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Extensions to General Relativity (GR) allow the polarization of gravitational waves (GW) from astrophysical sources to suffer from amplitude and velocity birefringence, which respectively induce changes in the ellipticity and orientation of the polarization tensor. We introduce a multi-messenger approach to test this polarization behavior of GWs during their cosmological propagation using binary sources, for which the initial polarization is determined by the inclination and orientation angles of the orbital angular momentum vector with respect to the line of sight. In particular, we use spatially-resolved radio imaging of the jet from a binary neutron star (BNS) merger to constrain the orientation angle and hence the emitted polarization orientation of the GW signal at the site of the merger, and compare to that observed on Earth by GW detectors. For GW170817 we constrain the deviation from GR due to amplitude birefringence to $\kappa_A = -0.12^{+0.60}_{-0.61}$, while the velocity birefringence parameter $\kappa_V$ remains unconstrained. The inability to constrain $\kappa_V$ is due to the fact that Virgo did not detect GW170817, and measurements of the polarization orientation require information from a combination of multiple detectors with different alignments. For this reason, we also mock future BNS mergers with resolved afterglow proper motion and project that $\kappa_V$ could be constrained to a precision of $5\,$rad (corresponding to an angular shift of the GW polarization of $\delta\phi_V\approx 0.2\,$rad for a BNS at $100\,$Mpc) by a future network of third-generation ground-based GW detectors such as Cosmic Explorer and the radio High Sensitivity Array. Crucially, this velocity birefringence effect cannot be constrained with dark binary mergers as it requires polarization information at the emission time, which can be provided only by electromagnetic emission.

We present AspGap, a new approach to infer stellar labels from low-resolution Gaia XP spectra, including precise [$\alpha$/M] estimates for the first time. AspGap is a neural-network based regression model trained on APOGEE spectra. In the training step, AspGap learns to use XP spectra not only to predict stellar labels but also the high-resolution APOGEE spectra that lead to the same stellar labels. The inclusion of this last model component -- dubbed the hallucinator -- creates a more physically motivated mapping and significantly improves the prediction of stellar labels in the validation, particularly of [$\alpha$/M]. For giant stars, we find cross-validated rms accuracies for Teff, log g, [M/H], [$\alpha$/M] of ~1%, 0.12 dex, 0.07 dex, 0.03 dex, respectively. We also validate our labels through comparison with external datasets and through a range of astrophysical tests that demonstrate that we are indeed determining [$\alpha$/M] from the XP spectra, rather than just inferring it indirectly from correlations with other labels. We publicly release the AspGap codebase, along with our stellar parameter catalog for all giants observed by Gaia XP. AspGap enables new insights into the formation and chemo-dynamics of our Galaxy by providing precise [$\alpha$/M] estimates for 23 million giant stars, including 12 million with radial velocities from Gaia., Comment: 29 pages, 19 figures, submitted to ApJS

We investigate the possibility of improving the accuracy of the phenomenological waveform model, IMRPhenomD, by jointly optimizing all the calibration coefficients at once, given a set of numerical relativity (NR) waveforms. When IMRPhenomD was first calibrated to NR waveforms, different parts (i.e., the inspiral, merger, and ringdown) of the waveform were calibrated separately. Using ripple, a library of waveform models compatible with automatic differentiation, we can, for the first time, perform gradient-based optimization on all the waveform coefficients at the same time. This joint optimization process allows us to capture previously ignored correlations between separate parts of the waveform. We found that after recalibration, the median mismatch between the model and NR waveforms decreases by 50%. We further explore how different regions of the source parameter space respond to the optimization procedure. We find that the degree of improvement correlates with the spins of the source. This work shows a promising avenue to help understand and treat systematic error in waveform models., Comment: 11 pages, 9 figures

Convolutional neural networks and their ilk have been very successful for many learning tasks involving images. These methods assume that the input is a scalar image representing the intensity in each pixel, possibly in multiple channels for color images. In natural-science domains however, image-like data sets might have vectors (velocity, say), tensors (polarization, say), pseudovectors (magnetic field, say), or other geometric objects in each pixel. Treating the components of these objects as independent channels in a CNN neglects their structure entirely. Our formulation -- the GeometricImageNet -- combines a geometric generalization of convolution with outer products, tensor index contractions, and tensor index permutations to construct geometric-image functions of geometric images that use and benefit from the tensor structure. The framework permits, with a very simple adjustment, restriction to function spaces that are exactly equivariant to translations, discrete rotations, and reflections. We use representation theory to quantify the dimension of the space of equivariant polynomial functions on 2-dimensional vector images. We give partial results on the expressivity of GeometricImageNet on small images. In numerical experiments, we find that GeometricImageNet has good generalization for a small simulated physics system, even when trained with a small training set. We expect this tool will be valuable for scientific and engineering machine learning, for example in cosmology or ocean dynamics.

The propagation of gravitational waves can reveal fundamental features of the structure of spacetime. For instance, differences in the propagation of gravitational-wave polarizations would be a smoking gun for parity violations in the gravitational sector, as expected from birefringent theories like Chern-Simons gravity. Here we look for evidence of amplitude birefringence in the third catalog of detections by the Laser Interferometer Gravitational Wave Observatory and Virgo through the use of birefringent templates inspired by dynamical Chern-Simons gravity. From $71$ binary-black-hole signals, we obtain the most precise constraints on gravitational-wave amplitude birefringence yet, measuring a birefringent attenuation of $\kappa = -0.019^{+0.038}_{-0.029} \, \mathrm{Gpc}^{-1}$ at $100 \, \mathrm{Hz}$ with $90\%$ credibility, equivalent to a parity-violation energy scale of $M_{\rm PV} \gtrsim 6.8 \times 10^{-21}\, {\rm GeV}$.

We present a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By combining likelihood heterodyning, automatically-differentiable and accelerator-compatible waveforms, and gradient-based Markov chain Monte Carlo (MCMC) sampling enhanced by normalizing flows, we achieve full Bayesian parameter estimation for real events like GW150914 and GW170817 within a minute of sampling time. Our framework does not require pretraining or explicit reparameterizations and can be generalized to handle higher dimensional problems. We present the details of our implementation and discuss trade-offs and future developments in the context of other proposed strategies for real-time parameter estimation. Our code for running the analysis is publicly available on GitHub https://github.com/kazewong/jim.

We propose the use of automatic differentiation through the programming framework jax for accelerating a variety of analysis tasks throughout gravitational wave (GW) science. Firstly, we demonstrate that complete waveforms which cover the inspiral, merger, and ringdown of binary black holes (i.e. IMRPhenomD) can be written in jax and demonstrate that the serial evaluation speed of the waveform (and its derivative) is similar to the lalsuite implementation in C. Moreover, jax allows for GPU-accelerated waveform calls which can be over an order of magnitude faster than serial evaluation on a CPU. We then focus on three applications where efficient and differentiable waveforms are essential. Firstly, we demonstrate how gradient descent can be used to optimize the $\sim 200$ coefficients that are used to calibrate the waveform model. In particular, we demonstrate that the typical match with numerical relativity waveforms can be improved by more than 50% without any additional overhead. Secondly, we show that Fisher forecasting calculations can be sped up by $\sim 100\times$ (on a CPU) with no loss in accuracy. This increased speed makes population forecasting substantially simpler. Finally, we show that gradient-based samplers like Hamiltonian Monte Carlo lead to significantly reduced autocorrelation values when compared to traditional Monte Carlo methods. Since differentiable waveforms have substantial advantages for a variety of tasks throughout GW science, we propose that waveform developers use jax to build new waveforms moving forward. Our waveform code, ripple, can be found at https://github.com/tedwards2412/ripple, and will continue to be updated with new waveforms as they are implemented., Comment: 12 pages, 6 figures, code can be found at https://github.com/tedwards2412/ripple

flowMC is a Python library for accelerated Markov Chain Monte Carlo (MCMC) leveraging deep generative modeling. It is built on top of the machine learning libraries JAX and Flax. At its core, flowMC uses a local sampler and a learnable global sampler in tandem to efficiently sample posterior distributions. While multiple chains of the local sampler generate samples over the region of interest in the target parameter space, the package uses these samples to train a normalizing flow model, then uses it to propose global jumps across the parameter space. The flowMC sampler can handle non-trivial geometry, such as multimodal distributions and distributions with local correlations. The key features of flowMC are summarized in the following list: * Since flowMC is built on top of JAX, it supports gradient-based samplers through automatic differentiation such as MALA and Hamiltonian Monte Carlo (HMC). * flowMC uses state-of-the-art normalizing flow models such as Rational-Quadratic Splines to power its global sampler. These models are very efficient in capturing important features within a relatively short training time. * Use of accelerators such as GPUs and TPUs are natively supported. The code also supports the use of multiple accelerators with SIMD parallelism. * By default, Just-in-time (JIT) compilations are used to further speed up the sampling process. * We provide a simple black box interface for the users who want to use flowMC by its default parameters, yet provide at the same time an extensive guide explaining trade-offs while tuning the sampler parameters. The tight integration of all the above features makes flowMC a highly performant yet simple- to-use package for statistical inference.

We report discovery of a bright, nearby ($G = 13.8;\,\,d = 480\,\rm pc$) Sun-like star orbiting a dark object. We identified the system as a black hole candidate via its astrometric orbital solution from the Gaia mission. Radial velocities validated and refined the Gaia solution, and spectroscopy ruled out significant light contributions from another star. Joint modeling of radial velocities and astrometry constrains the companion mass to $M_2 = 9.62\pm 0.18\,M_{\odot}$. The spectroscopic orbit alone sets a minimum companion mass of $M_2>5\,M_{\odot}$; if the companion were a $5\,M_{\odot}$ star, it would be $500$ times more luminous than the entire system. These constraints are insensitive to the mass of the luminous star, which appears as a slowly-rotating G dwarf ($T_{\rm eff}=5850\,\rm K$, $\log g = 4.5$, $M=0.93\,M_{\odot}$), with near-solar metallicity ($\rm [Fe/H] = -0.2$) and an unremarkable abundance pattern. We find no plausible astrophysical scenario that can explain the orbit and does not involve a black hole. The orbital period, $P_{\rm orb}=185.6$ days, is longer than that of any known stellar-mass black hole binary. The system's modest eccentricity ($e=0.45$), high metallicity, and thin-disk Galactic orbit suggest that it was born in the Milky Way disk with at most a weak natal kick. How the system formed is uncertain. Common envelope evolution can only produce the system's wide orbit under extreme and likely unphysical assumptions. Formation models involving triples or dynamical assembly in an open cluster may be more promising. This is the nearest known black hole by a factor of 3, and its discovery suggests the existence of a sizable population of dormant black holes in binaries. Future Gaia releases will likely facilitate the discovery of dozens more., Comment: 30 pages, 15 figures, 1-2 black holes. Accepted to MNRAS

We report evidence for nonlinear modes in the ringdown stage of the gravitational waveform produced by the merger of two comparable-mass black holes. We consider both the coalescence of black hole binaries in quasicircular orbits and high-energy, head-on black hole collisions. The presence of nonlinear modes in the numerical simulations confirms that general-relativistic nonlinearities are important and must be considered in gravitational-wave data analysis., Comment: 6 + 6 pages, 7 figures. Matches version published in Physical Review Letters

We present an automatic approach to discover analytic population models for gravitational-wave (GW) events from data. As more gravitational-wave (GW) events are detected, flexible models such as Gaussian Mixture Models have become more important in fitting the distribution of GW properties due to their expressivity. However, flexible models come with many parameters that lack physical motivation, making interpreting the implication of these models challenging. In this work, we demonstrate symbolic regression can complement flexible models by distilling the posterior predictive distribution of such flexible models into interpretable analytic expressions. We recover common GW population models such as a power-law-plus-Gaussian, and find a new empirical population model which combines accuracy and simplicity. This demonstrates a strategy to automatically discover interpretable population models in the ever-growing GW catalog, which can potentially be applied to other astrophysical phenomena., Comment: Published in ML4Astro Workshop at ICML 2022. 8 pages, 1 figure. Code at https://github.com/kazewong/SymbolicGWPopulation_paper

One promising way to extract information about stellar astrophysics from gravitational wave catalogs is to compare the catalog to the outputs of stellar population synthesis modeling with varying physical assumptions. The parameter space of physical assumptions in population synthesis is high-dimensional and the choice of parameters that best represents the evolution of a binary system may depend in an as-yet-to-be-determined way on the system's properties. Here we propose a pipeline to simultaneously infer zero-age main sequence properties and population synthesis parameter settings controlling modeled binary evolution from individual gravitational wave observations of merging compact binaries. Our pipeline can efficiently explore the high-dimensional space of population synthesis settings and progenitor system properties for each system in a catalog of gravitational wave observations. We apply our pipeline to observations in the third third LIGO-Virgo Gravitational-Wave Transient Catalog. We showcase the effectiveness of this pipeline with a detailed study of the progenitor properties and population synthesis settings that produce mergers like the observed GW150914. Our pipeline permits a measurement of the variation of population synthesis parameter settings with binary properties, if any; we present inferences for the recent GWTC-3 transient catalog that suggest that the stable mass transfer efficiency parameter may vary with primary black hole mass., Comment: Source code for compiling the paper is publicly hosted on github: https://github.com/kazewong/BackPop. Data is available on zenodo: 10.5281/zenodo.6624911

The population properties of intermediate mass black holes remain largely unknown, and understanding their distribution could provide a missing link in the formation of supermassive black holes and galaxies. Gravitational wave observations can help fill in the gap from stellar mass black holes to supermassive black holes. In our work, we propose a new method for probing lens populations through lensing statistics of gravitational waves, here focusing on inferring the number density of intermediate mass black holes. Using hierarchical Bayesian inference of injected lensed gravitational waves, we find that existing gravitational wave observatories at design sensitivity could either identify an injected number density of $10^6 \mathrm{Mpc}^{-3}$ or place an upper bound of $\lesssim 10^4 \mathrm{Mpc}^{-3}$ for an injected $10^3 \mathrm{Mpc}^{-3}$. More broadly, our method could be applied to probe other forms of compact matter as well.

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$\alpha$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}., Comment: 18 pages, 3 figures. More than 350 Tb of data from thousands of simulations publicly available at https://www.camel-simulations.org

Gravitational-wave population studies have become more important in gravitational-wave astronomy because of the rapid growth of the observed catalog. In recent studies, emulators based on different machine learning techniques are used to emulate the outcomes of the population synthesis simulation with fast speed. In this study, we benchmark the performance of two emulators that learn the truncated power-law phenomenological model by using Gaussian process regression and normalizing flows techniques to see which one is a more capable likelihood emulator in the population inference. We benchmark the characteristic of the emulators by comparing their performance in the population inference to the phenomenological model using mock and real observation data. Our results suggest that the normalizing flows emulator can recover the posterior distribution by using the phenomenological model in the population inference with up to 300 mock injections. The normalizing flows emulator also underestimates the uncertainty for some posterior distributions in the population inference on real observation data. On the other hand, the Gaussian process regression emulator has poor performance on the same task and can only be used effectively in low-dimension cases., Comment: 13 pages, 7 figures

We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2,000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span $\sim$100 million light years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine learning models, CMD is the largest dataset of its kind containing more than 70 Terabytes of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io., Comment: 17 pages, 1 figure. Third paper of a series of four. Hundreds of thousands of labeled 2D maps and 3D grids from thousands of simulated universes publicly available at https://camels-multifield-dataset.readthedocs.io

Despite their potential role as massive seeds for quasars, in dwarf galaxy feedback, and in tidal disruption events, the observational evidence for intermediate-mass black holes (IMBHs) is scarce. LISA may observe stellar-mass black hole binaries orbiting Galactic IMBHs, and reveal the presence of the IMBH by measuring the Doppler shift in the gravitational waveform induced by the binary's radial velocity. We estimate the number of detectable Doppler shift events from the Milky Way globular clusters (assuming they host IMBHs) and we find that it decreases with the IMBH mass. A few Galactic globular clusters (including M22 and $\omega$ Centauri) may produce at least one event detectable by LISA. Even in more pessimistic scenarios, one could still expect $\sim$ 1 event overall in the Milky Way. We also estimate the number of Doppler shift events for IMBHs wandering in the Milky Way as a result of the disruption of their parent clusters. If there is at least one binary black hole orbiting around each wandering IMBH, LISA may detect up to a few tens of Doppler shift events from this elusive IMBH population. Under more pessimistic assumptions, LISA may still detect $\sim 1$ wandering IMBH that would hardly be observable otherwise., Comment: 19 pages, 10 figures, 4 tables. Considerable additions including 4 new figures and an appendix

Electromagnetic observations have provided strong evidence for the existence of massive black holes in the center of galaxies, but their origin is still poorly known. Different scenarios for the formation and evolution of massive black holes lead to different predictions for their properties and merger rates. LISA observations of coalescing massive black hole binaries could be used to reverse engineer the problem and shed light on these mechanisms. In this paper, we introduce a pipeline based on hierarchical Bayesian inference to infer the mixing fraction between different theoretical models by comparing them to LISA observations of massive black hole mergers. By testing this pipeline against simulated LISA data, we show that it allows us to accurately infer the properties of the massive black hole population as long as our theoretical models provide a reliable description of the Universe. We also show that measurement errors, including both instrumental noise and weak lensing errors, have little impact on the inference., Comment: Matches PRD version, minor changes

Solutions to the two-body problem in general relativity allow us to predict the mass, spin and recoil velocity of a black-hole merger remnant given the masses and spins of its binary progenitors. In this paper we address the inverse problem: given a binary black-hole merger, can we use the parameters measured by gravitational-wave interferometers to tell if the binary components are of hierarchical origin, i.e. if they are themselves remnants of previous mergers? If so, can we determine at least some of the properties of their parents? This inverse problem is in general overdetermined. We show that hierarchical mergers occupy a characteristic region in the plane composed of the effective spin parameters $\chi_{\rm eff}$ and $\chi_{\rm p}$, and therefore a measurement of these parameters can add weight to the hierarchical-merger interpretation of some gravitational-wave events, including GW190521. If one of the binary components has hierarchical origin and its spin magnitude is well measured, we derive exclusion regions on the properties of its parents: for example we infer that the parents of GW190412 (if hierarchical) must have had unequal masses and low spins. Our formalism is quite general, and it can be used to infer constraints on the astrophysical environment producing hierarchical mergers., Comment: Matches the published version, 12 pages, 9 figures, 1 table

With several dozen binary black hole events detected by LIGO/Virgo to date and many more expected in the next few years, gravitational-wave astronomy is shifting from individual-event analyses to population studies. Using the GWTC-2 catalog, we perform a hierarchical Bayesian analysis that for the first time combines several state-of-the-art astrophysical formation models with a population of primordial black holes (PBHs) and constrains the fraction of a putative subpopulation of PBHs in the data. We find that this fraction depends significantly on the set of assumed astrophysical models. While a primordial population is statistically favored against certain competitive astrophysical channels, such as globular clusters and nuclear stellar clusters, a dominant contribution from the stable-mass-transfer isolated formation channel drastically reduces the need for PBHs, except for explaining the rate of mass-gap events like GW190521. The tantalizing possibility that black holes formed after inflation are contributing to LIGO/Virgo observations could only be verified by further reducing uncertainties in astrophysical and primordial formation models, and it may ultimately be confirmed by third-generation interferometers., Comment: 13 pages, 7 figures. v3: matching published version

The recent release of the second Gravitational-Wave Transient Catalog (GWTC-2) has increased significantly the number of known GW events, enabling unprecedented constraints on formation models of compact binaries. One pressing question is to understand the fraction of binaries originating from different formation channels, such as isolated field formation versus dynamical formation in dense stellar clusters. In this paper, we combine the $\texttt{COSMIC}$ binary population synthesis suite and the $\texttt{CMC}$ code for globular cluster evolution to create a mixture model for black hole binary formation under both formation scenarios. For the first time, these code bodies are combined self-consistently, with $\texttt{CMC}$ itself employing $\texttt{COSMIC}$ to track stellar evolution. We then use a deep-learning enhanced hierarchical Bayesian analysis to constrain the mixture fraction $f$ between formation models, while simultaneously constraining the common envelope efficiency $\alpha$ assumed in $\texttt{COSMIC}$ and the initial cluster virial radius $r_v$ assumed in $\texttt{CMC}$. Under specific assumptions about other uncertain aspects of isolated binary and globular cluster evolution, we report the median and $90\%$ confidence interval of three physical parameters $(f,\alpha,r_v)=(0.20^{+0.32}_{-0.18},2.26^{+2.65}_{-1.84},2.71^{+0.83}_{-1.17})$. This simultaneous constraint agrees with observed properties of globular clusters in the Milky Way and is an important first step in the pathway toward learning astrophysics of compact binary formation from GW observations., Comment: 8 pages, 5 figures

Primordial black holes (PBHs) might be formed in the early Universe and could comprise at least a fraction of the dark matter. Using the recently released GWTC-2 dataset from the third observing run of the LIGO-Virgo Collaboration, we investigate whether current observations are compatible with the hypothesis that all black hole mergers detected so far are of primordial origin. We constrain PBH formation models within a hierarchical Bayesian inference framework based on deep learning techniques, finding best-fit values for distinctive features of these models, including the PBH initial mass function, the fraction of PBHs in dark matter, and the accretion efficiency. The presence of several spinning binaries in the GWTC-2 dataset favors a scenario in which PBHs accrete and spin up. Our results indicate that PBHs may comprise only a fraction smaller than $0.3 \%$ of the total dark matter, and that the predicted PBH abundance is still compatible with other constraints., Comment: 14 pages, 5 figures. v2: matching published version

Computing signal-to-noise ratios (SNRs) is one of the most common tasks in gravitational-wave data analysis. While a single SNR evaluation is generally fast, computing SNRs for an entire population of merger events could be time consuming. We compute SNRs for aligned-spin binary black-hole mergers as a function of the (detector-frame) total mass, mass ratio and spin magnitudes using selected waveform models and detector noise curves, then we interpolate the SNRs in this four-dimensional parameter space with a simple neural network (a multilayer perceptron). The trained network can evaluate $10^6$ SNRs on a 4-core CPU within a minute with a median fractional error below $10^{-3}$. This corresponds to average speed-ups by factors in the range $[120,\,7.5\times10^4]$, depending on the underlying waveform model. Our trained network (and source code) is publicly available at https://github.com/kazewong/NeuralSNR, and it can be easily adapted to similar multidimensional interpolation problems., Comment: 6 pages, 4 figures. Trained networks and source code available at https://github.com/kazewong/NeuralSNR

Gravitational waves from the merger of two neutron stars cannot be easily distinguished from those produced by a comparable-mass mixed binary in which one of the companions is a black hole. Low-mass black holes are interesting because they could form in the aftermath of the coalescence of two neutron stars, from the collapse of massive stars, from matter overdensities in the primordial Universe, or as the outcome of the interaction between neutron stars and dark matter. Gravitational waves carry the imprint of the internal composition of neutron stars via the so-called tidal deformability parameter, which depends on the stellar equation of state and is equal to zero for black holes. We present a new data analysis strategy powered by Bayesian inference and machine learning to identify mixed binaries, hence low-mass black holes, using the distribution of the tidal deformability parameter inferred from gravitational-wave observations., Comment: 13 pages, 6 figures - v2: matches the published version in Phys. Rev. D 102, 023025

Two of the dominant channels to produce the black-hole binary mergers observed by LIGO and Virgo are believed to be the isolated evolution of stellar binaries in the field and dynamical formation in star clusters. Their relative efficiency can be characterized by a "mixing fraction." Pair instabilities prevent stellar collapse from generating black holes more massive than about $45 M_\odot$. This "mass gap" only applies to the field formation scenario, and it can be filled by repeated mergers in clusters. A similar reasoning applies to the binary's effective spin. If black holes are born slowly rotating, the high-spin portion of the parameter space (the "spin gap") can only be populated by black hole binaries that were assembled dynamically. Using a semianalytical cluster model, we show that future gravitational-wave events in either the mass gap, the spin gap, or both can be leveraged to infer the mixing fraction between the field and cluster formation channels., Comment: 21 pages, 14 figures, Matches the published version

We combine hierarchical Bayesian modeling with a flow-based deep generative network, in order to demonstrate that one can efficiently constraint numerical gravitational wave (GW) population models at a previously intractable complexity. Existing techniques for comparing data to simulation,such as discrete model selection and Gaussian process regression, can only be applied efficiently to moderate-dimension data. This limits the number of observable (e.g. chirp mass, spins.) and hyper-parameters (e.g. common envelope efficiency) one can use in a population inference. In this study, we train a network to emulate a phenomenological model with 6 observables and 4 hyper-parameters, use it to infer the properties of a simulated catalogue and compare the results to the phenomenological model. We find that a 10-layer network can emulate the phenomenological model accurately and efficiently. Our machine enables simulation-based GW population inferences to take on data at a new complexity level., Comment: 11 pages, 3 figures

We report on advances to interpret current and future gravitational-wave events in light of astrophysical simulations. A machine-learning emulator is trained on numerical population-synthesis predictions and inserted into a Bayesian hierarchical framework. In this case study, a modest but state-of-the-art suite of simulations of isolated binary stars is interpolated across two event parameters and one population parameter. The validation process of our pipelines highlights how omitting some of the event parameters might cause errors in estimating selection effects, which propagates as systematics to the final population inference. Using LIGO/Virgo data from O1 and O2 we infer that black holes in binaries are most likely to receive natal kicks with one-dimensional velocity dispersion $\sigma$ = 105+44 km/s. Our results showcase potential applications of machine-learning tools in conjunction with population-synthesis simulations and gravitational-wave data., Comment: 6 pages, 3 figures

The LIGO/Virgo gravitational-wave (GW) interferometers have to-date detected ten merging black hole (BH) binaries, some with masses considerably larger than had been anticipated. Stellar-mass BH binaries at the high end of the observed mass range (with "chirp mass" ${\cal M} \gtrsim 25 M_{\odot}$) should be detectable by a space-based GW observatory years before those binaries become visible to ground-based GW detectors. This white paper discusses some of the synergies that result when the same binaries are observed by instruments in space and on the ground. We consider intermediate-mass black hole binaries (with total mass $M \sim 10^2 -10^4 M_{\odot}$) as well as stellar-mass black hole binaries. We illustrate how combining space-based and ground-based data sets can break degeneracies and thereby improve our understanding of the binary's physical parameters. While early work focused on how space-based observatories can forecast precisely when some mergers will be observed on the ground, the reverse is also important: ground-based detections will allow us to "dig deeper" into archived, space-based data to confidently identify black hole inspirals whose signal-to-noise ratios were originally sub-threshold, increasing the number of binaries observed in both bands by a factor of $\sim 4 - 7$., Comment: White Paper submitted to Astro2020 (2020 Decadal Survey on Astronomy and Astrophysics)

Unlike traditional electromagnetic measurements, gravitational-wave observations are not affected by crowding and extinction. For this reason, compact object binaries orbiting around a massive black hole can be used as probes of the inner environment of the black hole in regions inaccessible to traditional astronomical measurements. The orbit of the binary's barycenter around the massive black hole will cause a Doppler shift in the gravitational waveform which is in principle measurable by future space-based gravitational-wave interferometers, such as the Laser Interferometer Space Antenna (LISA). We investigate the conditions under which these Doppler shifts are observable by LISA. Our results imply that Doppler shift observations can be used to study the central region of globular clusters in the Milky Way, as well the central environment of extragalactic massive black holes., Comment: 7 pages, 3 figures, 1 table. Matches version in press in MNRAS

Joint gravitational-wave detections of stellar-mass black-hole binaries by ground- and space-based observatories will provide unprecedented opportunities for fundamental physics and astronomy. We present a semianalytic method to estimate multiband event rates by combining selection effects of ground-based interferometers (like LIGO/Virgo) and space missions (like LISA). We forecast the expected number of multiband detections first by using information from current LIGO/Virgo data, and then through population synthesis simulations of binary stars. We estimate that few to tens of LISA detections can be used to predict mergers detectable on the ground. Conversely, hundreds of events could potentially be extracted from the LISA data stream using prior information from ground detections. In general, the merger signal of binaries observable by LISA is strong enough to be unambiguously identified by both current and future ground-based detectors. Therefore third-generation detectors will not increase the number of multiband detections compared to LIGO/Virgo. We use population synthesis simulations of isolated binary stars to explore some of the stellar physics that could be constrained with multiband events, and we show that specific formation pathways might be overrepresented in multiband events compared to ground-only detections., Comment: 17 pages, 11 figures. Database and python code available at https://github.com/dgerosa/spops - Published in PRD

The Laser Interferometer Space Antenna (LISA) gravitational-wave (GW) observatory will be limited in its ability to detect mergers of binary black holes (BBHs) in the stellar-mass range. A future ground-based detector network, meanwhile, will achieve by the LISA launch date a sensitivity that ensures complete detection of all mergers within a volume $>\!\mathcal{O}(10)\,{\rm Gpc}^{3}$. We propose a method to use the information from the ground to revisit the LISA data in search for sub-threshold events. By discarding spurious triggers that do not overlap with the ground-based catalogue, we show that the signal-to-noise threshold $\rho_{\rm LISA}$ employed in LISA can be significantly lowered, greatly boosting the detection rate. The efficiency of this method depends predominantly on the rate of false-alarm increase when the threshold is lowered and on the uncertainty in the parameter estimation for the LISA events. As an example, we demonstrate that while all current LIGO BBH-merger detections would have evaded detection by LISA when employing a standard $\rho_{\rm LISA}=8$ threshold, this method will allow us to easily (possibly) detect an event similar to GW150914 (GW170814) in LISA. Overall, we estimate that the total rate of stellar-mass BBH mergers detected by LISA can be boosted by a factor $\sim\!4$ ($\gtrsim\!8$) under conservative (optimistic) assumptions. This will enable new tests using multi-band GW observations, significantly aided by the greatly increased lever arm in frequency., Comment: 5 pages, 2 figures

Cunha et al. (2018) recently reexamined the possibility of detecting gravitational waves from exoplanets, claiming that three ultra-short period systems would be observable by LISA. We revisit their analysis and conclude that the currently known exoplanetary systems are unlikely to be detectable, even assuming a LISA observation time $T_{\rm obs}=4$ yrs. Conclusive statements on the detectability of one of these systems, GP Com b, will require better knowledge of the system's properties, as well as more careful modeling of both LISA's response and the galactic confusion noise. Still, the possibility of exoplanet detection with LISA is interesting enough to warrant further study, as gravitational waves could yield dynamical properties that are difficult to constrain with electromagnetic observations., Comment: 4 pages, 1 figure, 2 tables, revised in response to referee's comments and accepted in MNRAS

Light bosons, proposed as a possible solution to various problems in fundamental physics and cosmology, include a broad class of candidates for beyond the Standard Model physics, such as dilatons and moduli, wave dark matter and axion-like particles. If light bosons exist in nature, they will spontaneously form "clouds" by extracting rotational energy from rotating massive black holes through superradiance, a classical wave amplification process that has been studied for decades. The superradiant growth of the cloud sets the geometry of the final black hole, and the black hole geometry determines the shape of the cloud. Hence, both the black hole geometry and the cloud encode information about the light boson. For this reason, measurements of the gravitational field of the black hole/cloud system (as encoded in gravitational waves) are over-determined. We show that a single gravitational wave measurement can be used to verify the existence of light bosons by model selection, rule out alternative explanations for the signal, and measure the boson mass. Such measurements can be done generically for bosons in the mass range $[10^{-16.5},10^{-14}]$ eV using LISA observations of extreme mass-ratio inspirals.

We show how LIGO is expected to detect coalescing binary black holes at $z>1$, that are lensed by the intervening galaxy population. Gravitational magnification, $\mu$, strengthens gravitational wave signals by $\sqrt{\mu}$, without altering their frequencies, which if unrecognised leads to an underestimate of the event redshift and hence an overestimate of the binary mass. High magnifications can be reached for coalescing binaries because the region of intense gravitational wave emission during coalescence is so small ($\sim$100km), permitting very close projections between lensing caustics and gravitational-wave events. Our simulations incorporate accurate waveforms convolved with the LIGO power spectral density. Importantly, we include the detection dependence on sky position and orbital orientation, which for the LIGO configuration translates into a wide spread in observed redshifts and chirp masses. Currently we estimate a detectable rate of lensed events \rateEarly{}, that rises to \rateDesign{}, at LIGO's design sensitivity limit, depending on the high redshift rate of black hole coalescence., Comment: 5 pages, 4 figures