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We present a new matched filter algorithm for direct detection of point sources in the immediate vicinity of bright stars. The stellar Point Spread Function (PSF) is first subtracted using a Karhunen-Loeve Image Processing (KLIP) algorithm with Angular and Spectral Differential Imaging (ADI and SDI). The KLIP-induced distortion of the astrophysical signal is included in the matched filter template by computing a forward model of the PSF at every position in the image. To optimize the performance of the algorithm, we conduct extensive planet injection and recovery tests and tune the exoplanet spectra template and KLIP reduction aggressiveness to maximize the Signal-to-Noise Ratio (SNR) of the recovered planets. We show that only two spectral templates are necessary to recover any young Jovian exoplanets with minimal SNR loss. We also developed a complete pipeline for the automated detection of point source candidates, the calculation of Receiver Operating Characteristics (ROC), false positives based contrast curves, and completeness contours. We process in a uniform manner more than 330 datasets from the Gemini Planet Imager Exoplanet Survey (GPIES) and assess GPI typical sensitivity as a function of the star and the hypothetical companion spectral type. This work allows for the first time a comparison of different detection algorithms at a survey scale accounting for both planet completeness and false positive rate. We show that the new forward model matched filter allows the detection of $50%$ fainter objects than a conventional cross-correlation technique with a Gaussian PSF template for the same false positive rate.
The ESA-Ariel mission will include a tier dedicated to exoplanet phase curves corresponding to ~10% of the science time. We present here the current observing strategy for studying exoplanet phase curves with Ariel. We define science questions, requirements and a list of potential targets. We also estimate the precision of phase curve reconstruction and atmospheric retrieval using simulated phase curves. Based on this work, we found that full-orbit phase variations for 35-40 exoplanets could be observed during the 3.5-yr mission. This statistical sample would provide key constraints on atmospheric dynamics, composition, thermal structure and clouds of warm exoplanets, complementary to the scientific yield from spectroscopic transits/eclipses measurements.
Using high-resolution ground-based transmission spectroscopy to probe exoplanetary atmospheres is difficult due to the inherent telluric contamination from absorption in Earths atmosphere. A variety of methods have previously been used to remove telluric features in the optical regime and calculate the planetary transmission spectrum. In this paper we present and compare two such methods, specifically focusing on Na detections using high-resolution optical transmission spectra: (1) calculating the telluric absorption empirically based on the airmass, and (2) using a model of the Earths transmission spectrum. We test these methods on the transmission spectrum of the hot Jupiter HD 189733 b using archival data obtained with the HARPS spectrograph during three transits. Using models for Centre-to-Limb Variation and the Rossiter-McLaughlin effect, spurious signals which are imprinted within the transmission spectrum are reduced. We find that correcting tellurics with an atmospheric model of the Earth is more robust and produces consistent results when applied to data from different nights with changing atmospheric conditions. We confirm the detection of sodium in the atmosphere of HD 189733 b, with doublet line contrasts of -0.64 $pm$ 0.07 % (D2) and -0.53 $pm$ 0.07 % (D1). The average line contrast corresponds to an effective photosphere in the Na line located around 1.13 R$_p$. We also confirm an overall blueshift of the line centroids corresponding to net atmospheric eastward winds with a speed of 1.8 $pm$ 1.2 km/s. Our study highlights the importance of accurate telluric removal for consistent and reliable characterisation of exoplanetary atmospheres using high-resolution transmission spectroscopy.
I present RoadRunner, a fast exoplanet transit model that can use any radially symmetric function to model stellar limb darkening while still being faster to evaluate than the analytical transit model for quadratic limb darkening by Mandel & Agol (2002). CPU and GPU implementations of the model are available in the PyTransit transit modelling package, and come with platform-independent parallelisation, supersampling, and support for modelling complex heterogeneous time series. The code is written in numba-accelerated Python (and the GPU model in OpenCL) without C or Fortran dependencies, which allows for the limb darkening model to be given as any Python-callable function. Finally, as an example of the flexibility of the approach, the latest version of PyTransit comes with a numerical limb darkening model that uses LDTk-generated limb darkening profiles directly without approximating them by analytical models.
We present new observations of the planet beta Pictoris b from 2018 with GPI, the first GPI observations following conjunction. Based on these new measurements, we perform a joint orbit fit to the available relative astrometry from ground-based imaging, the Hipparcos Intermediate Astrometric Data (IAD), and the Gaia DR2 position, and demonstrate how to incorporate the IAD into direct imaging orbit fits. We find a mass consistent with predictions of hot-start evolutionary models and previous works following similar methods, though with larger uncertainties: 12.8 [+5.3, -3.2] M_Jup. Our eccentricity determination of 0.12 [+0.04, -0.03] disfavors circular orbits. We consider orbit fits to several different imaging datasets, and find generally similar posteriors on the mass for each combination of imaging data. Our analysis underscores the importance of performing joint fits to the absolute and relative astrometry simultaneously, given the strong covariance between orbital elements. Time of conjunction is well constrained within 2.8 days of 2017 September 13, with the star behind the planets Hill sphere between 2017 April 11 and 2018 February 16 (+/- 18 days). Following the recent radial velocity detection of a second planet in the system, beta Pic c, we perform additional two-planet fits combining relative astrometry, absolute astrometry, and stellar radial velocities. These joint fits find a significantly smaller mass for the imaged planet beta Pic b, of 8.0 +/- 2.6 M_Jup, in a somewhat more circular orbit. We expect future ground-based observations to further constrain the visual orbit and mass of the planet in advance of the release of Gaia DR4.
Vetting of exoplanet candidates in transit surveys is a manual process, which suffers from a large number of false positives and a lack of consistency. Previous work has shown that Convolutional Neural Networks (CNN) provide an efficient solution to these problems. Here, we apply a CNN to classify planet candidates from the Next Generation Transit Survey (NGTS). For training datasets we compare both real data with injected planetary transits and fully-simulated data, as well as how their different compositions affect network performance. We show that fewer hand labelled lightcurves can be utilised, while still achieving competitive results. With our best model, we achieve an AUC (area under the curve) score of $(95.6pm{0.2})%$ and an accuracy of $(88.5pm{0.3})%$ on our unseen test data, as well as $(76.5pm{0.4})%$ and $(74.6pm{1.1})%$ in comparison to our existing manual classifications. The neural network recovers 13 out of 14 confirmed planets observed by NGTS, with high probability. We use simulated data to show that the overall network performance is resilient to mislabelling of the training dataset, a problem that might arise due to unidentified, low signal-to-noise transits. Using a CNN, the time required for vetting can be reduced by half, while still recovering the vast majority of manually flagged candidates. In addition, we identify many new candidates with high probabilities which were not flagged by human vetters.