Importance of Tides for Periastron Precession in Eccentric Neutron Star - White Dwarf Binaries

Although not nearly as numerous as binaries with two white dwarfs, eccentric neutron star-white dwarf (NS-WD) binaries are important gravitational-wave (GW) sources for the next generation of space-based detectors sensitive to low frequency waves. Here we investigate periastron precession in these sources as a result of general relativistic, tidal, and rotational effects; such precession is expected to be detectable for at least some of the detected binaries of this type. Currently, two eccentric NS-WD binaries are known in the galactic field, PSR J1141-6545 and PSR B2303+46, both of which have orbits too wide to be relevant in their current state to GW observations. However, population synthesis studies predict the existence of a significant Galactic population of such systems. Though small in most of these systems, we find that tidally induced periastron precession becomes important when tides contribute to more than 3% of the total precession rate. For these systems, accounting for tides when analyzing periastron precession rate measurements can improve estimates of the WD component mass inferred and, in some cases, will prevent us from misclassifying the object. However, such systems are rare due to rapid orbital decay. To aid the inclusion of tidal effects when using periastron precession as a mass measurement tool, we derive a function that relates the WD radius and periastron precession constant to the WD mass.Comment: Published in The Astrophysical Journa

Machine-directed gravitational-wave counterpart discovery

Joint observations in electromagnetic and gravitational waves shed light on the physics of objects and surrounding environments with extreme gravity that are otherwise unreachable via siloed observations in each messenger. However, such detections remain challenging due to the rapid and faint nature of counterparts. Protocols for discovery and inference still rely on human experts manually inspecting survey alert streams and intuiting optimal usage of limited follow-up resources. Strategizing an optimal follow-up program requires adaptive sequential decision-making given evolving light curve data that (i) maximizes a global objective despite incomplete information and (ii) is robust to stochasticity introduced by detectors/observing conditions. Reinforcement learning (RL) approaches allow agents to implicitly learn the physics/detector dynamics and the behavior policy that maximize a designated objective through experience. To demonstrate the utility of such an approach for the kilonova follow-up problem, we train a toy RL agent for the goal of maximizing follow-up photometry for the true kilonova among several contaminant transient light curves. In a simulated environment where the agent learns online, it achieves 3x higher accuracy compared to a random strategy. However, it is surpassed by human agents by up to a factor of 2. This is likely because our hypothesis function (Q that is linear in state-action features) is an insufficient representation of the optimal behavior policy. More complex agents could perform at par or surpass human experts. Agents like these could pave the way for machine-directed software infrastructure to efficiently respond to next generation detectors, for conducting science inference and optimally planning expensive follow-up observations, scalably and with demonstrable performance guarantees.Comment: Submitted to the Astrophysical Journal; Comments welcome

The Mass Distribution of Stellar-Mass Black Holes

We perform a Bayesian analysis of the mass distribution of stellar-mass black holes using the observed masses of 15 low-mass X-ray binary systems undergoing Roche lobe overflow and five high-mass, wind-fed X-ray binary systems. Using Markov Chain Monte Carlo calculations, we model the mass distribution both parametrically---as a power law, exponential, gaussian, combination of two gaussians, or log-normal distribution---and non-parametrically---as histograms with varying numbers of bins. We provide confidence bounds on the shape of the mass distribution in the context of each model and compare the models with each other by calculating their relative Bayesian evidence as supported by the measurements, taking into account the number of degrees of freedom of each model. The mass distribution of the low-mass systems is best fit by a power-law, while the distribution of the combined sample is best fit by the exponential model. We examine the existence of a "gap" between the most massive neutron stars and the least massive black holes by considering the value, M_1%, of the 1% quantile from each black hole mass distribution as the lower bound of black hole masses. The best model (the power law) fitted to the low-mass systems has a distribution of lower-bounds with M_1% > 4.3 Msun with 90% confidence, while the best model (the exponential) fitted to all 20 systems has M_1% > 4.5 Msun with 90% confidence. We conclude that our sample of black hole masses provides strong evidence of a gap between the maximum neutron star mass and the lower bound on black hole masses. Our results on the low-mass sample are in qualitative agreement with those of Ozel, et al (2010).Comment: 56 pages, 22 figures, 9 tables, as accepted by Ap

Inferencing Progenitor and Explosion Properties of Evolving Core-collapse Supernovae from Zwicky Transient Facility Light Curves

We analyze a sample of 45 Type II supernovae from the Zwicky Transient Facility (ZTF) public survey using a grid of hydrodynamical models in order to assess whether theoretically-driven forecasts can intelligently guide follow up observations supporting all-sky survey alert streams. We estimate several progenitor properties and explosion physics parameters including zero-age-main-sequence (ZAMS) mass, mass-loss rate, kinetic energy, 56Ni mass synthesized, host extinction, and the time of explosion. Using complete light curves we obtain confident characterizations for 34 events in our sample, with the inferences of the remaining 11 events limited either by poorly constraining data or the boundaries of our model grid. We also simulate real-time characterization of alert stream data by comparing our model grid to various stages of incomplete light curves (t less than 25 days, t less than 50 days, all data), and find that some parameters are more reliable indicators of true values at early epochs than others. Specifically, ZAMS mass, time of explosion, steepness parameter beta, and host extinction are reasonably constrained with incomplete light curve data, whereas mass-loss rate, kinetic energy and 56Ni mass estimates generally require complete light curves spanning greater than 100 days. We conclude that real-time modeling of transients, supported by multi-band synthetic light curves tailored to survey passbands, can be used as a powerful tool to identify critical epochs of follow up observations. Our findings are relevant to identify, prioritize, and coordinate efficient follow up of transients discovered by Vera C. Rubin Observatory.Comment: 27 pages, 14 figures, Accepted to The Astrophysical Journa

Updated observing scenarios and multi-messenger implications for the International Gravitational-wave Network's O4 and O5

Advanced LIGO and Virgo's third observing run brought another binary neutron star merger (BNS) and the first neutron-star black-hole (NSBH) mergers. While no confirmed kilonovae (KNe) was identified in conjunction with any of these events, continued improvements of analyses surrounding GW170817 allow us to project constraints on the Hubble Constant ($H_0$), the Galactic enrichment from $r$-process nucleosynthesis, and ultra-dense matter possible from forthcoming events. Here, we describe the expected constraints based on the latest expected event rates from the international gravitational-wave network (IGWN) and analyses of GW170817. We show the expected detection rate of gravitational waves and their counterparts, as well as how sensitive potential constraints are to the observed numbers of counterparts. We intend this analysis as support for the community when creating scientifically-driven electromagnetic follow-up proposals. During the next observing run O4, we predict an annual detection rate of electromagnetic counterparts from BNS of $0.43^{+0.58}_{-0.26}$ ($1.97^{+2.68}_{-1.2}$) for the Zwicky Transient Facility (Rubin Observatory)