Julien Devriendt

Abstract:

Using the HORIZON-AGN hydrodynamical simulation and self-organizing maps (SOMs), we show how to compress the complex, high-dimensional data structure of a simulation into a 2D grid, which greatly facilitates the analysis of how galaxy observables are connected to intrinsic properties. We first verify the tight correlation between the observed 0.3–5 μm broad-band colours of HORIZON-AGN galaxies and their high-resolution spectra. The correlation is found to extend to physical properties such as redshift, stellar mass, and star formation rate (SFR). This direct mapping from colour to physical parameter space still works after including photometric uncertainties that mimic the COSMOS survey. We then label the SOM grid with a simulated calibration sample to estimate redshift and SFR for COSMOS-like galaxies up to z ∼ 3. In comparison to state-of-the-art techniques based on synthetic templates, our method is comparable in performance but less biased at estimating redshifts, and significantly better at predicting SFRs. In particular, our ‘data-driven’ approach, in contrast to model libraries, intrinsically allows for the complexity of galaxy formation and can handle sample biases. We advocate that observations to calibrate this method should be one of the goals of next-generation galaxy surveys.

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 $\epsilon_{\rm ff}$.