Quick-MESS: A fast statistical tool for Exoplanet Imaging Surveys

Random sampling technique for ultra-fast computations of molecular opacities for exoplanet atmospheres

Opacities of molecules in exoplanet atmospheres rely on increasingly detailed line-lists for these molecules. The line lists available today contain for many species up to several billions of lines. Computation of the spectral line profile created by pressure and temperature broadening, the Voigt profile, of all of these lines is becoming a computational challenge. We aim to create a method to compute the Voigt profile in a way that automatically focusses the computation time into the strongest lines, while still maintaining the continuum contribution of the high number of weaker lines. Here, …

系外行星探测与表征的先进建模

Advanced Modeling for Exoplanet Detection and Characterization

本研究利用开普勒光变曲线与机器学习方法，通过凌星信号提取行星参数并分类恒星，实现更高效的系外行星探测与表征。

Colour-magnitude diagrams of transiting Exoplanets -- III. A public code, nine strange planets, and the role of Phosphine

Colour-Magnitude Diagrams provide a convenient way of comparing populations of similar objects. When well populated with precise measurements, they allow quick inferences to be made about the bulk properties of an astronomic object simply from its proximity on a diagram to other objects. We present here a Python toolkit which allows a user to produce colour-magnitude diagrams of transiting exoplanets, comparing planets to populations of ultra-cool dwarfs, of directly imaged exoplanets, to theoretical models of planetary atmospheres, and to other transiting exoplanets. Using a selection of near…

Analyzing the Habitable Zones of Circumbinary Planets Using Machine Learning

Exoplanet detection in the past decade by efforts including NASA's Kepler and TESS missions has discovered many worlds that differ substantially from planets in our own Solar System, including more than 150 exoplanets orbiting binary or multi-star systems. This not only broadens our understanding of the diversity of exoplanets, but also promotes our study of exoplanets in the complex binary systems and provides motivation to explore their habitability. In this study, we investigate the Habitable Zones of circumbinary planets based on planetary trajectory and dynamically informed habitable zone…

Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey

The Gemini Planet Imager Exoplanet Survey (GPIES) is a multi-year 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 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…

A simple, quantitative method to infer the minimum atmospheric height of small exoplanets

Amongst the many hundreds of transiting planet candidates discovered by the Kepler Mission, one finds a large number of candidates with sizes between that of the Earth and Neptune. The composition of these worlds is not immediately obvious with no Solar System analog to draw upon and there exists some ambiguity as to whether a given candidate is a rocky Super-Earth or a gas-enveloped Mini-Neptune. The potential scientific value and observability of the atmospheres of these two classes of worlds varies significantly and given the sheer number of candidates in this size-range, there is evidently…

Active minimization of non-common path aberrations in long-exposure imaging of exoplanetary systems

Context. Spectroscopy of exoplanets is very challenging because of the high star-planet contrast. A technical difficulty in the design of imaging instruments is the noncommon path aberrations (NCPAs) between the adaptive optics (AO) sensing and the science camera, which induce planet-resembling stellar speckles in the coronagraphic science images. In an observing sequence of several long exposures, quickly evolving NCPAs average out and leave behind an AO halo that adds photon noise to the planet detection. Static NCPA can be calibrated a posteriori using differential imaging techniques. Howev…

The TESS Faint Star Search: 1,617 TOIs from the TESS Primary Mission

We present the detection of 1,617 new transiting planet candidates, identified in the Transiting Exoplanet Survey Satellite (TESS) full-frame images (FFIs) observed during the Primary Mission (Sectors 1 - 26). These candidates were initially detected by the Quick-Look Pipeline (QLP), which extracts FFI lightcurves for and searches all stars brighter than TESS magnitude T = 13.5 mag in each sector. However, QLP heavily relies on manual inspection for the identification of planet candidates, limiting vetting efforts to planet-hosting stars brighter than T = 10.5 mag and leaving millions of poten…

使用光谱成像中的光谱互相关进行系外行星探测极限研究：应用于ELT-HARMONI的分析模型

Exoplanets detection limits using spectral cross-correlation with spectro-imaging. An analytical model applied to the case of ELT-HARMONI

提出一种基于光谱互相关的分子图谱分析模型，用于高效预测系外行星探测性能，并应用于ELT-HARMONI，验证其在短分离冷行星探测中的高对比度潜力。

Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations

The TESS mission produces a large amount of time series data, only a small fraction of which contain detectable exoplanetary transit signals. Deep learning techniques such as neural networks have proved effective at differentiating promising astrophysical eclipsing candidates from other phenomena such as stellar variability and systematic instrumental effects in an efficient, unbiased and sustainable manner. This paper presents a high quality dataset containing light curves from the Primary Mission and 1st Extended Mission full frame images and periodic signals detected via Box Least Squares (…