Likelihood Non-Gaussianity in Large-Scale Structure Analyses

Standard present day large-scale structure (LSS) analyses make a major assumption in their Bayesian parameter inference --- that the likelihood has a Gaussian form. For summary statistics currently used in LSS, this assumption, even if the underlying density field is Gaussian, cannot be correct in detail. We investigate the impact of this assumption on two recent LSS analyses: the Beutler et al. (2017) power spectrum multipole ($P_\ell$) analysis and the Sinha et al. (2017) group multiplicity function ($\zeta$) analysis. Using non-parametric divergence estimators on mock catalogs originally constructed for covariance matrix estimation, we identify significant non-Gaussianity in both the $P_\ell$ and $\zeta$ likelihoods. We then use Gaussian mixture density estimation and Independent Component Analysis on the same mocks to construct likelihood estimates that approximate the true likelihood better than the Gaussian $pseudo$-likelihood. Using these likelihood estimates, we accurately estimate the true posterior probability distribution of the Beutler et al. (2017) and Sinha et al. (2017) parameters. Likelihood non-Gaussianity shifts the $f\sigma_8$ constraint by $-0.44\sigma$, but otherwise, does not significantly impact the overall parameter constraints of Beutler et al. (2017). For the $\zeta$ analysis, using the pseudo-likelihood significantly underestimates the uncertainties and biases the constraints of Sinha et al. (2017) halo occupation parameters. For $\log M_1$ and $\alpha$, the posteriors are shifted by $+0.43\sigma$ and $-0.51\sigma$ and broadened by $42\%$ and $66\%$, respectively. The divergence and likelihood estimation methods we present provide a straightforward framework for quantifying the impact of likelihood non-Gaussianity and deriving more accurate parameter constraints.Comment: 33 pages, 7 figure

A 10 GHz Quasi-Optical Grid Amplifier Using Integrated HBT Differential Pairs

We report the fabrication and testing of a 10 GHz grid amplifier utilizing sixteen GaAs chips each containing an HBT differential pair plus integral bias/feedback resistors. The overall amplifier consists of a 4x4 array of unit cells on an RT Duroid™ board having a relative permittivity of 2.2. Each unit cell consists of an emitter-coupled differential pair at the center, an input antenna which extends horizontally in both directions from the two base leads, an output antenna which extends vertically in both directions from the two collector leads, and high inductance bias lines. In operation, the active grid array is placed between a pair of crossed polarizers. The horizontally polarized input wave passes through the input polarizer and couples to the input leads. An amplified current then flows on the vertical leads, which radiate a vertically polarized amplified signal through the output polarizer. The polarizers serve dual functions, providing both input-output isolation as well as independent impedance matching for the input and output ports. The grid thus functions essentially as a free-space beam amplifier. Calculations indicate that output powers of several watts per square centimeter of grid area should be attainable with optimized structures

Suppressed dependence of polarization on epitaxial strain in highly polar ferroelectrics

A combined experimental and computational investigation of coupling between polarization and epitaxial strain in highly polar ferroelectric PbZr_0.2Ti_0.8O_3 (PZT) thin films is reported. A comparison of the properties of relaxed (tetragonality c/a = 1.05) and highly-strained (c/a = 1.09) epitaxial films shows that polarization, while being amongst the highest reported for PZT or PbTiO_3 in either film or bulk forms (P_r = 82 microC/cm^2), is almost independent of the epitaxial strain. We attribute this behavior to a suppressed sensitivity of the A-site cations to epitaxial strain in these Pb-based perovskites, where the ferroelectric displacements are already large, contrary to the case of less polar perovskites, such as BaTiO_3. In the latter case, the A-site cation (Ba) and equatorial oxygen displacements can lead to substantial polarization increases.Comment: 4 pages, 3 figure

Label Transfer from APOGEE to LAMOST: Precise Stellar Parameters for 450,000 LAMOST Giants

In this era of large-scale stellar spectroscopic surveys, measurements of stellar attributes ("labels," i.e. parameters and abundances) must be made precise and consistent across surveys. Here, we demonstrate that this can be achieved by a data-driven approach to spectral modeling. With The Cannon, we transfer information from the APOGEE survey to determine precise Teff, log g, [Fe/H], and [$\alpha$/M] from the spectra of 450,000 LAMOST giants. The Cannon fits a predictive model for LAMOST spectra using 9952 stars observed in common between the two surveys, taking five labels from APOGEE DR12 as ground truth: Teff, log g, [Fe/H], [\alpha/M], and K-band extinction $A_k$. The model is then used to infer Teff, log g, [Fe/H], and [$\alpha$/M] for 454,180 giants, 20% of the LAMOST DR2 stellar sample. These are the first [$\alpha$/M] values for the full set of LAMOST giants, and the largest catalog of [$\alpha$/M] for giant stars to date. Furthermore, these labels are by construction on the APOGEE label scale; for spectra with S/N > 50, cross-validation of the model yields typical uncertainties of 70K in Teff, 0.1 in log g, 0.1 in [Fe/H], and 0.04 in [$\alpha$/M], values comparable to the broadly stated, conservative APOGEE DR12 uncertainties. Thus, by using "label transfer" to tie low-resolution (LAMOST R $\sim$ 1800) spectra to the label scale of a much higher-resolution (APOGEE R $\sim$ 22,500) survey, we substantially reduce the inconsistencies between labels measured by the individual survey pipelines. This demonstrates that label transfer with The Cannon can successfully bring different surveys onto the same physical scale.Comment: 27 pages, 14 figures. Accepted by ApJ on 16 Dec 2016, implementing suggestions from the referee reports. Associated code available at https://github.com/annayqho/TheCanno

Boundary Segmentation For Fluorescence Microscopy Using Steerable Filters

Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation

Field ecology of sylvatic Rhodnius populations (Heteroptera, Triatominae): risk factors for palm tree infestation in western Ecuador.

Most Rhodnius species (Triatominae) are primarily associated with palm trees. They maintain enzootic Trypanosoma cruzi transmission and are responsible for human infection (causing Chagas disease) through the Neotropics. Assessing whether individual palm traits (ecological and/or botanical) may increase the risk of palm infestation by triatomines is relevant in areas where bugs invade houses flying from peridomestic palms. We developed a novel fieldwork approach with that objective, and applied it to study infestation by sylvatic Rhodnius ecuadoriensis in 110 tagua palms (Phytelephas aequatorialis). Palm infestation (23% overall) was non-randomly distributed in our sample. Palms located in anthropic landscapes were frequently infested (>27%, n=92), whereas no bugs were collected from palms surveyed within forest remnants (n=18; P=0.01). The presence of abundant decaying vegetable matter (P=0.001) and (to a lesser extent) epiphytic plants (P=0.049) on palm crowns and stems increased the probability of infestation and was positively correlated with the apparent density of bug colonies (R2=0.68). A trend towards higher infestation rates in male palms (34% vs. 18%) could relate to female palm management (removal of infrutescences and vegetable debris) in areas where palm seeds are harvested. An outline of 'risk palm ecotopes' and environmental management-based strategies for the control of peridomestic, palm tree-living vector populations are proposed

Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks

Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D

Chemical tagging can work: Identification of stellar phase-space structures purely by chemical-abundance similarity

Chemical tagging promises to use detailed abundance measurements to identify spatially separated stars that were in fact born together (in the same molecular cloud), long ago. This idea has not yielded much practical success, presumably because of the noise and incompleteness in chemical-abundance measurements. We have succeeded in substantially improving spectroscopic measurements with The Cannon, which has now delivered 15 individual abundances for ~100,000 stars observed as part of the APOGEE spectroscopic survey, with precisions around 0.04 dex. We test the chemical-tagging hypothesis by looking at clusters in abundance space and confirming that they are clustered in phase space. We identify (by the k-means algorithm) overdensities of stars in the 15-dimensional chemical-abundance space delivered by The Cannon, and plot the associated stars in phase space. We use only abundance-space information (no positional information) to identify stellar groups. We find that clusters in abundance space are indeed clusters in phase space. We recover some known phase-space clusters and find other interesting structures. This is the first-ever project to identify phase-space structures at survey-scale by blind search purely in abundance space; it verifies the precision of the abundance measurements delivered by The Cannon; the prospects for future data sets appear very good.Comment: accepted for publication in the Ap

An Affine-Invariant Sampler for Exoplanet Fitting and Discovery in Radial Velocity Data

Markov Chain Monte Carlo (MCMC) proves to be powerful for Bayesian inference and in particular for exoplanet radial velocity fitting because MCMC provides more statistical information and makes better use of data than common approaches like chi-square fitting. However, the non-linear density functions encountered in these problems can make MCMC time-consuming. In this paper, we apply an ensemble sampler respecting affine invariance to orbital parameter extraction from radial velocity data. This new sampler has only one free parameter, and it does not require much tuning for good performance, which is important for automatization. The autocorrelation time of this sampler is approximately the same for all parameters and far smaller than Metropolis-Hastings, which means it requires many fewer function calls to produce the same number of independent samples. The affine-invariant sampler speeds up MCMC by hundreds of times compared with Metropolis-Hastings in the same computing situation. This novel sampler would be ideal for projects involving large datasets such as statistical investigations of planet distribution. The biggest obstacle to ensemble samplers is the existence of multiple local optima; we present a clustering technique to deal with local optima by clustering based on the likelihood of the walkers in the ensemble. We demonstrate the effectiveness of the sampler on real radial velocity data.Comment: 24 pages, 7 figures, accepted to Ap