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Register a new userQuick-MESS: A fast statistical tool for Exoplanet Imaging Surveys
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Mariangela Bonavita
Publication date
2013
fields
Physics
and research's language is
English
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Several tools have been developed in the past few years for the statistical analysis of the exoplanet search surveys, mostly using a combination of Monte-Carlo simulations or a Bayesian approach.Here we present the Quick-MESS, a grid-based, non-Monte Carlo tool aimed to perform statistical analyses on results from and help with the planning of direct imaging surveys. Quick-MESS uses the (expected) contrast curves for direct imaging surveys to assess for each target the probability that a planet of a given mass and semi-major axis can be detected. By using a grid-based approach Quick-MESS is typically more than an order of magnitude faster than tools based on Monte-Carlo sampling of the planet distribution. In addition, Quick-MESS is extremely flexible, enabling the study of a large range of parameter space for the mass and semi-major axes distributions without the need of re-simulating the planet distribution. In order to show examples of the capabilities of the Quick-MESS, we present the analysis of the Gemini Deep Planet Survey and the predictions for upcoming surveys with extreme-AO instruments.
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The Exoplanet Imaging Data Challenge is a community-wide effort meant to offer a platform for a fair and common comparison of image processing methods designed for exoplanet direct detection. For this purpose, it gathers on a dedicated repository (Zenodo), data from several high-contrast ground-based instruments worldwide in which we injected synthetic planetary signals. The data challenge is hosted on the CodaLab competition platform, where participants can upload their results. The specifications of the data challenge are published on our website. The first phase, launched on the 1st of September 2019 and closed on the 1st of October 2020, consisted in detecting point sources in two types of common data-set in the field of high-contrast imaging: data taken in pupil-tracking mode at one wavelength (subchallenge 1, also referred to as ADI) and multispectral data taken in pupil-tracking mode (subchallenge 2, also referred to as ADI mSDI). In this paper, we describe the approach, organisational lessons-learnt and current limitations of the data challenge, as well as preliminary results of the participants submissions for this first phase. In the future, we plan to provide permanent access to the standard library of data sets and metrics, in order to guide the validation and support the publications of innovative image processing algorithms dedicated to high-contrast imaging of planetary systems.
The solar gravitational lens (SGL) provides a factor of $10^{11}$ amplification for viewing distant point sources beyond our solar system. As such, it may be used for resolved imaging of extended sources, such as exoplanets, not possible otherwise. To use the SGL, a spacecraft carrying a modest telescope and a coronagraph must reach the SGLs focal region, that begins at $sim$550 astronomical units (AU) from the Sun and is oriented outward along the line connecting the distant object and the Sun. No spacecraft has ever reached even a half of that distance; and to do so within a reasonable mission lifetime (e.g., less than 25 years) and affordable cost requires a new type of mission design, using solar sails and microsats ($<100$~kg). The payoff is high -- using the SGL is the only practical way we can ever get a high-resolution, multi-pixel image of an Earth-like exoplanet, one that we identify as potentially habitable. This paper describes a novel mission design starting with a rideshare launch from the Earth, spiraling in toward the Sun, and then flying around it to achieve solar system exit speeds of over $20$ AU/year. A new sailcraft design is used to make possible high area to mass ratio for the sailcraft. The mission design enables other fast solar system missions, starting with a proposed very low cost technology demonstration mission (TDM) to prove the functionality and operation of the microsat-solar sail design and then, building on the TDM, missions to explore distant regions of the solar system, and those to study Kuiper Belt objects (KBOs) and the recently discovered interstellar objects (ISOs) are also possible.
In Spring 2013, the LEECH (LBTI Exozodi Exoplanet Common Hunt) survey began its $sim$130-night campaign from the Large Binocular Telescope (LBT) atop Mt Graham, Arizona. This survey benefits from the many technological achievements of the LBT, including two 8.4-meter mirrors on a single fixed mount, dual adaptive secondary mirrors for high Strehl performance, and a cold beam combiner to dramatically reduce the telescopes overall background emissivity. LEECH neatly complements other high-contrast planet imaging efforts by observing stars at L (3.8 $mu$m), as opposed to the shorter wavelength near-infrared bands (1-2.4 $mu$m) of other surveys. This portion of the spectrum offers deep mass sensitivity, especially around nearby adolescent ($sim$0.1-1 Gyr) stars. LEECHs contrast is competitive with other extreme adaptive optics systems, while providing an alternative survey strategy. Additionally, LEECH is characterizing known exoplanetary systems with observations from 3-5$mu$m in preparation for JWST.
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We present the novel, semi-automated clustering tool ASPECT for analysing voluminous archives of spectra. The heart of the program is a neural network in form of Kohonens self-organizing map. The resulting map is designed as an icon map suitable for the inspection by eye. The visual analysis is supported by the option to blend in individual object properties such as redshift, apparent magnitude, or signal-to-noise ratio. In addition, the package provides several tools for the selection of special spectral types, e.g. local difference maps which reflect the deviations of all spectra from one given input spectrum (real or artificial). ASPECT is able to produce a two-dimensional topological map of a huge number of spectra. The software package enables the user to browse and navigate through a huge data pool and helps him to gain an insight into underlying relationships between the spectra and other physical properties and to get the big picture of the entire data set. We demonstrate the capability of ASPECT by clustering the entire data pool of 0.6 million spectra from the Data Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results regarding quality and completeness we track objects from existing catalogues of quasars and carbon stars, respectively, and connect the SDSS spectra with morphological information from the GalaxyZoo project.
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