Paper Reading AI Learner

Combining high contrast imaging and radial velocities to constrain the planetary architecture of nearby stars

2019-07-09 18:00:02
A. Boehle (1), S. P. Quanz (1), C. Lovis (2), D. Sègransan (2), S. Udry (2), D. Apai (3) ((1) ETH Zurich, (2) Observatoire Astronomique de l'Universitè de Genève, (3) University of Arizona)

Abstract

Nearby stars are prime targets for exoplanet searches and characterization using a variety of detection techniques. Combining constraints from the complementary detection methods of high contrast imaging (HCI) and radial velocity (RV) can further constrain the planetary architectures of these systems because these methods place limits at different regions of the companion mass and semi-major axis parameter space. We aim to constrain the planetary architectures from the combination of HCI and RV data for 6 nearby stars within 6 pc: $\tau$ Ceti, Kapteyn's star, AX Mic, 40 Eri, HD 36395, and HD 42581. We compiled the sample from stars with available archival VLT/NACO HCI data at L$^{\prime}$ band (3.8 $\mu$m). The NACO data were fully reanalyzed using the state-of-the-art direct imaging pipeline PynPoint and combined with RV data from HARPS, Keck/HIRES, and CORALIE. A Monte Carlo approach was used to assess the completeness in the companion mass/semi-major axis parameter space from the combination of the HCI and RV data sets. We find that the HCI data add significant information to the RV constraints, increasing the completeness for certain companions masses/semi-major axes by up to 68 - 99% for 4 of the 6 stars in our sample, and by up to 1 - 13% for the remaining stars. The improvements are strongest for intermediate semi-major axes (15 - 40 AU), corresponding to the semi-major axes of the ice giants in our own solar system. The HCI mass limits reach 5 - 20 $M_{\textrm{Jup}}$ in the background-limited regime, depending on the age of the star. Through the combination of HCI and RV data, we find that stringent constraints can be placed on the possible substellar companions in these systems. Applying these methods systematically to nearby stars will quantify our current knowledge of the planet population in the solar neighborhood and inform future observations.

Abstract (translated)

URL

https://arxiv.org/abs/1907.04334

PDF

https://arxiv.org/pdf/1907.04334


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Drone Dynamic_Memory_Network Edge_Detection Embedding Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot