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Abstract:

Many of the species in decline around the world are subject to different environmental stressors across their range, so replicated large-scale monitoring programmes, are necessary to disentangle the relative impacts of these threats. At the same time as funding for long-term monitoring is being cut, studies are increasingly being criticised for lacking statistical power. For those taxa or environments where a single vantage point can observe individuals or ecological processes, time-lapse cameras can provide a cost-effective way of collecting time series data replicated at large spatial scales that would otherwise be impossible. However, networks of time-lapse cameras needed to cover the range of species or processes create a problem in that the scale of data collection easily exceeds our ability to process the raw imagery manually. Citizen science and machine learning provide solutions to scaling up data extraction (such as locating all animals in an image). Crucially, citizen science, machine learning-derived classifiers, and the intersection between them, are key to understanding how to establish monitoring systems that are sensitive to – and sufficiently powerful to detect –changes in the study system. Citizen science works relatively ‘out of the box’, and we regard it as a first step for many systems until machine learning algorithms are sufficiently trained to automate the process. Using Penguin Watch (www.penguinwatch.org) data as a case study, we discuss a complete workflow from images to parameter estimation and interpretation: the use of citizen science and computer vision for image processing, and parameter estimation and individual recognition for investigating biological questions. We discuss which techniques are easily generalizable to a range of questions, and where more work is needed to supplement ‘out of the box’ tools. We conclude with a horizon scan of the advances in camera technology, such as on-board computer vision and decision making.

Abstract:

Utilizing optical and near-infrared broad-band photometry covering >5 deg2 in two of the most well-studied extragalactic legacy fields (COSMOS and XMM-LSS), we measure the galaxy stellar mass function (GSMF) between 0.1 < z < 2.0. We explore in detail the effect of two source extraction methods (SExtractor and ProFound) in addition to the inclusion/exclusion of Spitzer IRAC 3.6 and 4.5 μm photometry when measuring the GSMF. We find that including IRAC data reduces the number of massive (log10(M/M⊙) > 11.25) galaxies found due to improved photometric redshift accuracy, but has little effect on the more numerous lower-mass galaxies. We fit the resultant GSMFs with double Schechter functions down to log10(M/M⊙) = 7.75 (9.75) at z = 0.1 (2.0) and find that the choice of source extraction software has no significant effect on the derived best-fitting parameters. However, the choice of methodology used to correct for the Eddington bias has a larger impact on the high-mass end of the GSMF, which can partly explain the spread in derived M* values from previous studies. Using an empirical correction to model the intrinsic GSMF, we find evidence for an evolving characteristic stellar mass with δlog10(M*/M⊙)/δz = −0.16±0.05(−0.11±0.05)⁠, when using SExtractor (ProFound). We argue that with widely quenched star formation rates in massive galaxies at low redshift (z < 0.5), additional growth via mergers is required in order to sustain such an evolution to a higher characteristic mass.

Abstract:

We examine the distribution of radio emission from ∼42 000 quasars from the Sloan Digital Sky Survey, as measured in the LOFAR Two-metre Sky Survey (LoTSS). We present a model of the radio luminosity distribution of the quasars that assumes that every quasar displays a superposition of two sources of radio emission: active galactic nuclei (jets) and star formation. Our two-component model provides an excellent match to the observed radio flux density distributions across a wide range of redshifts and quasar optical luminosities; this suggests that the jet-launching mechanism operates in all quasars but with different powering efficiency. The wide distribution of jet powers allows for a smooth transition between the ‘radio-quiet’ and ‘radio-loud’ quasar regimes, without need for any explicit bimodality. The best-fitting model parameters indicate that the star formation rate of quasar host galaxies correlates strongly with quasar luminosity and also increases with redshift at least out to z ∼ 2. For a model where star formation rate scales as Lαbol(1+z)β⁠, we find α = 0.47 ± 0.01 and β = 1.61 ± 0.05, in agreement with far-infrared studies. Quasars contribute ≈0.15 per cent of the cosmic star formation rate density at z = 0.5, rising to 0.4 per cent by z ∼ 2. The typical radio jet power is seen to increase with both increasing optical luminosity and black hole mass independently, but does not vary with redshift, suggesting intrinsic properties govern the production of the radio jets. We discuss the implications of these results for the triggering of quasar activity and the launching of jets.