Contrast Limits with the Simultaneous Differential Extrasolar Planet Imager (SDI) at the VLT and MMT

Beth A. Biller,Laird M. Close,Elena Masciadri,Rainer Lenzen,Wolfgang Brandner,Donald McCarthy,Thomas Henning,Eric L. Nielsen,Markus Hartung,Stephan Kellner,Kerstin Geissler,Markus Kasper

Proceedings of SPIE--the International Society for Optical Engineering（2006）

Univ Arizona

Cited 23|Views7

Abstract

We discuss contrast limits obtained during a survey of young (<300 Myr), close (<50 pc) stars with the Simultaneous Differential Extrasolar Planet Imager (SDI) implemented at the VLT and the MMT. SDI uses a double Wollaston prism and a quad filter to take images simultaneously at 3 wavelengths surrounding the 1.62 μm methane bandhead found in the spectrum of cool brown dwarfs and gas giants. By performing a difference of images in these filters, speckle noise from the primary can be significantly attenuated, resulting in photon noise limited data. In our survey data, we achieved H band contrasts >25000 (5σ ΔF1(1.575μm)>10 mag, ΔH>10.6 mag for a T6 spectral type) at a separation of 0.5" from the primary star. With this degree of attenuation, we can image (5σ detection) a 2-4 Jupiter mass planet at 5 AU around a 30 Myr star at 10 pc. We are currently completing our survey of young, nearby stars. We have obtained complete datasets for 40 stars in the southern sky (VLT) and 11 stars in the northern sky (MMT). We believe that our SDI images are the highest contrast astronomical images ever made from ground or space for methane rich companions.

Translated text

Key words

high contrast imaging,adaptive optics,extrasolar planets,simultaneous differential imaging

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper