A Statistical Analysis of SEEDS and Other High-Contrast Exoplanet Surveys: Massive Planets or Low-Mass Brown Dwarfs?

We conduct a statistical analysis of a combined sample of direct imaging data, totalling nearly 250 stars. The stars cover a wide range of ages and spectral types, and include five detections ($kappa$ And b, two $sim$60 M$_{rm J}$ brown dwarf companions in the Pleiades, PZ Tel B, and CD$-$35 2722B). For some analyses we add a currently unpublished set of SEEDS observations, including the detections GJ 504b and GJ 758B. We conduct a uniform, Bayesian analysis of all stellar ages using both membership in a kinematic moving group and activity/rotation age indicators. We then present a new statistical method for computing the likelihood of a substellar distribution function. By performing most of the integrals analytically, we achieve an enormous speedup over brute-force Monte Carlo. We use this method to place upper limits on the maximum semimajor axis of the distribution function derived from radial-velocity planets, finding model-dependent values of $sim$30--100 AU. Finally, we model the entire substellar sample, from massive brown dwarfs to a theoretically motivated cutoff at $sim$5 M$_{rm Jup}$, with a single power law distribution. We find that $p(M, a) propto M^{-0.65pm0.60} a^{-0.85pm0.39}$ (1$sigma$ errors) provides an adequate fit to our data, with 1.0--3.1% (68% confidence) of stars hosting 5--70 $M_{rm Jup}$ companions between 10 and 100 AU. This suggests that many of the directly imaged exoplanets known, including most (if not all) of the low-mass companions in our sample, formed by fragmentation in a cloud or disk, and represent the low-mass tail of the brown dwarfs.

The Moving Group Targets of the SEEDS High-Contrast Imaging Survey of Exoplanets and Disks: Results and Observations from the First Three Years

We present results from the first three years of observations of moving group targets in the SEEDS high-contrast imaging survey of exoplanets and disks using the Subaru telescope. We achieve typical contrasts of ~10^5 at 1 and ~10^6 beyond 2 around 63 proposed members of nearby kinematic moving groups. We review each of the kinematic associations to which our targets belong, concluding that five, beta Pictoris (~20 Myr), AB Doradus (~100 Myr), Columba (~30 Myr), Tucana-Horogium (~30 Myr), TW Hydrae (~10 Myr), are sufficiently well-defined to constrain the ages of individual targets. Somewhat less than half of our targets are high-probability members of one of these moving groups. For all of our targets, we combine proposed moving group membership with other age indicators where available, including Ca II HK emission, X-ray activity, and rotation period, to produce a posterior probability distribution of age. SEEDS observations discovered a substellar companion to one of our targets, kappa And, a late B star. We do not detect any other substellar companions, but do find seven new close binary systems, of which one still needs to be confirmed. A detailed analysis of the statistics of this sample, and of the companion mass constraints given our age probability distributions and exoplanet cooling models, will be presented in a forthcoming paper.

New Techniques for High-Contrast Imaging with ADI: the ACORNS-ADI SEEDS Data Reduction Pipeline

We describe Algorithms for Calibration, Optimized Registration, and Nulling the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized software package to reduce high-contrast imaging data, and its application to data from the SEEDS survey. We implement several new algorithms, including a method to register saturated images, a trimmed mean for combining an image sequence that reduces noise by up to ~20%, and a robust and computationally fast method to compute the sensitivity of a high-contrast observation everywhere on the field-of-view without introducing artificial sources. We also include a description of image processing steps to remove electronic artifacts specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed analysis of the Locally Optimized Combination of Images (LOCI) algorithm commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in python. It is efficient and open-source, and includes several optional features which may improve performance on data from other instruments. ACORNS-ADI requires minimal modification to reduce data from instruments other than HiCIAO. It is freely available for download at www.github.com/t-brandt/acorns-adi under a BSD license.