23 research outputs found
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The Gemini Planet Imager Exoplanet Survey: Giant Planet and Brown Dwarf Demographics from 10 to 100 au
We present a statistical analysis of the first 300 stars observed by the
Gemini Planet Imager Exoplanet Survey (GPIES). This subsample includes six
detected planets and three brown dwarfs; from these detections and our contrast
curves we infer the underlying distributions of substellar companions with
respect to their mass, semi-major axis, and host stellar mass. We uncover a
strong correlation between planet occurrence rate and host star mass, with
stars M 1.5 more likely to host planets with masses between 2-13
M and semi-major axes of 3-100 au at 99.92% confidence. We fit a
double power-law model in planet mass (m) and semi-major axis (a) for planet
populations around high-mass stars (M 1.5M) of the form , finding = -2.4 0.8 and
= -2.0 0.5, and an integrated occurrence rate of %
between 5-13 M and 10-100 au. A significantly lower occurrence rate
is obtained for brown dwarfs around all stars, with 0.8% of
stars hosting a brown dwarf companion between 13-80 M and 10-100
au. Brown dwarfs also appear to be distributed differently in mass and
semi-major axis compared to giant planets; whereas giant planets follow a
bottom-heavy mass distribution and favor smaller semi-major axes, brown dwarfs
exhibit just the opposite behaviors. Comparing to studies of short-period giant
planets from the RV method, our results are consistent with a peak in
occurrence of giant planets between ~1-10 au. We discuss how these trends,
including the preference of giant planets for high-mass host stars, point to
formation of giant planets by core/pebble accretion, and formation of brown
dwarfs by gravitational instability
Recommended from our members
The Gemini Planet Imager Exoplanet Survey: Giant Planet and Brown Dwarf Demographics from 10 to 100 au
We present a statistical analysis of the first 300 stars observed by the
Gemini Planet Imager Exoplanet Survey (GPIES). This subsample includes six
detected planets and three brown dwarfs; from these detections and our contrast
curves we infer the underlying distributions of substellar companions with
respect to their mass, semi-major axis, and host stellar mass. We uncover a
strong correlation between planet occurrence rate and host star mass, with
stars M 1.5 more likely to host planets with masses between 2-13
M and semi-major axes of 3-100 au at 99.92% confidence. We fit a
double power-law model in planet mass (m) and semi-major axis (a) for planet
populations around high-mass stars (M 1.5M) of the form , finding = -2.4 0.8 and
= -2.0 0.5, and an integrated occurrence rate of %
between 5-13 M and 10-100 au. A significantly lower occurrence rate
is obtained for brown dwarfs around all stars, with 0.8% of
stars hosting a brown dwarf companion between 13-80 M and 10-100
au. Brown dwarfs also appear to be distributed differently in mass and
semi-major axis compared to giant planets; whereas giant planets follow a
bottom-heavy mass distribution and favor smaller semi-major axes, brown dwarfs
exhibit just the opposite behaviors. Comparing to studies of short-period giant
planets from the RV method, our results are consistent with a peak in
occurrence of giant planets between ~1-10 au. We discuss how these trends,
including the preference of giant planets for high-mass host stars, point to
formation of giant planets by core/pebble accretion, and formation of brown
dwarfs by gravitational instability
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Deep Exploration of Eridani with Keck Ms-band Vortex Coronagraphy and Radial Velocities: Mass and Orbital Parameters of the Giant Exoplanet
We present the most sensitive direct imaging and radial velocity (RV) exploration of Eridani to date. Eridani is an adolescent planetary system, reminiscent of the early solar system. It is surrounded by a prominent and complex debris disk that is likely stirred by one or several gas giant exoplanets. The discovery of the RV signature of a giant exoplanet was announced 15 yr ago, but has met with scrutiny due to possible confusion with stellar noise. We confirm the planet with a new compilation and analysis of precise RV data spanning 30 yr, and combine it with upper limits from our direct imaging search, the most sensitive ever performed. The deep images were taken in the Ms band (4.7 Όm) with the vortex coronagraph recently installed in W.M. Keck Observatory's infrared camera NIRC2, which opens a sensitive window for planet searches around nearby adolescent systems. The RV data and direct imaging upper limit maps were combined in an innovative joint Bayesian analysis, providing new constraints on the mass and orbital parameters of the elusive planet. Eridani b has a mass of M Jup and is orbiting Eridani at about 3.48 ± 0.02 au with a period of 7.37 ± 0.07 yr. The eccentricity of Eridani b's orbit is an order of magnitude smaller than early estimates and consistent with a circular orbit. We discuss our findings from the standpoint of planet-disk interactions and prospects for future detection and characterization with the James Webb Space Telescope
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Improving and Assessing Planet Sensitivity of the GPI Exoplanet Survey with a Forward Model Matched Filter
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-Loéve 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 (S/N) of the recovered planets. We show that only two spectral templates are necessary to recover any young Jovian exoplanets with minimal S/N loss. We also developed a complete pipeline for the automated detection of point-source candidates, the calculation of receiver operating characteristics (ROC), contrast curves based on false positives, and completeness contours. We process in a uniform manner more than 330 data sets from the Gemini Planet Imager Exoplanet Survey 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
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GPI Spectra of HR 8799 c, d, and e from 1.5 to 2.4 ÎŒm with KLIP Forward Modeling
We explore KLIP forward modeling spectral extraction on Gemini Planet Imager coronagraphic data of HR 8799, using PyKLIP, and show algorithm stability with varying KLIP parameters. We report new and re-reduced spectrophotometry of HR 8799 c, d, and e in the H and K bands. We discuss a strategy for choosing optimal KLIP PSF subtraction parameters by injecting simulated sources and recovering them over a range of parameters. The K1/K2 spectra for HR 8799 c and d are similar to previously published results from the same data set. We also present a K-band spectrum of HR 8799 e for the first time and show that our H-band spectra agree well with previously published spectra from the VLT/SPHERE instrument. We show that HR 8799 c and d show significant differences in their H and K spectra, but do not find any conclusive differences between d and e, nor between c and e, likely due to large error bars in the recovered spectrum of e. Compared to M-, L-, and T-type field brown dwarfs, all three planets are most consistent with mid- and late-L spectral types. All objects are consistent with low gravity, but a lack of standard spectra for low gravity limit the ability to fit the best spectral type. We discuss how dedicated modeling efforts can better fit HR 8799 planets' near-IR flux, as well as how differences between the properties of these planets can be further explored
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Automated data processing architecture for the gemini planet imager exoplanet survey
The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-Time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance
Recommended from our members
Automated data processing architecture for the gemini planet imager exoplanet survey
The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-Time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance