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Astrophysics - High Energy Astrophysical Phenomena and General Relativity and Quantum Cosmology

The LIGO/Virgo collaborations recently announced the detection of a likely binary neutron star merger, GW190425. The total mass of GW190425 is significantly larger than the masses of Galactic double neutron stars known through radio astronomy. This suggests that GW190425 formed differently from Galactic double neutron stars. We hypothesize that GW190425 formed via unstable "case BB" mass transfer. According to this hypothesis, the progenitor of GW190425 was a binary consisting of a neutron star and a ${\sim} 4-5 {M_\odot}$ helium star, which underwent a common-envelope process. Following the supernovae of the helium star core, a tight, eccentric, double neutron star was formed, which merged in ${\lesssim}$ 10 Myr. The helium star progenitor may explain the unusually large mass of GW190425, while the short time to merger may explain why we do not see similar systems in radio. In order to test this hypothesis, we measure the eccentricity of GW190425 using publicly available LIGO/Virgo data. We constrain the eccentricity at 10 Hz to be $e \leq 0.007$ with $90\%$ confidence. This result provides no evidence for or against the unstable mass transfer scenario because the binary is likely to have circularized to $e\lesssim10^{-4}$ by the time it entered the LIGO/Virgo band. Future detectors operating in lower frequency bands will enable us to discern the formation channel of mergers similar to GW190425 using eccentricity measurements.
Comment: 5 pages, 3 figures, Accepted for publication in MNRAS Letters

Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics, and General Relativity and Quantum Cosmology

The conventional wisdom, dating back to 2012, is that the mass distribution of Galactic double neutron stars is well-fit by a Gaussian distribution with a mean of $1.33 M_\odot$ and a width of $0.09 M_\odot$. With the recent discovery of new Galactic double neutron stars and GW170817, the first neutron star merger event to be observed with gravitational waves, it is timely to revisit this model. In order to constrain the mass distribution of double neutron stars, we perform Bayesian inference using a sample of 17 Galactic double neutron stars effectively doubling the sample used in previous studies. We expand the space of models so that the recycled neutron star need not be drawn from the same distribution as the non-recycled companion. Moreover, we consider different functional forms including uniform, single-Gaussian, and two-Gaussian distributions. While there is insufficient data to draw firm conclusions, we find positive support (a Bayes factor of 9) for the hypothesis that recycled and non-recycled neutron stars have distinct mass distributions. The most probable model---preferred with a Bayes factor of 29 over the conventional model---is one in which the recycled neutron star mass is distributed according to a two-Gaussian distribution and the non-recycled neutron star mass is distributed uniformly. We show that precise component mass measurements of $\approx 20$ double neutron stars are required in order to determine with high confidence (a Bayes factor of 150) if recycled and non-recycled neutron stars come from a common distribution. Approximately $60$ are needed in order to establish the detailed shape of the distributions.
Comment: Minor update of PSR J1913+1102 masses, 13 pages, 7 figures, 5 tables

Computer Science - Robotics

We present a self-contained, soft robotic hand composed of soft pneumatic actuator modules that are equipped with strain and pressure sensing. We show how this data can be used to discern whether a grasp was successful. Co-locating sensing and embedded computation with the actuators greatly simplifies control and system integration. Equipped with a small pump, the hand is self-contained and needs only power and data supplied by a single USB connection to a PC. We demonstrate its function by grasping a variety of objects ranging from very small to large and heavy objects weighing more than the hand itself. The presented system nicely illustrates the advantages of soft robotics: low cost, low weight, and intrinsic compliance. We exploit morphological computation to simplify control, which allows successful grasping via underactuation. Grasping indeed relies on morphological computation at multiple levels, ranging from the geometry of the actuator which determines the actuator's kinematics, embedded strain sensors to measure curvature, to maximizing contact area and applied force during grasping. Morphological computation reaches its limitations, however, when objects are too bulky to self-align with the gripper or when the state of grasping is of interest. We therefore argue that efficient and reliable grasping also requires not only intrinsic compliance, but also embedded sensing and computation. In particular, we show how embedded sensing can be used to detect successful grasps and vary the force exerted onto an object based on local feedback, which is not possible using morphological computation alone.