Galaxy formation and evolution

Abstract:

Distinguishing the photon output of an accreting supermassive black hole binary system from that of a single supermassive black hole accreting at the same rate is intrinsically difficult because the majority of the light emerges from near the innermost stable orbits of the black holes. However, there are two possible signals that can distinctively mark binaries, both arising from the gap formed in circumbinary accretion flows inside approximately twice the binary separation. One of these is a "notch" cut into the thermal spectra of these systems in the IR/optical/UV, the other a periodically varying excess hard X-ray luminosity whose period is of order the binary orbital period. Using data from detailed galaxy evolution simulations, we estimate the distribution function in mass, mass ratio, and accretion rate for accreting supermassive binary black holes (SMBBHs) as a function of redshift and then transform this distribution function into predicted source counts for these two potential signals. At flux levels >~10−13 erg cm−2 s−1, there may be ~O(102) such systems in the sky, mostly in the redshift range 0.5 <~ z <~ 1. Roughly 10% should have periods short enough (<~5 yr) to detect the X-ray modulation; this is also the period range accessible to Pulsar Timing Array observations.

Abstract:

Observations find a median star formation efficiency per free-fall time in Milky Way Giant Molecular Clouds (GMCs) on the order of $\epsilon_{\rm ff}\sim 1\%$ with dispersions of $\sim0.5\,{\rm dex}$. The origin of this scatter in $\epsilon_{\rm ff}$ is still debated and difficult to reproduce with analytical models. We track the formation, evolution and destruction of GMCs in a hydrodynamical simulation of a Milky Way-like galaxy and by deriving cloud properties in an observationally motivated way, measure the distribution of star formation efficiencies which are in excellent agreement with observations. We find no significant link between $\epsilon_{\rm ff}$ and any measured global property of GMCs (e.g. gas mass, velocity dispersion). Instead, a wide range of efficiencies exist in the entire parameter space. From the cloud evolutionary tracks, we find that each cloud follow a \emph{unique} evolutionary path which gives rise to wide diversity in all properties. We argue that it is this diversity in cloud properties, above all else, that results in the dispersion of $\epsilon_{\rm ff}$.

Abstract:

Reverberation mapping (RM) is a powerful approach for determining the nature of the broad-line region (BLR) in active galactic nuclei. However, inferring physical BLR properties from an observed spectroscopic time series is a difficult inverse problem. Here, we present a blind test of two widely used RM methods: MEMECHO (developed by Horne) and CARAMEL (developed by Pancoast and collaborators). The test data are simulated spectroscopic time series that track the Hα emission line response to an empirical continuum light curve. The underlying BLR model is a rotating, biconical accretion disc wind, and the synthetic spectra are generated via self-consistent ionization and radiative transfer simulations. We generate two mock data sets, representing Seyfert galaxies and QSOs. The Seyfert model produces a largely negative response, which neither method can recover. However, both fail ‘gracefully', neither generating spurious results. For the QSO model both CARAMEL and expert interpretation of MEMECHOś output both capture the broadly annular, rotation-dominated nature of the line-forming region, though MEMECHO analysis overestimates its size by 50 per cent, but CARAMEL is unable to distinguish between additional inflow and outflow components. Despite fitting individual spectra well, the CARAMEL velocity-delay maps and RMS line profiles are strongly inconsistent with the input data. Finally, since the Hα line-forming region is rotation dominated, neither method recovers the disc wind nature of the underlying BLR model. Thus considerable care is required when interpreting the results of RM analyses in terms of physical models.