Abstract
The discovery of two neutron star–black hole coalescences by LIGO and Virgo brings the total number of likely neutron stars observed in gravitational waves to six. We perform the first ...inference of the mass distribution of this extragalactic population of neutron stars. In contrast to the bimodal Galactic population detected primarily as radio pulsars, the masses of neutron stars in gravitational-wave binaries are thus far consistent with a uniform distribution, with a greater prevalence of high-mass neutron stars. The maximum mass in the gravitational-wave population agrees with that inferred from the neutron stars in our Galaxy and with expectations from dense matter.
ABSTRACT
Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology ...(e.g. as a prior for photometric-redshift measurements and as contextual data for transient light-curve classifications). While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming ‘Big-Data’ surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such data sets intractable for visual inspection (even via massively distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine learning, since it may be impractical to repeatedly produce training sets on short time-scales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of ‘morphological clusters’, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z < 1 (e.g. stellar-mass functions, rest-frame colours, and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.
We present Low-Frequency Array (LOFAR) High-Band Array observations of the Herschel-ATLAS North Galactic Pole survey area. The survey we have carried out, consisting of four pointings covering around ...142 deg2 of sky in the frequency range 126–173 MHz, does not provide uniform noise coverage but otherwise is representative of the quality of data to be expected in the planned LOFAR wide-area surveys, and has been reduced using recently developed ‘facet calibration’ methods at a resolution approaching the full resolution of the data sets (∼10 × 6 arcsec) and an rms off-source noise that ranges from 100 μJy beam−1 in the centre of the best fields to around 2 mJy beam−1 at the furthest extent of our imaging. We describe the imaging, cataloguing and source identification processes, and present some initial science results based on a 5σ source catalogue. These include (i) an initial look at the radio/far-infrared correlation at 150 MHz, showing that many Herschel sources are not yet detected by LOFAR; (ii) number counts at 150 MHz, including, for the first time, observational constraints on the numbers of star-forming galaxies; (iii) the 150-MHz luminosity functions for active and star-forming galaxies, which agree well with determinations at higher frequencies at low redshift, and show strong redshift evolution of the star-forming population; and (iv) some discussion of the implications of our observations for studies of radio galaxy life cycles.
Organisms continuously release DNA into their environments via shed cells, excreta, gametes and decaying material. Analysis of this ‘environmental DNA’ (eDNA) is revolutionizing biodiversity ...monitoring. eDNA outperforms many established survey methods for targeted detection of single species, but few studies have investigated how well eDNA reflects whole communities of organisms in natural environments. We investigated whether eDNA can recover accurate qualitative and quantitative information about fish communities in large lakes, by comparison to the most comprehensive long‐term gill‐net data set available in the UK. Seventy‐eight 2L water samples were collected along depth profile transects, gill‐net sites and from the shoreline in three large, deep lakes (Windermere, Bassenthwaite Lake and Derwent Water) in the English Lake District. Water samples were assayed by eDNA metabarcoding of the mitochondrial 12S and cytochrome b regions. Fourteen of the 16 species historically recorded in Windermere were detected using eDNA, compared to four species in the most recent gill‐net survey, demonstrating eDNA is extremely sensitive for detecting species. A key question for biodiversity monitoring is whether eDNA can accurately estimate abundance. To test this, we used the number of sequence reads per species and the proportion of sampling sites in which a species was detected with eDNA (i.e. site occupancy) as proxies for abundance. eDNA abundance data consistently correlated with rank abundance estimates from established surveys. These results demonstrate that eDNA metabarcoding can describe fish communities in large lakes, both qualitatively and quantitatively, and has great potential as a complementary tool to established monitoring methods.
We used a mechanistic physical model to examine the effect of variability in dissolved organic carbon (DOC) concentrations on the physical properties of small temperate lakes. The model was validated ...on eight small (6 × 10−4 to 3.8 × 10−2 km²) lakes in Wisconsin and Michigan, with a standard error , 1°C for seven of eight lakes. Attenuation of photosynthetically active radiation (400-700 nm) in these lakes was regulated by DOC concentrations and was important in the vertical structuring of water temperatures. Heat exchange below the surface mixed layer was near the molecular rate, increasing the importance of water clarity as a control on the heat content of deeper waters. To understand the thermal effects of changing DOC concentrations, we applied scenarios of a 50% increase and 50% decrease in DOC concentrations for one lake (Trout Bog) and found water temperatures to vary in response, with the seasonally averaged temperatures in the increased DOC scenario being > 2°C colder than the reduced scenario. We found a nonlinear relationship between DOC and temperature, with clearer (lower DOC) simulations being more sensitive to climate variability, suggesting that DOC may act as a buffer against a warming climate. Our model showed that DOC also influenced epilimnetic depths, as nocturnal mixing (related to the vertical partitioning of heat) was more important than wind-driven mixing. Small lakes are globally important regulators of biogeochemical cycles and are structurally different from larger lakes. Important feedbacks to physical processes must be accounted for when understanding the effects of changing DOC and climate on small lakes.
The detection of GW170817, the first neutron star-neutron star merger observed by Advanced LIGO and Virgo, and its following analyses represent the first contributions of gravitational wave data to ...understanding dense matter. Parameterizing the high density section of the equation of state of both neutron stars through spectral decomposition, and imposing a lower limit on the maximum mass value, led to an estimate of the stars' radii of km and km (Abbott et al 2018 Phys. Rev. Lett. 121 161101). These values do not, however, take into account any uncertainty owed to the choice of the crust low-density equation of state, which was fixed to reproduce the SLy equation of state model (Douchin and Haensel 2001 Astron. Astrophys. 380 151). We here re-analyze GW170817 data and establish that different crust models do not strongly impact the mass or tidal deformability of a neutron star-it is impossible to distinguish between low-density models with gravitational wave analysis. However, the crust does have an effect on inferred radius. We predict the systematic error due to this effect using neutron star structure equations, and compare the prediction to results from full parameter estimation runs. For GW170817, this systematic error affects the radius estimate by 0.3 km, approximately of the neutron stars' radii.
The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with ...state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process‐based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process‐based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training data set. The PGDL model performance (as measured by root‐mean‐square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 °C during the test period (DL: 1.78 °C, PB: 2.03 °C; in a small number of lakes PB or DL models were more accurate). This case‐study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.
Key Points
Process‐Guided Deep Learning (PGDL) models integrate advanced empirical techniques with process knowledge
We used PGDL to accurately predict lake water temperatures for various conditions
PGDL performance improved significantly when pretraining data included diverse conditions generated by an existing process‐based model
Lake ecosystems and the services that they provide to people are profoundly influenced by dissolved organic matter derived from terrestrial plant tissues. These terrestrial dissolved organic matter ...(tDOM) inputs to lakes have changed substantially in recent decades, and will likely continue to change. In this paper, we first briefly review the substantial literature describing tDOM effects on lakes and ongoing changes in tDOM inputs. We then identify and provide examples of four major challenges which limit predictions about the implications of tDOM change for lakes, as follows: First, it is currently difficult to forecast future tDOM inputs for particular lakes or lake regions. Second, tDOM influences ecosystems via complex, interacting, physical-chemical-biological effects and our holistic understanding of those effects is still rudimentary. Third, non-linearities and thresholds in relationships between tDOM inputs and ecosystem processes have not been well described. Fourth, much understanding of tDOM effects is built on comparative studies across space that may not capture likely responses through time. We conclude by identifying research approaches that may be important for overcoming those challenges in order to provide policy- and management-relevant predictions about the implications of changing tDOM inputs for lakes.
Lotic ecosystems such as rivers and streams are unique in that they represent a continuum of both space and time during the transition from headwaters to the river mouth. As microbes have very ...different controls over their ecology, distribution and dispersion compared with macrobiota, we wished to explore biogeographical patterns within a river catchment and uncover the major drivers structuring bacterioplankton communities. Water samples collected across the River Thames Basin, UK, covering the transition from headwater tributaries to the lower reaches of the main river channel were characterised using 16S rRNA gene pyrosequencing. This approach revealed an ecological succession in the bacterial community composition along the river continuum, moving from a community dominated by Bacteroidetes in the headwaters to Actinobacteria-dominated downstream. Location of the sampling point in the river network (measured as the cumulative water channel distance upstream) was found to be the most predictive spatial feature; inferring that ecological processes pertaining to temporal community succession are of prime importance in driving the assemblages of riverine bacterioplankton communities. A decrease in bacterial activity rates and an increase in the abundance of low nucleic acid bacteria relative to high nucleic acid bacteria were found to correspond with these downstream changes in community structure, suggesting corresponding functional changes. Our findings show that bacterial communities across the Thames basin exhibit an ecological succession along the river continuum, and that this is primarily driven by water residence time rather than the physico-chemical status of the river.
Environmental DNA (eDNA) is a promising tool for rapid and noninvasive biodiversity monitoring. eDNA density is low in environmental samples, and a capture method, such as filtration, is often ...required to concentrate eDNA for downstream analyses. In this study, six treatments, with differing filter types and pore sizes for eDNA capture, were compared for their efficiency and accuracy to assess fish community structure with known fish abundance and biomass via eDNA metabarcoding. Our results showed that different filters (with the exception of 20‐μm large‐pore filters) were broadly consistent in their DNA capture ability. The 0.45‐μm filters performed the best in terms of total DNA yield, probability of species detection, repeatability within pond and consistency between ponds. However performance of 0.45‐μm filters was only marginally better than for 0.8‐μm filters, while filtration time was significantly longer. Given this trade‐off, the 0.8‐μm filter is the optimal pore size of membrane filter for turbid, eutrophic and high fish density ponds analysed here. The 0.45‐μm Sterivex enclosed filters performed reasonably well and are suitable in situations where on‐site filtration is required. Finally, prefilters are applied only if absolutely essential for reducing the filtration time or increasing the throughput volume of the capture filters. In summary, we found encouraging similarity in the results obtained from different filtration methods, but the optimal pore size of filter or filter type might strongly depend on the water type under study.
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