Abstract Tides pose significant operational and engineering challenges and are critical drivers of many natural processes. Accurate tidal predictions are important for modeling these phenomena. Conventionally, tidal prediction is carried out using harmonic analysis, the accuracy of which degrades when non-stationary and nontidal forcing are present. While Munk and Cartwright’s response method avoids the assumptions that give rise to this degradation, the difficulty of defining realistic interactions between inputs has inhibited automated applications. Here, we develop a non-parametric framework for tidal analysis and prediction of sea levels under compound forcing. The approach embeds a class of neural networks capable of representing any arbitrary Volterra series—the mathematical basis of the response method—within the classic method. The new ML Response Framework overcomes the automation challenges imposed by the original method and can directly infer high-order nonlinear interactions. This makes the inclusion of meteorological and other non-tidal forcing straightforward. Furthermore, we show that by accounting for this nonstationarity explicitly, a better astronomical tidal estimate is obtained. A method is devised to obtain physical insights from the learned model, illustrating how it can be used to study the interaction and modulation of astronomical tides by external forcing. By taking a nonparametric approach, our framework makes the study of phenomena that heretofore could not be accounted for straightforward. We provide several case studies, including the analysis and prediction of tide-surge interaction, riverine tides, and nuisance flooding. These applications, and more, can be replicated using only three lines of code with the open-source Python package (RTide).
ABSTRACT Stellar active regions like spots and faculae can distort the shapes of spectral lines, inducing variations in the radial velocities that are often orders of magnitude larger than the signals from Earth-like planets. Efforts to mitigate these activity signals have hitherto focused on either the time or the velocity (wavelength) domains. We present a physics-driven Gaussian process (GP) framework to model activity signals directly in time series of line profiles or cross-correlation functions (CCFs). Unlike existing methods that correct activity signals in line profile time series, our approach exploits the time correlation between velocity (wavelength) bins in the line profile variations, and is based on a simplified but physically motivated model for the origin of these variations. When tested on both synthetic and real data sets with signal-to-noise ratios down to ∼100, our method was able to separate the planetary signal from the activity signal, even when their periods were identical. We also conducted injection/recovery tests using two years of realistically sampled HARPS-N solar data, demonstrating the ability of the method to accurately recover a signal induced by a 1.5-Earth mass planet with a semi-amplitude of 0.3 m s−1 and a period of 33 d during high solar activity.
International Atomic Energy Agency 1989. Metallogenesis of Uranium Deposits. Proceedings of a Technical Committee Meeting, Vienna, 9–12 March 1987. ix + 492 pp. Vienna: International Atomic Energy Agency. Price Austrian schillings 1200.00 (paperback). ISBN 92 0 141289 4; ISSN 0074-1876. - Volume 128 Issue 2
The recently approved NASA K2 mission has the potential to multiply by an order of magnitude the number of short-period transiting planets found by Kepler around bright and low-mass stars, and to revolutionise our understanding of stellar variability in open clusters. However, the data processing is made more challenging by the reduced pointing accuracy of the satellite, which has only two functioning reaction wheels. We present a new method to extract precise light curves from K2 data, combining list-driven, soft-edged aperture photometry with a star-by-star correction of systematic effects associated with the drift in the roll-angle of the satellite about its boresight. The systematics are modelled simultaneously with the stars' intrinsic variability using a semi-parametric Gaussian process model. We test this method on a week of data collected during an engineering test in January 2014, perform checks to verify that our method does not alter intrinsic variability signals, and compute the precision as a function of magnitude on long-cadence (30-min) and planetary transit (2.5-hour) timescales. In both cases, we reach photometric precisions close to the precision reached during the nominal Kepler mission for stars fainter than 12th magnitude, and between 40 and 80 parts per million for brighter stars. These results confirm the bright prospects for planet detection and characterisation, asteroseismology and stellar variability studies with K2. Finally, we perform a basic transit search on the light curves, detecting 2 bona fide transit-like events, 7 detached eclipsing binaries and 13 classical variables.
Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshift estimation is presented. Synthetic data set simulating the Euclid survey and real data from SDSS DR12 are used to train and test the proposed models. We show that approaches which include careful data preparation and model design offer a significant improvement in comparison with several competing machine learning algorithms. Standard implementations of most regression algorithms use the minimization of the sum of squared errors as the objective function. For redshift inference, this induces a bias in the posterior mean of the output distribution, which can be problematic. In this paper, we directly minimize the target metric Δz = (zs − zp)/(1 + zs) and address the bias problem via a distribution-based weighting scheme, incorporated as part of the optimization objective. The results are compared with other machine learning algorithms in the field such as artificial neural networks (ANN), Gaussian processes (GPs) and sparse GPs. The proposed framework reaches a mean absolute Δz = 0.0026(1 + zs), over the redshift range of 0 ≤ zs ≤ 2 on the simulated data, and Δz = 0.0178(1 + zs) over the entire redshift range on the SDSS DR12 survey, outperforming the standard ANNz used in the literature. We also investigate how the relative size of the training sample affects the photometric redshift accuracy. We find that a training sample of >30 per cent of total sample size, provides little additional constraint on the photometric redshifts, and note that our GP formalism strongly outperforms ANNz in the sparse data regime for the simulated data set.
How do birds orient over familiar terrain? In the best studied avian species, the homing pigeon ( Columba livia ), two apparently independent primary mechanisms are currently debated: either memorized visual landmarks provide homeward guidance directly, or birds rely on a compass to home from familiar locations. Using miniature Global Positioning System tracking technology and clock-shift procedures, we set sun-compass and landmark information in conflict, showing that experienced birds can accurately complete their memorized routes by using landmarks alone. Nevertheless, we also find that route following is often consistently offset in the expected compass direction, faithfully reproducing the shape of the track, but in parallel. Thus, we demonstrate conditions under which compass orientation and landmark guidance must be combined into a system of simultaneous or oscillating dual control.
The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up and help beat coronavirus' digital survey alongside demographic, symptom and self-reported respiratory condition data. Digital survey submissions were linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,565 of 72,999 participants and 24,105 of 25,706 positive cases. Respiratory symptoms were reported by 45.6% of participants. This dataset has additional potential uses for bioacoustics research, with 11.3% participants self-reporting asthma, and 27.2% with linked influenza PCR test results.
Although the biochemical correlates of freeze tolerance in insects are becoming well-known, the process of ice formation in vivo is subject to speculation. We used synchrotron x-rays to directly visualise real-time ice formation at 3.3 Hz in intact insects. We observed freezing in diapausing 3(rd) instar larvae of Chymomyza amoena (Diptera: Drosophilidae), which survive freezing if it occurs above -14 degrees C, and non-diapausing 3(rd) instar larvae of C. amoena and Drosophila melanogaster (Diptera: Drosophilidae), neither of which survive freezing. Freezing was readily observed in all larvae, and on one occasion the gut was seen to freeze separately from the haemocoel. There were no apparent qualitative differences in ice formation between freeze tolerant and non-freeze tolerant larvae. The time to complete freezing was positively related to temperature of nucleation (supercooling point, SCP), and SCP declined with decreasing body size, although this relationship was less strong in diapausing C. amoena. Nucleation generally occurred at a contact point with the thermocouple or chamber wall in non-diapausing larvae, but at random in diapausing larvae, suggesting that the latter have some control over ice nucleation. There were no apparent differences between freeze tolerant and non-freeze tolerant larvae in tracheal displacement or distension of the body during freezing, although there was markedly more distension in D. melanogaster than in C. amoena regardless of diapause state. We conclude that although control of ice nucleation appears to be important in freeze tolerant individuals, the physical ice formation process itself does not differ among larvae that can and cannot survive freezing. This suggests that a focus on cellular and biochemical mechanisms is appropriate and may reveal the primary adaptations allowing freeze tolerance in insects.
Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics, and the large-scale structure of the Universe in the coming years. These surveys typically require calculating redshifts for huge numbers (hundreds of millions to billions) of galaxies - almost all of which must be derived from photometry rather than spectroscopy. In this paper we investigate how using statistical models to understand the populations that make up the colour-magnitude distribution of galaxies can be combined with machine learning photometric redshift codes to improve redshift estimates. In particular we combine the use of Gaussian Mixture Models with the high performing machine learning photo-z algorithm GPz and show that modelling and accounting for the different colour-magnitude distributions of training and test data separately can give improved redshift estimates, reduce the bias on estimates by up to a half, and speed up the run-time of the algorithm. These methods are illustrated using data from deep optical and near infrared data in two separate deep fields, where training and test data of different colour-magnitude distributions are constructed from the galaxies with known spectroscopic redshifts, derived from several heterogeneous surveys.
Anthropogenic mercury (Hg) emissions to the atmosphere have increased the concentration of this potent neurotoxin in terrestrial and aquatic ecosystems. The magnitude of regional variation in atmospheric Hg pollution levels raises questions about the interactions between natural processes and human activities at local and regional scales that are shaping global atmospheric Hg cycling. Peatlands are potentially valuable and widespread records of past atmospheric Hg levels that could help address these questions. This perspective aims to improve the utility of peatlands as authentic Hg archives by summarizing the processes that could affect Hg cycling in peatlands. We identify the overlooked role of peat vegetation species and their primary productivity in Hg sequestration under climatic and anthropogenic activities. We provide recommendations to improve the reliability of using peat cores to reconstruct the atmospheric Hg levels from past decades to millennia. Better information from peatland archives on regional variation in atmospheric Hg levels will be of value for testing hypotheses about the processes controlling global Hg cycling. This information can also contribute to evaluating how well international efforts under the UNEP Minamata Convention are succeeding in reducing atmospheric Hg levels and deposition in different regions.
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