neural network-based method integrated with population synthesis to assess the detectability and population limits of ultra-compact binary pulsars.
Findings
Binary neutron star systems with unequal masses are easier to detect.
Estimated upper limits of 1450 and 1100 ultra-compact NS--WD and DNS systems in the Milky Way.
Current Arecibo surveys have a 50-80 ext{%} chance of detection with current times, increasing with optimized integration.
Abstract
Using neural networks, we integrate the ability to account for Doppler smearing due to a pulsar's orbital motion with the pulsar population synthesis package \psrpoppy\ to develop accurate modeling of the observed binary pulsar population. As a first application, we show that binary neutron star systems where the two components have highly unequal mass are, on average, easier to detect than systems which are symmetric in mass. We then investigate the population of ultra-compact () neutron star--white dwarf (NS--WD) and double neutron star (DNS) systems which are promising sources for the Laser Interferometer Space Antenna gravitational-wave detector. Given the non-detection of these systems in radio surveys thus far, we estimate a 95\% confidence upper limit of 1450 and 1100 ultra-compact NS--WD and DNS systems in the Milky…
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