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We introduce BAYES-LOSVD, a novel implementation of the non-parametric extraction of line-of-sight velocity distributions (LOSVDs) in galaxies. We employ bayesian inference to obtain robust LOSVDs and associated uncertainties. Our method relies on principal component analysis to reduce the dimensionality of the base of templates required for the extraction and thus increase the performance of the code. In addition, we implement several options to regularise the output solutions. Our tests, conducted on mock spectra, confirm the ability of our approach to model a wide range of LOSVD shapes, overcoming limitations of the most widely used parametric methods (e.g. Gauss-Hermite expansion). We present examples of LOSVD extractions for real galaxies with known peculiar LOSVD shapes, i.e. NGC4371, IC0719 and NGC4550, using MUSE and SAURON integral-field unit (IFU) data. Our implementation can also handle data from other popular IFU surveys (e.g. ATLAS3D, CALIFA, MaNGA, SAMI). Details of the code and relevant documentation are freely available to the community in the dedicated repositories.
We present an improved study of the expected shape of the line-of-sight velocity distribution in shell galaxies. We found a simple analytical expression connecting prominent and in principle observable characteristics of the line profile and mass-distribution of the galaxy. The prediction was compared with the results from a test-particle simulation of a radial merger.
We investigate line-of-sight velocity distribution (LOSVD) corrections for absorption line-strength indices of early-type galaxies in the Lick/IDS system. This system is often used to estimate basic stellar population parameters such as luminosity weighted ages and metallicities. Using single stellar population model spectral energy distributions by Vazdekis (1999) we find that the LOSVD corrections are largely insensitive to changes in the stellar populations for old galaxies (age >3 Gyr). Only the Lick/IDS Balmer series indices show an appreciable effect, which is on the order of the correction itself. Furthermore, we investigate the sensitivity of the LOSVD corrections to non-Gaussian LOSVDs. In this case the LOSVD can be described by a Gauss-Hermite series and it is shown that typical values of h_3 and h_4 observed in early-type galaxies can lead to significant modifications of the LOSVD corrections and thus to changes in the derived luminosity weighted ages and metallicities. A new, simple parameterisation for the LOSVD corrections, taking into account the h_3 and h_4 terms, is proposed and calibrations given for a subset of the Lick/IDS indices and two additional indices applicable to old (>3 Gyr) stellar populations.
Emerging single-photon-sensitive sensors combined with advanced inverse methods to process picosecond-accurate time-stamped photon counts have given rise to unprecedented imaging capabilities. Rather than imaging photons that travel along direct paths from a source to an object and back to the detector, non-line-of-sight (NLOS) imaging approaches analyse photons {scattered from multiple surfaces that travel} along indirect light paths to estimate 3D images of scenes outside the direct line of sight of a camera, hidden by a wall or other obstacles. Here we review recent advances in the field of NLOS imaging, discussing how to see around corners and future prospects for the field.
We present a grid-based non-parametric approach to obtain a triaxial three-dimensional luminosity density from its surface brightness distribution. Triaxial deprojection is highly degenerate and our approach illustrates the resulting difficulties. Fortunately, for massive elliptical galaxies, many deprojections for a particular line of sight can be discarded, because their projection along other lines of sight does not resemble elliptical galaxies. The near-elliptical isophotes of these objects imply near ellipsoidal intrinsic shapes. In fact, deprojection is unique for densities distributed on ellipsoidal shells. The constrained non-parametric deprojection method we present here relaxes this constraint and assumes that the contours of the luminosity density are boxy/discy ellipsoids with radially varying axis ratios. With this approach we are able to reconstruct the intrinsic triaxial densities of our test models, including one drawn from an $N$-body simulation. The method also allows to compare the relative likelihood of deprojections at different viewing angles. We show that the viewing orientations of individual galaxies with nearly ellipsoidal isophotal shapes can be constrained from photometric data alone.
Non-line-of-sight (NLOS) imaging is based on capturing the multi-bounce indirect reflections from the hidden objects. Active NLOS imaging systems rely on the capture of the time of flight of light through the scene, and have shown great promise for the accurate and robust reconstruction of hidden scenes without the need for specialized scene setups and prior assumptions. Despite that existing methods can reconstruct 3D geometries of the hidden scene with excellent depth resolution, accurately recovering object textures and appearance with high lateral resolution remains an challenging problem. In this work, we propose a new problem formulation, called NLOS photography, to specifically address this deficiency. Rather than performing an intermediate estimate of the 3D scene geometry, our method follows a data-driven approach and directly reconstructs 2D images of a NLOS scene that closely resemble the pictures taken with a conventional camera from the location of the relay wall. This formulation largely simplifies the challenging reconstruction problem by bypassing the explicit modeling of 3D geometry, and enables the learning of a deep model with a relatively small training dataset. The results are NLOS reconstructions of unprecedented lateral resolution and image quality.