Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

We analyse the demographics of black holes (BHs) in the large-volume cosmological hydrodynamical simulation Horizon-AGN. This simulation statistically models how much gas is accreted on to BHs, traces the energy deposited into their environment and, consequently, the back-reaction of the ambient medium on BH growth. The synthetic BHs reproduce a variety of observational constraints such as the redshift evolution of the BH mass density and the mass function. Strong self-regulation via AGN feedback, weak supernova feedback, and unresolved internal processes result in a tight BH–galaxy mass correlation. Starting at z ∼ 2, tidal stripping creates a small population of BHs over-massive with respect to the halo. The fraction of galaxies hosting a central BH or an AGN increases with stellar mass. The AGN fraction agrees better with multi-wavelength studies, than single-wavelength ones, unless obscuration is taken into account. The most massive haloes present BH multiplicity, with additional BHs gained by ongoing or past mergers. In some cases, both a central and an off-centre AGN shine concurrently, producing a dual AGN. This dual AGN population dwindles with decreasing redshift, as found in observations. Specific accretion rate and Eddington ratio distributions are in good agreement with observational estimates. The BH population is dominated in turn by fast, slow, and very slow accretors, with transitions occurring at z = 3 and z = 2, respectively.

Abstract:

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org, more than 28,000 volunteers classified 1.51 million images taken in a large-scale camera-trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert-verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post-hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large-scale monitoring of African wildlife.