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To unlock access to stronger winds, the offshore wind industry is advancing with significantly larger and taller wind turbines. This massive upscaling motivates a departure from univariate wind forecasting methods that traditionally focused on a single representative height. To fill this gap, we propose DeepMIDE--a statistical deep learning method which jointly models the offshore wind speeds across space, time, and height. DeepMIDE is formulated as a multi-output integro-difference equation model with a multivariate, nonstationary, and state-dependent kernel characterized by a set of advection vectors that encode the physics of wind field formation and propagation. Embedded within DeepMIDE, an advanced deep learning architecture learns these advection vectors from high dimensional streams of exogenous weather information, which, along with other parameters, are plugged back into the statistical model for probabilistic multi-height space-time forecasting. Tested on real-world data from future offshore wind energy sites in the Northeastern United States, the wind speed and power forecasts from DeepMIDE are shown to outperform those from prevalent time series, spatio-temporal, and deep learning methods.

We have carried out spatially resolved thermal/nonthermal separation on two edge-on galaxies, NGC~3044 and NGC~5775, using only radio data. Narrow-band imaging within a frequency band that is almost contiguous from 1.25 to 7.02 GHz (L-band, S-band and C-band) has allowed us to fit spectra and construct thermal, nonthermal, and nonthermal spectral index maps. This method does not require any ancillary H$\alpha$ and infrared data, or reliance on dust corrections that are challenging in edge-on galaxies. For NGC~3044, at 15 arcsec resolution, we find a median thermal fraction of $\sim\, 13$\% with an estimated uncertainty in this fraction of $\sim\, 50$\% at 4.13 GHz. This compares well with the H$\alpha$ mixture method results. We uncovered evidence for a vertical outflow feature reaching at least $z\,\sim\,3.5$ kpc in projection above the plane, reminiscent of M82's starburst wind. For the higher SFR galaxy, NGC~5775 at 12 arcsec resolution, we find a median thermal fraction of 44\% at 4.13 GHz with an estimated error on this fraction of 17\%. Both galaxies show a change of slope (flattening) in L-band. These results suggest that a radio-only method for separating thermal from nonthermal emission is not only feasible, but able to reveal new features that might otherwise be obscured in edge-on disks., Comment: Accepted to AJ on July 18, 2024

The problem of missing satellites still remains one of the well-known problems of the Lambda-CDM cosmological theory. Despite significant progress in cosmological modeling achieved in recent years, the search for new dwarf galaxies-satellites of nearby giant galaxies remains extremely important. In this series of papers we report the first results of an on-going systematic survey of faint dwarf spheroidal galaxies in the vicinity of the bright late-type spiral NGC 253 galaxy, the brightest member of the Sculptor filament located at a distance of 3.7 Mpc. We performed a new NGC 253 satellite search by means of visual inspection using co-added image cutouts reprocessed in the DESI Legacy image surveys, reaching a very low surface brightness regime (28.0--29.0 mag arcsec-2). Five new dwarf galaxies have been discovered in the vicinity of NGC 253, that we named them Do V, Do VI, Do VII, Do VIII and Do IX. Assuming they are associated to NGC 253, their total absolute V-magnitudes fall in the -7 to -9 mag range, which is typical for dwarf satellites in the local Universe. The central surface brightness tend to be extremely low for all the discovered dwarfs and fall roughly in the range of 25--26 mag arcsec-2 in g-band. We present a new list of galaxies located around the giant spiral NGC 253. With the inclusion of these additional satellites, the overall spatial distribution of the system becomes less flattened and is now broadly consistent with analogs drawn from Lambda-CDM expectations. Interestingly, the distribution appears to be rather lopsided. Yet, firm conclusions on the presence of absence of a correlated satellite structure are hampered since distance information is lacking, the census of observed dwarfs in the system remains far from complete, and spectroscopic velocities are not even available for most known satellites., Comment: 10 pages, 6 figures, 4 Tables, submitted to Astronomy and Astrophysics

This article addresses the problem of multi-object tracking by using a non-deterministic model of target behaviors with hard constraints. To capture the evolution of target features as well as their locations, we permit objects to lie in a general topological target configuration space, rather than a Euclidean space. We obtain tracker performance bounds based on sample rates, and derive a flexible, agnostic tracking algorithm. We demonstrate our algorithm on two scenarios involving laboratory and field data.

We have investigated the properties (e.g., age, metallicity) of the stellar populations of a ring-like tidal stellar stream (or streams) around the edge-on galaxy SPRC047 (z = 0.031) using spectral energy distribution (SED) fits to integrated broad-band aperture flux densities. We used visual images in six different bands and Spitzer/IRAC 3.6 micron data. We have attempted to derive best-fit stellar population parameters (metallicity, age) in three non-contiguous segments of the stream. Due to the very low surface brightness of the stream, we have performed a deconvolution with a Richardson-Lucy type algorithm of the low spatial resolution 3.6 micron IRAC image, thereby reducing the effect of the point-spread-function (PSF) aliased "emission" from the bright edge-on central galaxy at the locations of our three stream segments. Our SED fits that used several different star formation history priors, from an exponentially decaying star formation burst to continuous star formation, indicate that the age-metallicity-dust degeneracy is not resolved, most likely because of inadequate wavelength coverage and low signal-to-noise ratios of the low surface brightness features. We also discuss how future deep visual-near-infrared observations, combined with absolute flux calibration uncertainties at or below the 1 per cent level, complemented by equally well absolute flux calibrated observations in ultraviolet and mid-infrared bands, would improve the accuracy of broad-band SED fitting results for low surface brightness targets, such as stellar streams around nearby galaxies that are not resolved into stars., Comment: Accepted for publication in the Astrophysical Journal

Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.

The extraplanar diffuse ionized gas (eDIG) represents the cool/warm ionized gas reservoir around galaxies. We present a spatial analysis of H$\alpha$ images of 22 nearby edge-on spiral galaxies from the CHANG-ES sample (the eDIG-CHANGES project), taken with the APO 3.5m telescope, in order to study their eDIG. We conduct an exponential fit to the vertical intensity profiles of the sample galaxies, of which 16 can be decomposed into a thin disk plus an extended thick disk component. The median value of the scale height (h) of the extended component is $1.13\pm 0.14$ kpc. We find a tight sublinear correlation between h and the SFR. Moreover, the offset of individual galaxies from the best-fit SFR-h relation shows significant anti-correlation with SFR_SD. This indicates that galaxies with more intense star formation tend to have disproportionately extended eDIG. Combined with data from the literature, we find that the correlations between the eDIG properties and the galaxies' properties extend to broader ranges. We further compare the vertical extension of the eDIG to multi-wavelength measurements of other CGM phases. We find the eDIG to be slightly more extended than the neutral gas (HI 21-cm line), indicating the existence of some extended ionizing sources. Most galaxies have an X-ray scale height smaller than the h, suggesting that the majority of the X-ray emission detected in shallow observations are actually from the thick disk. The h is comparable to the L-band radio continuum scale height, both slightly larger than that at higher frequencies (C-band), where the cooling is stronger and the thermal contribution may be larger. The comparable H$\alpha$ and L-band scale height indicates that the thermal and non-thermal electrons have similar spatial distributions. This further indicates that the thermal gas, the cosmics rays, and the magnetic field may be close to energy equipartition.

AT 2000ch is a highly variable massive star and supernova imposter in NGC 3432 first detected in 2000. It is similar and often compared to SN 2009ip, and it is therefore expected to undergo a core-collapse supernova -- a SN imposter of similar brightness -- in the near future. We characterize the long-term variability of AT 2000ch in the radio and optical regimes with archival data reaching back to the year 1984. We use these newly reduced observations in addition to observations in the literature to restrict the mass-loss rates of AT 2000ch at multiple epochs based on different approaches, and to infer the general properties of its circumstellar nebula with respect to the detected radio brightness. We extend the known optical light curve of AT 2000ch up to the beginning of 2022 by performing point spread function photometry on archival data from the Palomar Transient Factory and the Zwicky Transient Facility. We reduced archival radio continuum observations obtained with the Very Large Array using standard calibration and imaging methods and complemented these with pre-reduced \changes observations as well as observations obtained with the Westerbork Synthesis Radio Telescope and LOw Frequency ARray. For the analysis of AT 2000ch, we consider the optical light curve and color evolution, its radio continuum brightness at different frequencies and times, and the corresponding spectral indices. We estimated mass-loss rates and optical depths based on radio continuum brightnesses and Ha fluxes. We report two newly detected outbursts of AT 2000ch similar to those found in the 2000s and 13 re-brightening events, of which at least four are not conclusively detected because of insufficient sampling of the light curve. The dates of all outbursts and significant, well-sampled re-brightening events are consistent with a period of $\sim 201 \pm 12\,$days over a total time-span of two decades. Such a behavior has never been found for any SN imposter, especially not for candidate SN~2009ip analogs. During 2010 to 2012 and 2014 to 2018, we only have a few detections, which is insufficient to come to any conclusion as to a possible less eruptive phase of the transient. We find steady dimming after the most recent re-brightening events and possible evidence of porosity in the circumstellar envelope, suggesting AT~2000ch may currently be in transition to a state of relative calm. We identified a second, unrelated source at a projected distance of $\sim 23\,$pc ($\sim0.5^{\prime\prime}$) that has contaminated the optical measurements of AT~2000ch at its minimum luminosity over the last two decades probably on a $5\%-10\,\%$ level, but this does not affect our overall findings and is negligible during re-brightening. We are able to restrict the mass-loss rate of AT~2000ch to range between several $10^{-6}\,\textrm{M}_{\odot}/\textrm{yr}$ and several $10^{-5}\,\textrm{M}_{\odot}/\textrm{yr}$. The fresh ejecta appear to be optically thick to radio continuum emission at least within the first $\sim 25\,$days after significant re-brightening., Comment: Accepted for publication on A&A on 23/10/2022

Current models for spatial extremes are concerned with the joint upper (or lower) tail of the distribution at two or more locations. Such models cannot account for teleconnection patterns of two-meter surface air temperature ($T_{2m}$) in North America, where very low temperatures in the contiguous Unites States (CONUS) may coincide with very high temperatures in Alaska in the wintertime. This dependence between warm and cold extremes motivates the need for a model with opposite-tail dependence in spatial extremes. This work develops a statistical modeling framework which has flexible behavior in all four pairings of high and low extremes at pairs of locations. In particular, we use a mixture of rotations of common Archimedean copulas to capture various combinations of four-corner tail dependence. We study teleconnected $T_{2m}$ extremes using ERA5 reanalysis of daily average two-meter temperature during the boreal winter. The estimated mixture model quantifies the strength of opposite-tail dependence between warm temperatures in Alaska and cold temperatures in the midlatitudes of North America, as well as the reverse pattern. These dependence patterns are shown to correspond to blocked and zonal patterns of mid-tropospheric flow. This analysis extends the classical notion of correlation-based teleconnections to considering dependence in higher quantiles.

A common approach to approximating Gaussian log-likelihoods at scale exploits the fact that precision matrices can be well-approximated by sparse matrices in some circumstances. This strategy is motivated by the \emph{screening effect}, which refers to the phenomenon in which the linear prediction of a process $Z$ at a point $\mathbf{x}_0$ depends primarily on measurements nearest to $\mathbf{x}_0$. But simple perturbations, such as i.i.d. measurement noise, can significantly reduce the degree to which this exploitable phenomenon occurs. While strategies to cope with this issue already exist and are certainly improvements over ignoring the problem, in this work we present a new one based on the EM algorithm that offers several advantages. While in this work we focus on the application to Vecchia's approximation (1988), a particularly popular and powerful framework in which we can demonstrate true second-order optimization of M steps, the method can also be applied using entirely matrix-vector products, making it applicable to a very wide class of precision matrix-based approximation methods.

Nonstationary Gaussian process models can capture complex spatially varying dependence structures in spatial datasets. However, the large number of observations in modern datasets makes fitting such models computationally intractable with conventional dense linear algebra. In addition, derivative-free or even first-order optimization methods can be slow to converge when estimating many spatially varying parameters. We present here a computational framework that couples an algebraic block-diagonal plus low-rank covariance matrix approximation with stochastic trace estimation to facilitate the efficient use of second-order solvers for maximum likelihood estimation of Gaussian process models with many parameters. We demonstrate the effectiveness of these methods by simultaneously fitting 192 parameters in the popular nonstationary model of Paciorek and Schervish using 107,600 sea surface temperature anomaly measurements.

Max-stable processes are the most popular models for high-impact spatial extreme events, as they arise as the only possible limits of spatially-indexed block maxima. However, likelihood inference for such models suffers severely from the curse of dimensionality, since the likelihood function involves a combinatorially exploding number of terms. In this paper, we propose using the Vecchia approximation, which conveniently decomposes the full joint density into a linear number of low-dimensional conditional density terms based on well-chosen conditioning sets designed to improve and accelerate inference in high dimensions. Theoretical asymptotic relative efficiencies in the Gaussian setting and simulation experiments in the max-stable setting show significant efficiency gains and computational savings using the Vecchia likelihood approximation method compared to traditional composite likelihoods. Our application to extreme sea surface temperature data at more than a thousand sites across the entire Red Sea further demonstrates the superiority of the Vecchia likelihood approximation for fitting complex models with intractable likelihoods, delivering significantly better results than traditional composite likelihoods, and accurately capturing the extremal dependence structure at lower computational cost.

The Mat\'ern covariance function is ubiquitous in the application of Gaussian processes to spatial statistics and beyond. Perhaps the most important reason for this is that the smoothness parameter $\nu$ gives complete control over the mean-square differentiability of the process, which has significant implications for the behavior of estimated quantities such as interpolants and forecasts. Unfortunately, derivatives of the Mat\'ern covariance function with respect to $\nu$ require derivatives of the modified second-kind Bessel function $\mathcal{K}_\nu$ with respect to $\nu$. While closed form expressions of these derivatives do exist, they are prohibitively difficult and expensive to compute. For this reason, many software packages require fixing $\nu$ as opposed to estimating it, and all existing software packages that attempt to offer the functionality of estimating $\nu$ use finite difference estimates for $\partial_\nu \mathcal{K}_\nu$. In this work, we introduce a new implementation of $\mathcal{K}_\nu$ that has been designed to provide derivatives via automatic differentiation (AD), and whose resulting derivatives are significantly faster and more accurate than those computed using finite differences. We provide comprehensive testing for both speed and accuracy and show that our AD solution can be used to build accurate Hessian matrices for second-order maximum likelihood estimation in settings where Hessians built with finite difference approximations completely fail.

In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values that exceed a threshold or as maximum values over distinct blocks of time, and subsequent estimation procedures are motivated by asymptotic theory for extremes of random processes. For environmental data, nonstationary behavior in the bulk of the distribution, such as seasonality or climate change, will also be observed in the tails. To accurately model such nonstationarity, it seems natural to use the entire dataset rather than just the most extreme values. It is also common to observe different types of nonstationarity in each tail of a distribution. Most work on extremes only focuses on one tail of a distribution, but for temperature, both tails are of interest. This paper builds on a recently proposed parametric model for the entire probability distribution that has flexible behavior in both tails. We apply an extension of this model to historical records of daily mean temperature at several locations across the United States with different climates and local conditions. We highlight the ability of the method to quantify changes in the bulk and tails across the year over the past decades and under different geographic and climatic conditions. The proposed model shows good performance when compared to several benchmark models that are typically used in extreme value analysis of temperature.

We propose to use deep learning to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible. We show how to estimate parameters from max-stable processes, where inference is exceptionally challenging even with small datasets but simulation is straightforward. We use data from model simulations as input and train deep neural networks to learn statistical parameters. Our neural-network-based method provides a competitive alternative to current approaches, as demonstrated by considerable accuracy and computational time improvements. It serves as a proof of concept for deep learning in statistical parameter estimation and can be extended to other estimation problems.