Hypercubes of AGN Tori (HYPERCAT). II. Resolving the Torus with Extremely Large Telescopes

Recent infrared interferometric observations revealed sub-parsec scale dust distributions around active galactic nuclei (AGNs). Using images of Clumpy torus models and NGC 1068 as an example, we demonstrate that the near- and mid-infrared nuclear emission of some nearby AGNs will be resolvable in direct imaging with the next generation of 30 m telescopes, potentially breaking degeneracies from previous studies that used integrated spectral energy distributions of unresolved AGN tori. To that effect we model wavelength-dependent point spread functions from the pupil images of various telescopes: James Webb Space Telescope, Keck, Giant Magellan Telescope, Thirty Meter Telescope, and Extremely Large Telescope. We take into account detector pixel scales and noise, and apply deconvolution techniques for image recovery. We also model 2D maps of the 10 μm silicate feature strength, S 10, of NGC 1068 and compare with observations. When the torus is resolved, we find S 10 variations across the image. However, to reproduce the S 10 measurements of an unresolved torus a dusty screen of A V > 9 mag is required. We also fit the first resolved image of the K-band emission in NGC 1068 recently published by the GRAVITY Collaboration, deriving likely model parameters of the underlying dust distribution. We find that both (1) an elongated structure suggestive of a highly inclined emission ring, and (2) a geometrically thin but optically thick flared disk where the emission arises from a narrow strip of hot cloud surface layers on the far inner side of the torus funnel, can explain the observations.

Membership Lists for 431 Open Clusters in Gaia DR2 Using Extreme Deconvolution Gaussian Mixture Models

Open clusters are groups of stars that form at the same time, making them an ideal laboratory to test theories of star formation, stellar evolution, and dynamics in the Milky Way disk. However, the utility of an open cluster can be limited by the accuracy and completeness of its known members. Here, we employ a “top-down” technique, Extreme Deconvolution Gaussian Mixture Models (XDGMMs), to extract and evaluate known open clusters from Gaia DR2 by fitting the distribution of stellar parallax and proper motion along a line of sight. Extreme deconvolution techniques can recover the intrinsic distribution of astrometric quantities, accounting for the full covariance matrix of the errors; this allows open cluster members to be identified even when presented with relatively uncertain measurement data. To date, open cluster studies have only applied extreme deconvolution to specialized searches for individual systems. We use XDGMMs to characterize the open clusters reported by Ahumada & Lapasset and are able to recover 420 of the 426 open clusters therein (98.1%). Our membership list contains the overwhelming majority (>95%) of previously known cluster members. We also identify a new, significant, and relatively faint cluster member population and validate their membership status using Gaia eDR3. We report the fortuitous discovery of 11 new open cluster candidates within the lines of sight we analyzed. We present our technique, as well as its advantages and challenges, and publish our membership lists and updated cluster parameters.

Deconvoluting acoustic beamforming maps with a deep neural network

Localization and quantification of noise sources is an important scientific and industrial problem, the use of phased arrays of microphones being the standard techniques in many applications. Non-physical artifacts appears on the output due to the nature of the method, thus, a supplementary step known as deconvolution is often performed. The use of data-driven machine learning can be a candidate to solve such problem. Neural networks can be extremely advantageous since no hypothesis concerning the environment or the characteristics of the sources are necessary, different from classical deconvolution techniques. Information on the acoustic propagation is implicitly extracted from pairs of source-output maps. On this work, a convolutional neural network is trained to deconvolute the beamforming map obtained from synthetic data simulating the response of an array of microphones. Quality of the estimation and the computational cost are compared to those of classical deconvolution methods (DAMAS, CLEAN-SC). Constraints associated with the size of the dataset used for training the neural network are also investigated and presented.

Estimation of Depth to Bouguer Anomaly Sources Using Euler Deconvolution Techniques

The geophysical measurement of variations in gravitational field of the Earth for a particular location is carried out through a gravity survey method. These variations termed anomalies can help investigate the subsurface of interest. An investigation was carried out using the airborne satellite-based (EGM08) gravity dataset to reveal the geological information inherent in a location. Qualitative analysis of the gravity dataset by filtering techniques of two-dimensional fast Fourier transform (FFT2D) shows that the area is made up of basement and sedimentary Formations. Further enhancements on the residual anomaly after separation show the sedimentary intrusion into the study area and zones of possible rock minerals of high and low density contrasts. Quantitative interpretations of the study area by 3-D Euler deconvolution depth estimation technique described the depth and locations of gravity bodies that yielded the gravity field. The result of the depth to basement approach was found to be in the depth range of 930 m to 2,686 m (for Structural Index, SI = 0). The research location is a probable area for economic mineral deposits and hydrocarbon exploration.

Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach

The deconvolution process is a key step for quantitative evaluation of fluorescence lifetime imaging microscopy (FLIM) samples. By this process, the fluorescence impulse responses (FluoIRs) of the sample are decoupled from the instrument response (InstR). In blind deconvolution estimation (BDE), the FluoIRs and InstR are jointly extracted from a dataset with minimal a priori information. In this work, two BDE algorithms are introduced based on linear combinations of multi-exponential functions to model each FluoIR in the sample. For both schemes, the InstR is assumed with a free-form and a sparse structure. The local perspective of the BDE methodology assumes that the characteristic parameters of the exponential functions (time constants and scaling coefficients) are estimated based on a single spatial point of the dataset. On the other hand, the same exponential functions are used in the whole dataset in the global perspective, and just the scaling coefficients are updated for each spatial point. A least squares formulation is considered for both BDE algorithms. To overcome the nonlinear interaction in the decision variables, an alternating least squares (ALS) methodology iteratively solves both estimation problems based on non-negative and constrained optimizations. The validation stage considered first synthetic datasets at different noise types and levels, and a comparison with the standard deconvolution techniques with a multi-exponential model for FLIM measurements, as well as, with two BDE methodologies in the state of the art: Laguerre basis, and exponentials library. For the experimental evaluation, fluorescent dyes and oral tissue samples were considered. Our results show that local and global perspectives are consistent with the standard deconvolution techniques, and they reached the fastest convergence responses among the BDE algorithms with the best compromise in FluoIRs and InstR estimation errors.

Numerical experiments on firn isotope diffusion with the Community Firn Model

Advances in analytical methods have made it possible to obtain high-resolution water isotopic data from ice cores. Their spectral signature contains information on the diffusion process that attenuated the isotopic signal during the firn densification process. Here, we provide a tool for estimating firn-diffusion rates that builds on the Community Firn Model. Our model requires two main inputs, temperature and accumulation, and it calculates the diffusion lengths for δ17O, δ18O and δD. Prior information on the isotopic signal of the precipitation is not a requirement. In combination with deconvolution techniques, diffusion lengths can be used in order reconstruct the pre-diffusion isotopic signal. Furthermore, the temperature dependence of the isotope diffusion and firn densification makes the diffusion length an interesting candidate as a temperature proxy. We test the model under steady state and transient scenarios and compare four densification models. Comparisons with ice core data provide an evaluation of the four models and indicate that there are differences in their performance. Combining data-based diffusion length estimates with information on past accumulation rates and ice flow thinning, we reconstruct absolute temperatures from three Antarctic ice core sites.

Heroin Fatality in a Feline: A Case Report with Postmortem Liver Concentrations

A case of feline intoxication and fatality with the illicit drug heroin is described. A 5-year-old castrated male domestic shorthair cat was recently diagnosed with an active pneumonitis and left at home for a couple of days under the care of another resident. Upon return, the owner found his cat dead with strong suspicion of foul play. The cat was necropsied by a local veterinary clinic to retrieve the liver for diagnostic toxicology. The postmortem liver sample screened positive for 6-acetylmorphine and 6-acetylcodeine by gas chromatography mass spectrometry. Deconvolution techniques were applied to chromatograms, which revealed the additional presence of morphine and mirtazapine. Subsequent quantitation of mirtazapine, heroin, morphine, 6-acetylmorphine and 6-acetylcodeine was performed by gas chromatography tandem quadrupole mass spectrometry. Although companion animal fatalities arising from toxicities are a likely consequence of drug abuse in a home, this is the first reported case of a malicious feline fatality resulting from heroin with quantitation of heroin metabolites.

The right tools for the job: the central role for next generation chemical probes and chemistry-based target deconvolution methods in phenotypic drug discovery

High quality chemical probes and chemistry-based target deconvolution techniques will be crucial to the advancement of phenotypic drug discovery, providing new hope for treatment of diseases with highly complex biology.