The Gemini Planet Imager Exoplanet Survey: Giant Planet and Brown Dwarf Demographics From 10-100 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 $M_\odot$ more likely to host planets with masses between 2-13 M$_{\rm Jup}$ 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$_\odot$) of the form $\frac{d^2 N}{dm da} \propto m^\alpha a^\beta$, finding $\alpha$ = -2.4 $\pm$ 0.8 and $\beta$ = -2.0 $\pm$ 0.5, and an integrated occurrence rate of $9^{+5}_{-4}$% between 5-13 M$_{\rm Jup}$ and 10-100 au. A significantly lower occurrence rate is obtained for brown dwarfs around all stars, with 0.8$^{+0.8}_{-0.5}$% of stars hosting a brown dwarf companion between 13-80 M$_{\rm Jup}$ 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.Comment: 52 pages, 18 figures. AJ in pres

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. 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, semimajor axis, and host stellar mass. We uncover a strong correlation between planet occurrence rate and host star mass, with stars M ∗ \u3e1.5 M o more likely to host planets with masses between 2 and 13M Jup and semimajor axes of 3-100 au at 99.92% confidence. We fit a double power-law model in planet mass (m) and semimajor axis (a) for planet populations around high-mass stars (M ∗ \u3e1.5 M o) of the form , finding α = -2.4 +0.8 and β = -2.0 +0.5, and an integrated occurrence rate of % between 5-13M Jup and 10-100 au. A significantly lower occurrence rate is obtained for brown dwarfs around all stars, with % of stars hosting a brown dwarf companion between 13-80M Jup and 10-100 au. Brown dwarfs also appear to be distributed differently in mass and semimajor axis compared to giant planets; whereas giant planets follow a bottom-heavy mass distribution and favor smaller semimajor axes, brown dwarfs exhibit just the opposite behaviors. Comparing to studies of short-period giant planets from the radial velocity method, our results are consistent with a peak in occurrence of giant planets between ∼1 and 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

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. 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, semimajor axis, and host stellar mass. We uncover a strong correlation between planet occurrence rate and host star mass, with stars M* > 1.5 M⊙ more likely to host planets with masses between 2 and 13MJup and semimajor axes of 3–100 au at 99.92% confidence. We fit a double power-law model in planet mass (m) and semimajor axis (a) for planet populations around high-mass stars (M* > 1.5 M⊙) of the form d2N/(dm da) ∝ mα aβ, finding α = −2.4 ± 0.8 and β = −2.0 ± 0.5, and an integrated occurrence rate of 9+5-4% between 5–13MJup and 10–100 au. A significantly lower occurrence rate is obtained for brown dwarfs around all stars, with 0.8+0.8-0.5% of stars hosting a brown dwarf companion between 13–80MJup and 10–100 au. Brown dwarfs also appear to be distributed differently in mass and semimajor axis compared to giant planets; whereas giant planets follow a bottom-heavy mass distribution and favor smaller semimajor axes, brown dwarfs exhibit just the opposite behaviors. Comparing to studies of short-period giant planets from the radial velocity method, our results are consistent with a peak in occurrence of giant planets between ∼1 and 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.Peer reviewe

Open-loop control demonstration of micro-electro-mechanical-system MEMS deformable mirror

New astronomical challenges revolve around the observation of faint galaxies, nearby star-forming regions and the direct imaging of exoplanets. The technologies required to progress in these fields of research rely on the development of custom Adaptive Optics (AO) instruments such as Multi-Object AO (MOAO) or Extreme AO (ExAO). Many obstacles remain in the development of these new technologies. A major barrier to the implementation of MOAO is the utilisation of deformable mirrors (DMs) in an open-loop control system. Micro-Electro-Mechanical-System (MEMS) DMs show promise for application in both MOAO and ExAO. Despite recent encouraging laboratory results, it remains an immature technology which has yet to be demonstrated on a fully operational on-sky AO system. Much of the research in this area focuses on the development of an accurate model of the MEMS DMs. In this paper, a thorough characterization process of a MEMS DM is performed, with the goal of developing an open-loop control strategy free of computationally heavy modelling (such as the use of plate equations). Instead, a simpler approach, based on the additivity of the influence functions, is chosen. The actuator stroke-voltage relationship and the actuator influence functions are carefully calibrated. For 100 initial phase screens with a mean rms of 97 nm (computer generated following a Von Karman statistic), the resulting mean residual open-loop rms error is 16.5 nm, the mean fitting error rms is 13.3 nm and the mean DM error rms is 10.8 nm (error reflecting the performances of the model under test in this paper). This corresponds to 11% of residual DM error

On the performance of three indices of agreement: an easy-to-use r-code for calculating the Willmott indices

<div><p>ABSTRACT A key step for any modeling study is to compare model-produced estimates with observed/reliable data. The original index of agreement (also known as original Willmott index) has been widely used to measure how well model-produced estimates simulate observed data. However, in its original version such index may lead the user to erroneously select a predicting model. Therefore, this study compared the sensibility of the original index of agreement with its two newer versions (modified and refined) and provided an easy-to-use R-code capable of calculating these three indices. First, the sensibility of the indices was evaluated through Monte Carlo Experiments. These controlled simulations considered different sorts of errors (systematic, random and systematic + random) and errors magnitude. By using the R-code, we also carried out a case of study in which the indices are expected to indicate that th empirical Thornthwaite’s model produces poor estimates of daily reference evapotranspiration in respect to the standard method Penman-Monteith (FAO56). Our findings indicate that the original index of agreement may indeed erroneously select a predicting model performing poorly. Our results also indicate that the newer versions of this index overcome such problem, producing more rigorous evaluations. Although the refined Willmott index presents the broadest range of possible values, it does not inform the user if a predicting model overestimate or underestimate the simulated data, resulting in no extra information regarding those already provided by the modified version. None of the indices represents the error as linear functions of its magnitude in respect to the observed process.</p></div

Impacts of climate change on drought: changes to drier conditions at the beginning of the crop growing season in southern Brazil

<div><p>ABSTRACT The intensification of drought incidence is one of the most important threats of the 21st century with significant effects on food security. Accordingly, there is a need to improve the understanding of the regional impacts of climate change on this hazard. This study assessed long-term trends in probability-based drought indices (Standardized Precipitation Index and Standardized Evapotranspiration Index) in the State of São Paulo, Brazil. Owing to the multi-scalar nature of both indices, the analyses were performed at 1 to 12-month time scales. The indices were calculated by means of a relativist approach that allowed us to compare drought conditions from different periods. The years 1961-1990 were used as the referential period. To the authors’ best knowledge, this is the first time that such relativist approach is used in historical trend analysis. The results suggest that the evapotranspiration rates have intensified the regional drought conditions. The time scale used to calculate the indices significantly affected the outcomes of drought trend assessments. The reason behind this feature is that the significant changes in the monthly regional patterns are limited to a specific period of the year. More specifically, virtually all significant changes have been observed during the first trimester of the rainy season (October, November and December). Considering that this period corresponds to critical plant growth stages (flowering/regrowth/sprouting) of several major crops (e.g. Sugarcane and Citrus), we may conclude that these significant changes have increased the risk of crop yield reductions due to agricultural drought.</p></div