Calibrating and Stabilizing Spectropolarimeters with Charge Shuffling and Daytime Sky Measurements

Well-calibrated spectropolarimetry studies at resolutions of $R>$10,000 with signal-to-noise ratios (SNRs) better than 0.01\% across individual line profiles, are becoming common with larger aperture telescopes. Spectropolarimetric studies require high SNR observations and are often limited by instrument systematic errors. As an example, fiber-fed spectropolarimeters combined with advanced line-combination algorithms can reach statistical error limits of 0.001\% in measurements of spectral line profiles referenced to the continuum. Calibration of such observations is often required both for cross-talk and for continuum polarization. This is not straightforward since telescope cross-talk errors are rarely less than $\sim$1\%. In solar instruments like the Daniel K. Inouye Solar Telescope (DKIST), much more stringent calibration is required and the telescope optical design contains substantial intrinsic polarization artifacts. This paper describes some generally useful techniques we have applied to the HiVIS spectropolarimeter at the 3.7m AEOS telescope on Haleakala. HiVIS now yields accurate polarized spectral line profiles that are shot-noise limited to 0.01\% SNR levels at our full spectral resolution of 10,000 at spectral sampling of $\sim$100,000. We show line profiles with absolute spectropolarimetric calibration for cross-talk and continuum polarization in a system with polarization cross-talk levels of essentially 100\%. In these data the continuum polarization can be recovered to one percent accuracy because of synchronized charge-shuffling model now working with our CCD detector. These techniques can be applied to other spectropolarimeters on other telescopes for both night and day-time applications such as DKIST, TMT and ELT which have folded non-axially symmetric foci.Comment: Accepted to A&

LASR-Guided Stellar Photometric Variability Subtraction: The Linear Algorithm For Significance Reduction

We develop a technique for removing stellar variability in the light curves of $\delta$-Scuti and similar stars. Our technique, which we name the Linear Algorithm for Significance Reduction (LASR), subtracts oscillations from a time series by minimizing their statistical significance in frequency space. We demonstrate that LASR can subtract variable signals of near-arbitrary complexity and can robustly handle close frequency pairs and overtone frequencies. We demonstrate that our algorithm performs an equivalent fit as prewhitening to the straightforward variable signal of KIC 9700322. We also show that LASR provides a better fit to seismic activity than prewhitening in the case of the complex $\delta$-Scuti KOI-976.Comment: 9 pages, 5 figures, accepted for publication in Astronomy & Astrophysics. Pseudocode and github link to code included in manuscrip

D3^3PO - Denoising, Deconvolving, and Decomposing Photon Observations

The analysis of astronomical images is a non-trivial task. The D3PO algorithm addresses the inference problem of denoising, deconvolving, and decomposing photon observations. Its primary goal is the simultaneous but individual reconstruction of the diffuse and point-like photon flux given a single photon count image, where the fluxes are superimposed. In order to discriminate between these morphologically different signal components, a probabilistic algorithm is derived in the language of information field theory based on a hierarchical Bayesian parameter model. The signal inference exploits prior information on the spatial correlation structure of the diffuse component and the brightness distribution of the spatially uncorrelated point-like sources. A maximum a posteriori solution and a solution minimizing the Gibbs free energy of the inference problem using variational Bayesian methods are discussed. Since the derivation of the solution is not dependent on the underlying position space, the implementation of the D3PO algorithm uses the NIFTY package to ensure applicability to various spatial grids and at any resolution. The fidelity of the algorithm is validated by the analysis of simulated data, including a realistic high energy photon count image showing a 32 x 32 arcmin^2 observation with a spatial resolution of 0.1 arcmin. In all tests the D3PO algorithm successfully denoised, deconvolved, and decomposed the data into a diffuse and a point-like signal estimate for the respective photon flux components.Comment: 22 pages, 8 figures, 2 tables, accepted by Astronomy & Astrophysics; refereed version, 1 figure added, results unchanged, software available at http://www.mpa-garching.mpg.de/ift/d3po

ADAM: a general method for using various data types in asteroid reconstruction

We introduce ADAM, the All-Data Asteroid Modelling algorithm. ADAM is simple and universal since it handles all disk-resolved data types (adaptive optics or other images, interferometry, and range-Doppler radar data) in a uniform manner via the 2D Fourier transform, enabling fast convergence in model optimization. The resolved data can be combined with disk-integrated data (photometry). In the reconstruction process, the difference between each data type is only a few code lines defining the particular generalized projection from 3D onto a 2D image plane. Occultation timings can be included as sparse silhouettes, and thermal infrared data are efficiently handled with an approximate algorithm that is sufficient in practice due to the dominance of the high-contrast (boundary) pixels over the low-contrast (interior) ones. This is of particular importance to the raw ALMA data that can be directly handled by ADAM without having to construct the standard image. We study the reliability of the inversion by using the independent shape supports of function series and control-point surfaces. When other data are lacking, one can carry out fast nonconvex lightcurve-only inversion, but any shape models resulting from it should only be taken as illustrative global-scale ones.Comment: 11 pages, submitted to A&

HOL(y)Hammer: Online ATP Service for HOL Light

HOL(y)Hammer is an online AI/ATP service for formal (computer-understandable) mathematics encoded in the HOL Light system. The service allows its users to upload and automatically process an arbitrary formal development (project) based on HOL Light, and to attack arbitrary conjectures that use the concepts defined in some of the uploaded projects. For that, the service uses several automated reasoning systems combined with several premise selection methods trained on all the project proofs. The projects that are readily available on the server for such query answering include the recent versions of the Flyspeck, Multivariate Analysis and Complex Analysis libraries. The service runs on a 48-CPU server, currently employing in parallel for each task 7 AI/ATP combinations and 4 decision procedures that contribute to its overall performance. The system is also available for local installation by interested users, who can customize it for their own proof development. An Emacs interface allowing parallel asynchronous queries to the service is also provided. The overall structure of the service is outlined, problems that arise and their solutions are discussed, and an initial account of using the system is given

Using 3D Voronoi grids in radiative transfer simulations

Probing the structure of complex astrophysical objects requires effective three-dimensional (3D) numerical simulation of the relevant radiative transfer (RT) processes. As with any numerical simulation code, the choice of an appropriate discretization is crucial. Adaptive grids with cuboidal cells such as octrees have proven very popular, however several recently introduced hydrodynamical and RT codes are based on a Voronoi tessellation of the spatial domain. Such an unstructured grid poses new challenges in laying down the rays (straight paths) needed in RT codes. We show that it is straightforward to implement accurate and efficient RT on 3D Voronoi grids. We present a method for computing straight paths between two arbitrary points through a 3D Voronoi grid in the context of a RT code. We implement such a grid in our RT code SKIRT, using the open source library Voro++ to obtain the relevant properties of the Voronoi grid cells based solely on the generating points. We compare the results obtained through the Voronoi grid with those generated by an octree grid for two synthetic models, and we perform the well-known Pascucci RT benchmark using the Voronoi grid. The presented algorithm produces correct results for our test models. Shooting photon packages through the geometrically much more complex 3D Voronoi grid is only about three times slower than the equivalent process in an octree grid with the same number of cells, while in fact the total number of Voronoi grid cells may be lower for an equally good representation of the density field. We conclude that the benefits of using a Voronoi grid in RT simulation codes will often outweigh the somewhat slower performance.Comment: 9 pages, 7 figures, accepted by A