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Optimal sampling strategies are critical for surveys of deeper coral reef and shoal systems, due to the significant cost of accessing and field sampling these remote and poorly understood ecosystems. Additionally, well-established standard…
We utilize a Bayesian approach to fit the observed mid-IR-to-submm/mm spectral energy distributions (SEDs) of 22 WISE-selected and submm-detected, hyperluminous hot dust-obscured galaxies. By adopting the Torus+GB model, we decompose the…
Robustly determining the optimal number of clusters in a data set is an essential factor in a wide range of applications. Cluster enumeration becomes challenging when the true underlying structure in the observed data is corrupted by…
Quiescent solar prominence fine structures are typically modelled as density enhancements, called threads, which occupy a fraction of a longer magnetic flux tube. The profile of the mass density along the magnetic field is however unknown…
To optimize electron energy toward in-situ imaging large bio-samples up to 10-um thickness with nanoscale resolution, we implemented an analytical model based on elastic and inelastic characteristic angles [1]. This model can be used to…
This letter utilizes composite spectral energy distributions (SEDs) constructed from NEWFIRM Medium-Band Survey photometry to constrain the dust attenuation curve in 0.5<z<2.0 galaxies. Based on similarities between the full SED shapes…
Computer models, aiming at simulating a complex real system, are often calibrated in the light of data to improve performance. Standard calibration methods assume that the optimal values of calibration parameters are invariant to the model…
Based on the distribution of tidal deformabilities and component masses of binary neutron star merger GW170817, the parametric equation of states (EOS) are employed to probe the nuclear symmetry energy and the properties of neutron star. To…
Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood…
Aims. Deriving accurate frequencies, amplitudes, and mode lifetimes from stochastically driven pulsation is challenging, more so, if one demands that realistic error estimates be given for all model fitting parameters. As has been shown by…
Local increases in the mean of a random field are detected (conservatively) by thresholding a field of test statistics at a level $u$ chosen to control the tail probability or $p$-value of its maximum. This $p$-value is approximated by the…
Sophisticated spectral energy distribution (SED) models describe dust attenuation and emission using geometry parameters. This treatment is natural since dust effects are driven by the underlying star-dust geometry in galaxies. An example…
We describe perturbation theory (PT) models of galaxy bias for applications to photometric galaxy surveys. We model the galaxy-galaxy and galaxy-matter correlation functions in configuration space and validate against measurements from mock…
We report results from multi-epoch X-ray observations of the polar-scattered Seyfert 1 galaxy ESO 323-G77. The source exhibits remarkable spectral variability from months to years timescales. The observed spectral variability is entirely…
Bayesian model selection provides the cosmologist with an exacting tool to distinguish between competing models based purely on the data, via the Bayesian evidence. Previous methods to calculate this quantity either lacked general…
The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly more prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration…
In this work a robust clustering algorithm for stationary time series is proposed. The algorithm is based on the use of estimated spectral densities, which are considered as functional data, as the basic characteristic of stationary time…
Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The…
We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a Scanning Electron Microscope. An image results from a sequence of projections of the…
Taurus is a balloon-borne cosmic microwave background (CMB) experiment optimized to map the E-mode polarization and Galactic foregrounds at the largest angular scales ($\ell$ $\lt$ 30) and improve measurements of the optical depth to…