The transiting multi-planet system HD3167: a 5.7 MEarth Super-Earth and a 8.3 MEarth mini-Neptune

HD3167 is a bright (V=8.9 mag) K0V star observed by the NASA's K2 space mission during its Campaign 8. It has been recently found to host two small transiting planets, namely, HD3167b, an ultra short period (0.96 d) super-Earth, and HD3167c, a mini-Neptune on a relatively long-period orbit (29.85 d). Here we present an intensive radial velocity follow-up of HD3167 performed with the FIES@NOT, [email protected], and HARPS-N@TNG spectrographs. We revise the system parameters and determine radii, masses, and densities of the two transiting planets by combining the K2 photometry with our spectroscopic data. With a mass of 5.69+/-0.44 MEarth, radius of 1.574+/-0.054 REarth, and mean density of 8.00(+1.0)(-0.98) g/cm^3, HD3167b joins the small group of ultra-short period planets known to have a rocky terrestrial composition. HD3167c has a mass of 8.33 (+1.79)(-1.85) MEarth and a radius of 2.740(+0.106)(-0.100) REarth, yielding a mean density of 2.21(+0.56)(-0.53) g/cm^3, indicative of a planet with a composition comprising a solid core surrounded by a thick atmospheric envelope. The rather large pressure scale height (about 350 km) and the brightness of the host star make HD3167c an ideal target for atmospheric characterization via transmission spectroscopy across a broad range of wavelengths. We found evidence of additional signals in the radial velocity measurements but the currently available data set does not allow us to draw any firm conclusion on the origin of the observed variation.Comment: 18 pages, 11 figures, 5 table

How to Directly Image a Habitable Planet Around Alpha Centauri with a ~30-45cm Space Telescope

Several mission concepts are being studied to directly image planets around nearby stars. It is commonly thought that directly imaging a potentially habitable exoplanet around a Sun-like star requires space telescopes with apertures of at least 1m. A notable exception to this is Alpha Centauri (A and B), which is an extreme outlier among FGKM stars in terms of apparent habitable zone size: the habitable zones are ~3x wider in apparent size than around any other FGKM star. This enables a ~30-45cm visible light space telescope equipped with a modern high performance coronagraph or starshade to resolve the habitable zone at high contrast and directly image any potentially habitable planet that may exist in the system. We presents a brief analysis of the astrophysical and technical challenges involved with direct imaging of Alpha Centauri with a small telescope and describe two new technologies that address some of the key technical challenges. In particular, the raw contrast requirements for such an instrument can be relaxed to 1e-8 if the mission spends 2 years collecting tens of thousands of images on the same target, enabling a factor of 500-1000 speckle suppression in post processing using a new technique called Orbital Difference Imaging (ODI). The raw light leak from both stars is controllable with a special wavefront control algorithm known as Multi-Star Wavefront Control (MSWC), which independently suppresses diffraction and aberrations from both stars using independent modes on the deformable mirror. We also show an example of a small coronagraphic mission concept to take advantage of this opportunity.Comment: 12 pages, 8 figures, 1 table, to appear in Proc. SPIE 9605. See other ACESat papers by Bendek, Males, and Thoma

Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms ($L1$ Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks for different constraints are used as basis functions to encode the observed functional activity at a given time point. These encodings are decoded using machine learning to compare both the algorithms and their assumptions, using the time series weights to predict whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. For classifying cognitive activity, the sparse coding algorithm of $L1$ Regularized Learning consistently outperformed 4 variations of ICA across different numbers of networks and noise levels (p$<$0.001). The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy. Within each algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p$<$0.001). The success of sparse coding algorithms may suggest that algorithms which enforce sparse coding, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA

Theoretical Reflectance Spectra of Earth-Like Planets through Their Evolutions: Impact of Clouds on the Detectability of Oxygen, Water, and Methane with Future Direct Imaging Missions

In the near-future, atmospheric characterization of Earth-like planets in the habitable zone will become possible via reflectance spectroscopy with future telescopes such as the proposed LUVOIR and HabEx missions. While previous studies have considered the effect of clouds on the reflectance spectra of Earth-like planets, the molecular detectability considering a wide range of cloud properties has not been previously explored in detail. In this study, we explore the effect of cloud altitude and coverage on the reflectance spectra of Earth-like planets at different geological epochs and examine the detectability of $\mathrm{O_2}$, $\mathrm{H_2O}$, and $\mathrm{CH_4}$ with test parameters for the future mission concept, LUVOIR, using a coronagraph noise simulator previously designed for WFIRST-AFTA. Considering an Earth-like planet located at 5 pc away, we have found that for the proposed LUVOIR telescope, the detection of the $\mathrm{O_2}$ A-band feature (0.76 $\mathrm{\mu}$m) will take approximately 100, 30, and 10 hours for the majority of the cloud parameter space modeled for the atmospheres with 10\%, 50\%, and 100\% of modern Earth O$_2$ abundances, respectively. Especially, for {the case of $\gtrsim 50$\%} of modern Earth O$_2$ abundance, the feature will be detectable with integration time $\lesssim 10$ hours as long as there are lower altitude ($\lesssim 8$ km) clouds with a global coverage of $\gtrsim 20\%$. For the 1\% of modern Earth $\mathrm{O_2}$ abundance case, however, it will take more than 100 hours for all the cloud parameters we modeled.Comment: 16 pages, 10 figures, accepted for publication in A

DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation

This paper considers the task of articulated human pose estimation of multiple people in real world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other. This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation. Models and code available at http://pose.mpi-inf.mpg.de.Comment: Accepted at IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016