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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 with masses between ∼100–10⁴M_⊙. In our work, we propose a new method for examining lens populations through lensing statistics of gravitational waves, here focusing on inferring the number density of intermediate-mass black holes through hierarchical Bayesian inference. Simulating ∼200 lensed gravitational-wave signals, we find that existing gravitational-wave observatories at their design sensitivity could either constrain the number density of 10⁶Mpc⁻³within a factor of 10, or place an upper bound of ≲10⁴Mpc⁻³if the true number density is 10³Mpc⁻³. More broadly, our method leaves room for incorporation of additional lens populations, providing a general framework for probing the population properties of lenses in the universe.

 

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 modelling of radial velocities and astrometry constrains the companion mass of $M_2 = 9.62\pm 0.18\, \mathrm{M}_{\odot }$. The spectroscopic orbit alone sets a minimum companion mass of $M_2\gt 5\, \mathrm{M}_{\odot }$; if the companion were a $5\, \mathrm{M}_{\odot }$ 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, $M=0.93\, \mathrm{M}_{\odot }$), 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, Porb = 185.6 d, is longer than that of any known stellar-mass black hole binary. The system’s modest eccentricity (e = 0.45), high metallicity, and thin-disc Galactic orbit suggest that it was born in the Milky Way disc 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.

 

Abstract 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 4233 cosmological simulations, 2049 N -body simulations, and 2184 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 catalogs, power spectra, bispectra, Ly α spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N -body simulations that have been combined with the Santa Cruz semianalytic 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 https://camels.readthedocs.io .