In this paper we describe an unmanned aerial system equipped with a thermalinfrared camera and software pipeline that we have developed to monitor animal populations for conservation purposes. Taking a multi-disciplinary approach to tackle this problem, we use freely available astronomical source detection software and the associated expertise of astronomers, to efficiently and reliably detect humans and animals in aerial thermal-infrared footage. Combining this astronomical detection software with existing machine learning algorithms into a single, automated, end-to-end pipeline, we test the software using aerial video footage taken in a controlled, field-like environment. We demonstrate that the pipeline works reliably and describe how it can be used to estimate the completeness of different observational datasets to objects of a given type as a function of height, observing conditions etc. -a crucial step in converting video footage to scientifically useful information such as the spatial distribution and density of different animal species. Finally, having demonstrated the potential utility of the system, we describe the steps we are taking to adapt the system for work in the field, in particular systematic monitoring of endangered species at National Parks around the world.
A large body of work based on collisionless cosmological N-body simulations going back over two decades has advanced the idea that collapsed dark matter (DM) haloes have simple and approximately universal forms for their mass density and pseudo-phase-space density (PPSD) distributions. However, a general consensus on the physical origin of these results has not yet been reached. In the present study, we explore to what extent the apparent universality of these forms holds when we vary the initial conditions (i.e. the primordial power spectrum of density fluctuations) away from the standard CMB-normalized case, but still within the context of lambda cold dark matter with a fixed expansion history. Using simulations that vary the initial amplitude and shape, we show that the structure of DM haloes retains a clear memory of the initial conditions. Specifically, increasing (lowering) the amplitude of fluctuations increases (decreases) the concentration of haloes and, if pushed far enough, the density profiles deviate strongly from the NFW form that is a good approximation for the CMB-normalized case. Although, an Einasto form works well. Rather than being universal, the slope of the PPSD (or pseudo-entropy) profile steepens (flattens) with increasing (decreasing) power spectrum amplitude and can exhibit a strong halo mass dependence. Our results therefore indicate that the previously identified universality of the structure of DM haloes is mostly a consequence of adopting a narrow range of (CMB-normalized) initial conditions for the simulations. Our new suite provides a useful test-bench against which physical models for the origin of halo structure can be validated.
In the context of forthcoming galaxy surveys, to ensure unbiased constraints on cosmology and gravity when using non-linear structure information, percent-level accuracy is required when modelling the power spectrum. This calls for frameworks that can accurately capture the relevant physical effects, while allowing for deviations from ΛCDM. Massive neutrino and baryonic physics are two of the most relevant such effects. We present an integration of the halo model reaction frameworks for massive neutrinos and beyond-ΛCDM cosmologies. The integrated halo model reaction, combined with a pseudo power spectrum modelled by HMCode2020 is then compared against N-body simulations that include both massive neutrinos and an f(R) modification to gravity. We find that the framework is 4 per cent accurate down to at least k ≈ 3 h Mpc−1 for a modification to gravity of |fR0| ≤ 10−5 and for the total neutrino mass Mν ≡ ∑mν ≤ 0.15 eV. We also find that the framework is 4 per cent consistent with EuclidEmulator2 as well as the Bacco emulator for most of the considered νwCDM cosmologies down to at least k ≈ 3 h Mpc−1. Finally, we compare against hydrodynamical simulations employing HMCode2020’s baryonic feedback modelling on top of the halo model reaction. For νΛCDM cosmologies we find 2 per cent accuracy for Mν ≤ 0.48 eV down to at least k ≈ 5h Mpc−1. Similar accuracy is found when comparing to νwCDM hydrodynamical simulations with Mν = 0.06 eV. This offers the first non-linear, theoretically general means of accurately including massive neutrinos for beyond-ΛCDM cosmologies, and further suggests that baryonic, massive neutrino and dark energy physics can be reliably modelled independently.
In this work we consider the impact of spatially-uniform but time-varying dark energy (or ‘dynamical dark energy’, DDE) on large-scale structure in a spatially flat universe, using large cosmological hydrodynamical simulations that form part of the BAHAMAS project. As DDE changes the expansion history of the universe, it impacts the growth of structure. We explore variations in DDE that are constrained to be consistent with the cosmic microwave background. We find that DDE can affect the clustering of matter and haloes at the $\sim 10\%$ level (suppressing it for so-called ‘freezing’ models, while enhancing it for ‘thawing’ models), which should be distinguishable with upcoming large-scale structure surveys. DDE cosmologies can also enhance or suppress the halo mass function (with respect to ΛCDM) over a wide range of halo masses. The internal properties of haloes are minimally affected by changes in DDE, however. Finally, we show that the impact of baryons and associated feedback processes is largely independent of the change in cosmology and that these processes can be modelled separately to typically better than a few percent accuracy.
Recent analyses of the cosmic microwave background (CMB) and the Lyman-α forest indicate a mild preference for a deviation from a power law primordial matter power spectrum (a so-called negative 'running'). We use an extension to the BAHAMAS suite of cosmological hydrodynamic simulations to explore the effects that a running scalar spectral index has on large-scale structure (LSS), using P lanck CMB constraints to initialize the simulations. We focus on 5 key statistics: i) the non-linear matter power spectrum ii) the halo mass function; iii) the halo two-point auto correlation function; iv) total mass halo density profiles; and v) the halo concentration-mass relation. In terms of the matter power spectrum, we find that a running scalar spectral index affects all k−scales examined in this study, with a negative (positive) running leading to an amplification (suppression) of power. These effects should be easily detectable with upcoming surveys such as LSST and Euclid. In the mass range sampled, a positive running leads to an increase in the mass of galaxy groups and clusters, with the favoured negative running leading to a decrease in mass of lower-mass (M 10 13 M ) halos, but an increase for the most massive (M 10 13 M ) halos. Changes in the mass are generally confined to 5-10% which, while not insignificant, cannot by itself reconcile the claimed tension between the primary CMB and cluster number counts. We find that running does not significantly affect the shapes of density profiles of matched halos, changing only their amplitude. Finally, we demonstrate that the observed effects on LSS due to a running scalar spectral index are separable from those of baryonic effects to typically a few percent precision.
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