The infrared imaging spectrograph (IRIS) for TMT: sensitivities and simulations

We present sensitivity estimates for point and resolved astronomical sources for the current design of the InfraRed Imaging Spectrograph (IRIS) on the future Thirty Meter Telescope (TMT). IRIS, with TMT's adaptive optics system, will achieve unprecedented point source sensitivities in the near-infrared (0.84 - 2.45 {\mu}m) when compared to systems on current 8-10m ground based telescopes. The IRIS imager, in 5 hours of total integration, will be able to perform a few percent photometry on 26 - 29 magnitude (AB) point sources in the near-infrared broadband filters (Z, Y, J, H, K). The integral field spectrograph, with a range of scales and filters, will achieve good signal-to-noise on 22 - 26 magnitude (AB) point sources with a spectral resolution of R=4,000 in 5 hours of total integration time. We also present simulated 3D IRIS data of resolved high-redshift star forming galaxies (1 < z < 5), illustrating the extraordinary potential of this instrument to probe the dynamics, assembly, and chemical abundances of galaxies in the early universe. With its finest spatial scales, IRIS will be able to study luminous, massive, high-redshift star forming galaxies (star formation rates ~ 10 - 100 M yr-1) at ~100 pc resolution. Utilizing the coarsest spatial scales, IRIS will be able to observe fainter, less massive high-redshift galaxies, with integrated star formation rates less than 1 M yr-1, yielding a factor of 3 to 10 gain in sensitivity compared to current integral field spectrographs. The combination of both fine and coarse spatial scales with the diffraction-limit of the TMT will significantly advance our understanding of early galaxy formation processes and their subsequent evolution into presentday galaxies.Comment: SPIE Astronomical Instrumentation 201

Characterization of Surface Karst Using LiDAR and Field Traverses, Fort Hood Military Installation, Coryell County, Texas

The Fort Hood Military Installation is a karst landscape that has been significantly altered for training exercises that include heavy vehicle maneuvers and simulated combat. Traditional karst surveys are often time-consuming and require extensive field analyses to adequately characterize large areas. Bias is given to areas that are most easily accessible and false negatives are common. Previous studies conducted in the eastern and western portion of the base have understated the abundance and spatial distribution of karst, particularly in the western portion. This study used field traverses and 0.5-meter Light Detection and Ranging (LiDAR) data to characterize surface karst depressions, create a set of new and refined filters and buffering mechanisms to remove non-karst depressions, and determine the accuracy of the model. LiDAR data was used to create a digital elevation model (DEM), which was used to extract areas with localized depressions at a sub-meter scale. In order to isolate features that were formed through karst processes, data were processed through a series of filters with parameters based on features found during traverse surveys. Field verifications to assess the accuracy of the LiDAR conducted with previous filters and buffering mechanisms had an overall accuracy of 77.3%, indicating this model overestimated the number of features in the study area. To assess the accuracy of the new filters and buffering parameters, field verified features from a random point survey and a remote verification survey of features within each of the filters was conducted. The overall accuracy was 84.1%, indicating that the new filters and buffering parameters improved depression characterization and the ability to determine those features that were influenced by natural and anthropogenic processes

A second-order PHD filter with mean and variance in target number

The Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filters are popular solutions to the multi-target tracking problem due to their low complexity and ability to estimate the number and states of targets in cluttered environments. The PHD filter propagates the first-order moment (i.e. mean) of the number of targets while the CPHD propagates the cardinality distribution in the number of targets, albeit for a greater computational cost. Introducing the Panjer point process, this paper proposes a second-order PHD filter, propagating the second-order moment (i.e. variance) of the number of targets alongside its mean. The resulting algorithm is more versatile in the modelling choices than the PHD filter, and its computational cost is significantly lower compared to the CPHD filter. The paper compares the three filters in statistical simulations which demonstrate that the proposed filter reacts more quickly to changes in the number of targets, i.e., target births and target deaths, than the CPHD filter. In addition, a new statistic for multi-object filters is introduced in order to study the correlation between the estimated number of targets in different regions of the state space, and propose a quantitative analysis of the spooky effect for the three filters

Spectral filtering as a method of visualising and removing striped artefacts in digital elevation data

Spectral filtering was compared with traditional mean spatial filters to assess their ability to identify and remove striped artefacts in digital elevation data. The techniques were applied to two datasets: a 100 m contour derived digital elevation model (DEM) of southern Norway and a 2 m LiDAR DSM of the Lake District, UK. Both datasets contained diagonal data artefacts that were found to propagate into subsequent terrain analysis. Spectral filtering used fast Fourier transformation (FFT) frequency data to identify these data artefacts in both datasets. These were removed from the data by applying a cut filter, prior to the inverse transform. Spectral filtering showed considerable advantages over mean spatial filters, when both the absolute and spatial distribution of elevation changes made were examined. Elevation changes from the spectral filtering were restricted to frequencies removed by the cut filter, were small in magnitude and consequently avoided any global smoothing. Spectral filtering was found to avoid the smoothing of kernel based data editing, and provided a more informative measure of data artefacts present in the FFT frequency domain. Artefacts were found to be heterogeneous through the surfaces, a result of their strong correlations with spatially autocorrelated variables: landcover and landsurface geometry. Spectral filtering performed better on the 100 m DEM, where signal and artefact were clearly distinguishable in the frequency data. Spectrally filtered digital elevation datasets were found to provide a superior and more precise representation of the landsurface and be a more appropriate dataset for any subsequent geomorphological applications

Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model

Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles. While the single-cell techniques are extremely powerful and hold great promise, they are currently still labor intensive, have a high cost per cell, and, most importantly, do not provide information on spatial distribution of cell types in specific regions of the brain. We propose a complementary approach that uses computational methods to infer the cell types and their gene expression profiles through analysis of brain-wide single-cell resolution in situ hybridization (ISH) imagery contained in the Allen Brain Atlas (ABA). We measure the spatial distribution of neurons labeled in the ISH image for each gene and model it as a spatial point process mixture, whose mixture weights are given by the cell types which express that gene. By fitting a point process mixture model jointly to the ISH images, we infer both the spatial point process distribution for each cell type and their gene expression profile. We validate our predictions of cell type-specific gene expression profiles using single cell RNA sequencing data, recently published for the mouse somatosensory cortex. Jointly with the gene expression profiles, cell features such as cell size, orientation, intensity and local density level are inferred per cell type