High dynamic-range imagers aim to block out or null light from a very bright primary star to make it possible to detect and measure far fainter companions; in real systems a small fraction of the primary light is scattered, diffracted, and unocculted
. We introduce S4, a flexible data-driven model for the unocculted (and highly speckled) light in the P1640 spectroscopic coronograph. The model uses Principal Components Analysis (PCA) to capture the spatial structure and wavelength dependence of the speckles but not the signal produced by any companion. Consequently, the residual typically includes the companion signal. The companion can thus be found by filtering this error signal with a fixed companion model. The approach is sensitive to companions that are of order a percent of the brightness of the speckles, or up to $10^{-7}$ times the brightness of the primary star. This outperforms existing methods by a factor of 2-3 and is close to the shot-noise physical limit.
Keplers immense photometric precision to date was maintained through satellite stability and precise pointing. In this white paper, we argue that image modeling--fitting the Kepler-downlinked raw pixel data--can vastly improve the precision of Kepler
in pointing-degraded two-wheel mode. We argue that a non-trivial modeling effort may permit continuance of photometry at 10-ppm-level precision. We demonstrate some baby steps towards precise models in both data-driven (flexible) and physics-driven (interpretably parameterized) modes. We demonstrate that the expected drift or jitter in positions in the two-weel era will help with constraining calibration parameters. In particular, we show that we can infer the device flat-field at higher than pixel resolution; that is, we can infer pixel-to-pixel variations in intra-pixel sensitivity. These results are relevant to almost any scientific goal for the repurposed mission; image modeling ought to be a part of any two-wheel repurpose for the satellite. We make other recommendations for Kepler operations, but fundamentally advocate that the project stick with its core mission of finding and characterizing Earth analogs. [abridged]
We present the SDSS-XDQSO quasar targeting catalog for efficient flux-based quasar target selection down to the faint limit of the Sloan Digital Sky Survey (SDSS) catalog, even at medium redshifts (2.5 <~ z <~ 3) where the stellar contamination is si
gnificant. We build models of the distributions of stars and quasars in flux space down to the flux limit by applying the extreme-deconvolution method to estimate the underlying density. We convolve this density with the flux uncertainties when evaluating the probability that an object is a quasar. This approach results in a targeting algorithm that is more principled, more efficient, and faster than other similar methods. We apply the algorithm to derive low-redshift (z < 2.2), medium-redshift (2.2 <= z <= 3.5), and high-redshift (z > 3.5) quasar probabilities for all 160,904,060 point sources with dereddened i-band magnitude between 17.75 and 22.45 mag in the 14,555 deg^2 of imaging from SDSS Data Release 8. The catalog can be used to define a uniformly selected and efficient low- or medium-redshift quasar survey, such as that needed for the SDSS-IIIs Baryon Oscillation Spectroscopic Survey project. We show that the XDQSO technique performs as well as the current best photometric quasar-selection technique at low redshift, and outperforms all other flux-based methods for selecting the medium-redshift quasars of our primary interest. We make code to reproduce the XDQSO quasar target selection publicly available.
The near future of astrophysics involves many large solid-angle, multi-epoch, multi-band imaging surveys. These surveys will, at their faint limits, have data on large numbers of sources that are too faint to be detected at any individual epoch. Here
we show that it is possible to measure in multi-epoch data not only the fluxes and positions, but also the parallaxes and proper motions of sources that are too faint to be detected at any individual epoch. The method involves fitting a model of a moving point source simultaneously to all imaging, taking account of the noise and point-spread function in each image. By this method it is possible to measure the proper motion of a point source with an uncertainty close to the minimum possible uncertainty given the information in the data, which is limited by the point-spread function, the distribution of observation times (epochs), and the total signal-to-noise in the combined data. We demonstrate our technique on multi-epoch Sloan Digital Sky Survey imaging of the SDSS Southern Stripe. We show that we can distinguish very red brown dwarfs by their proper motions from very high-redshift quasars more than $1.6mag$ fainter than with traditional technique on these SDSS data, and with better better fidelity than by multi-band imaging alone. We re-discover all 10 known brown dwarfs in our sample and present 9 new candidate brown dwarfs, identified on the basis of high proper motion.
