1 / 16

Goldilocks Zones – a Fine-Grained Exoplanet Taxonomy

Goldilocks Zones – a Fine-Grained Exoplanet Taxonomy. Patrick J. Talbot (Presenter) Dennis R. Ellis (Analysis). Introduction and Summary. Situation: Since the 1990's, over 5,000 exoplanets have been identified* and the rate of discovery is accelerating:

klopp
Download Presentation

Goldilocks Zones – a Fine-Grained Exoplanet Taxonomy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Goldilocks Zones – a Fine-Grained Exoplanet Taxonomy Patrick J. Talbot (Presenter) Dennis R. Ellis (Analysis)

  2. Introduction and Summary Situation: Since the 1990's, over 5,000 exoplanets have been identified* and the rate of discovery is accelerating: 1,569 Planets with good orbits + 24 Microlensing and imaged planets = 1,593 Total confirmed planets + 3,751 Unconfirmed Kepler candidates = 5,344 Total exoplanets + Kelper Candidates Need: a robust, extensible organization scheme for understanding, processing, and pattern discovery Approach: • A classic trade study to rank and score candidate attributes • A critic, a sequential optimizer, and software tests • Iteration to find a minimum spanning taxonomy • Result: • A minimum spanning taxonomy provides a uniques set of characteristics for each exoplanet. • To the extent practical, each exoplanet has a unique set of characteristics, or signature. https://github.com/OpenExoplanetCatalogue/open_exoplanet_catalogue * http://exoplanets.org/, as of 1 September 2015.

  3. Objective Organize information about exoplanets Input exoplanets characteristics into a hierarchical, frame-based ontology, like Protege Identify the minimum set of characteristics that span the problem space, so that each exoplanet has, to the extent practical, a unique signature. Input the knowledge base into data analytics tools, such as Weka Rule Induction, to automatically discover interesting pattterns. Quantify missing and conflicting data as sources of uncertainty to drive further refinement

  4. Approach Perform Trades • Mission • Functional Discretize • Rules Filter Attributes • Birthday Problem Compute Signatures Yes No Unique? Reduce #States Increase #States Verify Uniqueness No Yes Minimal? Done

  5. Data Structures, MIT Open Exoplanet Catalog Tag Description Unit Planet a single planet. May be a free floating (orphan) planet Star A single star. A star can be host to one or more planets Binary two stars, star/binary or two binaries. Declination Declination +/- dd mm ss Rightascension Right ascension hh mm ss Distance Distance from the Sun parsec Name Used multiple times for objects with multiple names. Semimajoraxis Semi-major axis of a planet (heliocentric coordinates) AU Separation Projected separation of planet from its host AU, arcsec Positionangle Position angle degree Eccentricity Eccentricity Periastron Longitude of periastron degree Longitude Mean longitude at a given Epoch (all planets in a system) degree Meananomaly Mean anomaly at a given Epoch (all planets in one system) degree Ascendingnode Longitude of the ascending node degree InclinatioN Inclination of the orbit degree Epoch Epoch for the orbital elements BJD Period Orbital period day Transittime Time of the center of a transit BJD Periastrontime Time of periastron BJD Mass Mass (or m sin(i) for radial velocity planets) Jupiter/Solar Radius Physical radius Jupiter/Solar Temperature Temperature (surface or equilibrium) Kelvin Age Age Gyr Metallicity Stellar metallicity log, rel/ solar Spectraltype Spectral type MagB B magnitude MagV Visual magnitude MagR R magnitude MagI I magnitude MagJ J magnitude MagH H magnitude MagK K magnitude Discoverymethod Discovery method : timing, RV, transit, imaging. Istransiting Whether the planet is transiting (1) or not (0). Description Short description of the planet Discoveryyear Year of the planet's discovery yyyy Lastupdate Date of the last (non-trivial) update yy/mm/dd Spinorbitalignment Rossiter-McLaughlin Effect. degree

  6. Mission Trade Study

  7. Functional Trade Study

  8. Node Composite Score

  9. Sample Rules

  10. Probability of Duplicates vs Catalog Size

  11. Exoplanet Characteristics Hierarchy Exoplanet Name Input Exoplanets Header Only Header Only Header Only Elements Physical Host star Inclination Period Metallicity Distance Binary Eccentricity Mass Spectral Type Radius Semi-major axis Separation Age Temperature

  12. Exoplanet Signature Separation (Moderate) Eccentricity (Nearly Circular) Distance (Far) Mass (Small) Spectral type (Hottest) Age (Ancient) Temperature (Hot) H H 2 M L S L N L O L A Binary (2 stars) Physical Radius (Large) Period (Long) Inclination (Low) Semi-major Axis (Large)

  13. Exoplanet Kiviat Diagram Physical Orbital Composition [FeHg] Inclination Mass Period Size Kepler 423c Habitability Index = .3 Goldilocks Zone Host Variability Host Age Host Spectral Type Environmental Influential

  14. Coming in the Full Paper Percent of Duplicate Signatures Sensitivity of Duplicates to Attribute States Optimum Set of Attributes and # States Weka Rule Induction to Identify Patterns

  15. Summary Characteristics of an exoplanet taxonomy were identified Trade studies ranked attributes Rules discretized attributes Taxonomy was sized (Birthday Problem) A fine-grained taxonomy was portrayed: Taxonomy Hierarchy Taxonomy Signature Taxonomy Kiviat

  16. Backup:Example Histograms

More Related