Multimode circular integrated optical microresonators: Coupled mode theory modeling

A frequency domain model of multimode circular microresonators for filter applications in integrated optics is investigated. Analytical basis modes of 2D bent waveguides or curved interfaces are combined with modes of straight channels in a spatial coupled mode theory framework. Free of fitting parameters, the model allows to predict quite efficiently the spectral response of the microresonators. It turns out to be sufficient to take only a few dominant cavity modes into account. Comparisons of these simulations with computationally more expensive rigorous numerical calculations show a satisfactory agreement

GALFIT-CORSAIR: implementing the core-Sersic model into GALFIT

We introduce GALFIT-CORSAIR: a publicly available, fully retro-compatible modification of the 2D fitting software GALFIT (v.3) which adds an implementation of the core-Sersic model. We demonstrate the software by fitting the images of NGC 5557 and NGC 5813, which have been previously identified as core-Sersic galaxies by their 1D radial light profiles. These two examples are representative of different dust obscuration conditions, and of bulge/disk decomposition. To perform the analysis, we obtained deep Hubble Legacy Archive (HLA) mosaics in the F555W filter (~V-band). We successfully reproduce the results of the previous 1D analysis, modulo the intrinsic differences between the 1D and the 2D fitting procedures. The code and the analysis procedure described here have been developed for the first coherent 2D analysis of a sample of core-Sersic galaxies, which will be presented in a forth-coming paper. As the 2D analysis provides better constraining on multi-component fitting, and is fully seeing-corrected, it will yield complementary constraints on the missing mass in depleted galaxy cores.Comment: Accepted for publication in PASP; A binary version of GALFIT-CORSAIR is publicly available at http://astronomy.swin.edu.au/~pbonfini/galfit-corsair

Theoretical Systematics of Future Baryon Acoustic Oscillation Surveys

Future Baryon Acoustic Oscillation surveys aim at observing galaxy clustering over a wide range of redshift and galaxy populations at great precision, reaching tenths of a percent, in order to detect any deviation of dark energy from the \LCDM model. We utilize a set of paired quasi-\Nb\, FastPM simulations that were designed to mitigate the sample variance effect on the BAO feature and evaluated the BAO systematics as precisely as $\sim 0.01\%$. We report anisotropic BAO scale shifts before and after density field reconstruction in the presence of redshift-space distortions over a wide range of redshift, galaxy/halo biases, and shot noise levels. We test different reconstruction schemes and different smoothing filter scales, and introduce physically-motivated BAO fitting models. For the first time, we derive a Galilean-invariant infrared resummed model for halos in real and redshift space. We test these models from the perspective of robust BAO measurements and non-BAO information such as growth rate and nonlinear bias. We find that pre-reconstruction BAO scale has moderate fitting-model dependence at the level of $0.1\%-0.2\%$ for matter while the dependence is substantially reduced to less than $0.07\%$ for halos. We find that post-reconstruction BAO shifts are generally reduced to below $0.1\%$ in the presence of galaxy/halo bias and show much smaller fitting model dependence. Different reconstruction conventions can potentially make a much larger difference on the line-of-sight BAO scale, upto $0.3\%$. Meanwhile, the precision (error) of the BAO measurements is quite consistent regardless of the choice of the fitting model or reconstruction convention.Comment: 33 pages, 19 figures, 4 tables. Submitted to MNRAS. Matches version accepted to MNRAS. Moderate changes were made during revision including a comparison between TreePM and FastPM BAO featur

Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter

The likelihood calculation of a vast number of particles is the computational bottleneck for the particle filter in applications where the observation information is rich. For fast computing the likelihood of particles, a numerical fitting approach is proposed to construct the Likelihood Probability Density Function (Li-PDF) by using a comparably small number of so-called fulcrums. The likelihood of particles is thereby analytically inferred, explicitly or implicitly, based on the Li-PDF instead of directly computed by utilizing the observation, which can significantly reduce the computation and enables real time filtering. The proposed approach guarantees the estimation quality when an appropriate fitting function and properly distributed fulcrums are used. The details for construction of the fitting function and fulcrums are addressed respectively in detail. In particular, to deal with multivariate fitting, the nonparametric kernel density estimator is presented which is flexible and convenient for implicit Li-PDF implementation. Simulation comparison with a variety of existing approaches on a benchmark 1-dimensional model and multi-dimensional robot localization and visual tracking demonstrate the validity of our approach.Comment: 42 pages, 17 figures, 4 tables and 1 appendix. This paper is a draft/preprint of one paper submitted to the IEEE Transaction

Model-based 3D gait biometrics

There have as yet been few gait biometrics approaches which use temporal 3D data. Clearly, 3D gait data conveys more information than 2D data and it is also the natural representation of human gait perceived by human. In this paper we explore the potential of using model-based methods in a 3D volumetric (voxel) gait dataset. We use a structural model including articulated cylinders with 3D Degrees of Freedom (DoF) at each joint to model the human lower legs. We develop a simple yet effective model-fitting algorithm using this gait model, correlation filter and a dynamic programming approach. Human gait kinematics trajectories are then extracted by fitting the gait model into the gait data. At each frame we generate a correlation energy map between the gait model and the data. Dynamic programming is used to extract the gait kinematics trajectories by selecting the most likely path in the whole sequence. We are successfully able to extract both gait structural and dynamics features. Some of the features extracted here are inherently unique to 3D data. Analysis on a database of 46 subjects each with 4 sample sequences, shows an encouraging correct classification rate and suggests that 3D features can contribute even more