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      Evidence for mass-dependent peculiar velocities in compact object binaries: towards better constraints on natal kicks

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          ABSTRACT

          We compile a catalogue of low-mass and high-mass X-ray binaries, some recently reported binaries that likely host a neutron star (NS) or a black hole (BH), and binary pulsars (a pulsar and a non-degenerated companion) that have measured systemic radial velocities (γ). Using Gaia and radio proper motions together with γ, we integrate their Galactic orbits and infer their post-supernova (post-SN) 3D peculiar velocities ($v_\mathrm{pec }^{z=0}\(at Galactic plane crossing); these velocities bear imprints of the natal kicks that compact objects received at birth. With the sample totalling 85 objects, we model the overall distribution of \)v_\mathrm{pec }^{z=0}\(and find a two-component Maxwellian distribution with a low- (\)\sigma _v \approx 21\, \mathrm{km s^{-1}}$) and a high-velocity ($\sigma _v \approx 107\, \mathrm{km s^{-1}}$) component. A further comparison between distributions of binary subgroups suggests that binaries hosting high-mass donors/luminous companions mostly have $v_\mathrm{pec }^{z=0}\lesssim 100\, \mathrm{km s^{-1}}$, while binaries with low-mass companions exhibit a broader distribution that extends up to $\sim 400\, \mathrm{km s^{-1}}$. We also find significant anticorrelations of $v_\mathrm{pec }^{z=0}\(with binary total mass (Mtot) and orbital period (Porb), at over 99 per cent confidence. Specifically, our fit suggests \)v_\mathrm{pec }^{z=0}\propto M_\mathrm{tot}^{-0.5}\(and \)v_\mathrm{pec }^{z=0}\propto P_\mathrm{orb}^{-0.2}$. Discussions are presented on possible interpretation of the correlations in the context of kinematics and possible biases. The sample should enable a range of follow-up studies on compact object binary kinematics and evolution.

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          Matplotlib: A 2D Graphics Environment

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            Array programming with NumPy

            Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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              Inference from Iterative Simulation Using Multiple Sequences

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                Author and article information

                Contributors
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                Journal
                Monthly Notices of the Royal Astronomical Society
                Oxford University Press (OUP)
                0035-8711
                1365-2966
                October 2023
                August 09 2023
                October 2023
                August 09 2023
                July 27 2023
                : 525
                : 1
                : 1498-1519
                Article
                10.1093/mnras/stad2226
                7057777b-3589-4ce6-a10c-f1918cb4e68d
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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