Direct exoplanet detection and characterization using the ANDROMEDA method: Performance on VLT/NaCo data

Context. The direct detection of exoplanets with high-contrast imaging requires advanced data processing methods to disentangle potential planetary signals from bright quasi-static speckles. Among them, angular differential imaging (ADI) permits potential planetary signals with a known rotation rate to be separated from instrumental speckles that are either statics or slowly variable. The method presented in this paper, called ANDROMEDA for ANgular Differential OptiMal Exoplanet Detection Algorithm is based on a maximum likelihood approach to ADI and is used to estimate the position and the flux of any point source present in the field of view. Aims. In order to optimize and experimentally validate this previously proposed method, we applied ANDROMEDA to real VLT/NaCo data. In addition to its pure detection capability, we investigated the possibility of defining simple and efficient criteria for automatic point source extraction able to support the processing of large surveys. Methods. To assess the performance of the method, we applied ANDROMEDA on VLT/NaCo data of TYC-8979-1683-1 which is surrounded by numerous bright stars and on which we added synthetic planets of known position and flux in the field. In order to accommodate the real data properties, it was necessary to develop additional pre-processing and post-processing steps to the initially proposed algorithm. We then investigated its skill in the challenging case of a well-known target, $\beta$ Pictoris, whose companion is close to the detection limit and we compared our results to those obtained by another method based on principal component analysis (PCA). Results. Application on VLT/NaCo data demonstrates the ability of ANDROMEDA to automatically detect and characterize point sources present in the image field. We end up with a robust method bringing consistent results with a sensitivity similar to the recently published algorithms, with only two parameters to be fine tuned. Moreover, the companion flux estimates are not biased by the algorithm parameters and do not require a posteriori corrections. Conclusions. ANDROMEDA is an attractive alternative to current standard image processing methods that can be readily applied to on-sky data

The Diversity of Diffuse Lyα\alpha Nebulae around Star-Forming Galaxies at High Redshift

We report the detection of diffuse Ly$\alpha$ emission, or Ly$\alpha$ halos (LAHs), around star-forming galaxies at $z\approx3.78$ and $2.66$ in the NOAO Deep Wide-Field Survey Bo\"otes field. Our samples consist of a total of $\sim$1400 galaxies, within two separate regions containing spectroscopically confirmed galaxy overdensities. They provide a unique opportunity to investigate how the LAH characteristics vary with host galaxy large-scale environment and physical properties. We stack Ly$\alpha$ images of different samples defined by these properties and measure their median LAH sizes by decomposing the stacked Ly$\alpha$ radial profile into a compact galaxy-like and an extended halo-like component. We find that the exponential scale-length of LAHs depends on UV continuum and Ly$\alpha$ luminosities, but not on Ly$\alpha$ equivalent widths or galaxy overdensity parameters. The full samples, which are dominated by low UV-continuum luminosity Ly$\alpha$ emitters ($M_{\rm UV} \gtrsim -21$), exhibit LAH sizes of 5$\,-\,6\,$kpc. However, the most UV- or Ly$\alpha$-luminous galaxies have more extended halos with scale-lengths of 7$\,-\,9\,$kpc. The stacked Ly$\alpha$ radial profiles decline more steeply than recent theoretical predictions that include the contributions from gravitational cooling of infalling gas and from low-level star formation in satellites. On the other hand, the LAH extent matches what one would expect for photons produced in the galaxy and then resonantly scattered by gas in an outflowing envelope. The observed trends of LAH sizes with host galaxy properties suggest that the physical conditions of the circumgalactic medium (covering fraction, HI column density, and outflow velocity) change with halo mass and/or star-formation rates.Comment: published in ApJ, minor proof corrections applie

Siamese Instance Search for Tracking

In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-the-art tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos. The presented tracker simply matches the initial patch of the target in the first frame with candidates in a new frame and returns the most similar patch by a learned matching function. The strength of the matching function comes from being extensively trained generically, i.e., without any data of the target, using a Siamese deep neural network, which we design for tracking. Once learned, the matching function is used as is, without any adapting, to track previously unseen targets. It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, which only uses the original observation of the target from the first frame, suffices to reach state-of-the-art performance. Further, we show the proposed tracker even allows for target re-identification after the target was absent for a complete video shot.Comment: This paper is accepted to the IEEE Conference on Computer Vision and Pattern Recognition, 201

A survey of young, nearby, and dusty stars to understand the formation of wide-orbit giant planets

Direct imaging has confirmed the existence of substellar companions on wide orbits. To understand the formation and evolution mechanisms of these companions, the full population properties must be characterized. We aim at detecting giant planet and/or brown dwarf companions around young, nearby, and dusty stars. Our goal is also to provide statistics on the population of giant planets at wide-orbits and discuss planet formation models. We report a deep survey of 59 stars, members of young stellar associations. The observations were conducted with VLT/NaCo at L'-band (3.8 micron). We used angular differential imaging to reach optimal detection performance. A statistical analysis of about 60 % of the young and southern A-F stars closer than 65 pc allows us to derive the fraction of giant planets on wide orbits. We use gravitational instability models and planet population synthesis models following the core-accretion scenario to discuss the occurrence of these companions. We resolve and characterize new visual binaries and do not detect any new substellar companion. The survey's median detection performance reaches contrasts of 10 mag at 0.5as and 11.5 mag at 1as. We find the occurrence of planets to be between 10.8-24.8 % at 68 % confidence level assuming a uniform distribution of planets in the interval 1-13 Mj and 1-1000 AU. Considering the predictions of formation models, we set important constraints on the occurrence of massive planets and brown dwarf companions that would have formed by GI. We show that this mechanism favors the formation of rather massive clump (Mclump > 30 Mj) at wide (a > 40 AU) orbits which might evolve dynamically and/or fragment. For the population of close-in giant planets that would have formed by CA, our survey marginally explore physical separations (<20 AU) and cannot constrain this population

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available