Sean T. McWilliams

We present a highly accurate, fully analytical model for the late inspiral, merger, and ringdown of black-hole binaries with arbitrary mass ratios and spin vectors, including the contributions of harmonics beyond the fundamental mode. This model assumes only that nonlinear effects remain small throughout the entire coalescence, and is developed based on a physical understanding of the dynamics of late stage binary evolution, in particular on the tendency of the dynamical binary spacetime to behave like a linear perturbation of the static merger-remnant spacetime, even at times before the merger has occurred. We demonstrate that our model agrees with the most accurate numerical relativity results to within their own uncertainties throughout the merger-ringdown phase, and it does so for example cases spanning the full range of binary parameter space that is currently testable with numerical relativity. Furthermore, our model maintains accuracy back to the innermost stable circular orbit of the merger-remnant spacetime over much of the relevant parameter space, greatly decreasing the need to introduce phenomenological degrees of freedom to describe the late inspiral.

Daniel M. Siegel, Jennifer Barnes, Brian D. Metzger

The binary neutron star merger GW170817 detected by Advanced LIGO/Virgo was followed by a short gamma-ray burst (GRB) and thermal radiation ('kilonova') powered by the radioactive decay of heavy nuclei synthesized in the merger ejecta by the rapid neutron capture process ('r-process'). The large inferred quantity of ejecta is best understood as originating in an outflow from the accretion disk surrounding the newly-formed black hole, the same engine that was likely responsible for the GRB jet. Similar accretion flows accompany the collapse of rotating massive stars ('collapsars'), powering the class of long GRBs and their associated supernovae. Here we show that collapsar accretion disks also produce neutron-rich outflows that synthesize heavy r-process nuclei, despite the comparatively proton-rich composition of the infalling star. Though occurring less frequently than mergers, the much greater accreted mass in collapsars---and their correspondingly larger disk wind ejecta---implicate them as dominant contributors to the Galactic r-process. Collapsars provide the rare r-process source needed to promptly enrich a small fraction of ultra-faint dwarf galaxies early in the Universe which is also compatible with the Galactic chemical enrichment of europium relative to iron over longer timescales. We predict an excess in the late-time near-infrared emission from GRB supernovae, testable by future observations.

Gaoqing Cao, Shu Lin

In this work, we propose a new source for gravitational wave (GW) radiation associated with the quantum chromodynamics (QCD) phase transition in the inner cores of neutron stars. The mechanism is based on the bubble dynamics during the first-order phase transition from nuclear matter to quark matter. We identify the characteristic frequency to be of order $\omega_c\sim 10^6~{\rm rad/s}$ for this kind of sources and the strain magnitude ($h\sim 10^{-24}$ for a neutron star at a distance of $0.1~{\rm Mpc}$) reachable by future GW detectors. The GW spectra are shown to be useful to check the transition nature at high baryon chemical potential as well as to constrain the radius and density of the inner cores, which are still indistinct up to now.

David Alonso, Javier Sanchez, Anže Slosar

The pseudo-$C_\ell$ is an algorithm for estimating the angular power and cross-power spectra that is very fast and, in realistic cases, also nearly optimal. The algorithm can be extended to deal with contaminant deprojection and $E/B$ purification, and can therefore be applied in a wide variety of scenarios of interest for current and future cosmological observations. This paper presents NaMaster, a public, validated, accurate and easy-to-use software package that, for the first time, provides a unified framework to compute angular cross-power spectra of any pair of spin-0 or spin-2 fields, contaminated by an arbitrary number of linear systematics and requiring $B$- or $E$-mode purification, both on the sphere or in the flat-sky approximation. We describe the mathematical background of the estimator, including all the features above, and its software implementation in NaMaster. We construct a validation suite that aims to resemble the types of observations that next-generation large-scale structure and ground-based CMB experiments will face, and use it to show that the code is able to recover the input power spectra in the most complex scenarios with no detectable bias. NaMaster can be found at https://github.com/LSSTDESC/NaMaster, and is provided with comprehensive documentation and a number of code examples.

Alex Malz, Renée Hložek, Tarek Allam Jr, Anita Bahmanyar, Rahul Biswas, Mi Dai, Lluís Galbany, Emille Ishida, Saurabh Jha, David Jones, Rick Kessler, Michelle Lochner, Ashish Mahabal, Kaisey Mandel, Rafael Martínez-Galarza, Jason McEwen, Daniel Muthukrishna, Gautham Narayan, Hiranya Peiris, Christina Peters, Christian Setzer, LSST Dark Energy Science Collaboration, LSST Transients, Variable Stars Science Collaboration

Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of their underlying physical processes. However, upcoming deep photometric surveys, including the Large Synoptic Survey Telescope (LSST), will produce a deluge of low signal-to-noise data for which traditional labeling procedures are inappropriate. Probabilistic classification is more appropriate for the data but are incompatible with the traditional metrics used on deterministic classifications. Furthermore, large survey collaborations intend to use these classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks probabilistic classifications and must serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) is an open competition aiming to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community both within and outside astronomy. Using mock classification probability submissions emulating archetypes of those anticipated of PLAsTiCC, we compare the sensitivity of metrics of classification probabilities under various weighting schemes, finding that they yield qualitatively consistent results. We choose as a metric for PLAsTiCC a weighted modification of the cross-entropy because it can be meaningfully interpreted. Finally, we propose extensions of our methodology to ever more complex challenge goals and suggest some guiding principles for approaching the choice of a metric of probabilistic classifications.