The Kepler mission surveys a region of the Milky Way galaxy to detect, identify and characterize planets in or near the habitable zone (region around a star where a planet with sufficient atmospheric pressure can maintain liquid water on its surface). The spacecraft is located in an orbit that permits continuous targeting of a single stellar region.
The Kepler field or plane is defined as stellar coordinates l = 76.32°, b = +13.5° (RA=19h 22m 40s, Dec=+44° 30' 00'. RA (the equivalent of longitude) means right ascension, and Dec (the equivalent of latitude) means descension.
Data from the spacecraft is downloaded and archived as original data. Data then is sorted and analyzed or processed along with engineering data files (equipment performance). Finally, astronomical data is released publicly through different media while support data is maintained by NASA, while complying with astronomical data standards.
The Mikulski Archive for Space Telescopes (MAST) supports NASA’s astronomical data archives, as well as the Kepler Exoplanet Archive (Caltech.edu). The latter site allows retrieval of data for the 2321 data points on record. The team chose to use the latter standardized data which is downloadable in CSV format. A data dictionary:
(http://exoplanetarchive.ipac.caltech.edu/docs/API_kepcandidate_columns.html),
and additional documentation on astronomical data is available at the site:
http://exoplanetarchive.ipac.caltech.edu/docs/documentation.html.
Considering the massive amounts of non static data available, our challenge was data reduction and attributes selection: which parameters or attributes do we need to select to complete our artistic rendition or visualization yet representing scientific data factually, given the many ways to calculate or utilize many of the exoplanets parameters available.
The Kepler Exoplanet Archive contains approximately 108 attributes (file is updated periodically). Only fourteen (14) attributes are being used for this visualization. The remaining attributes are not required for meeting the objective of this visualization. Data was verified using Google Refine in search of aberrant values, unnecessary spaces or signs of data in need of normalization. Data did not need additional action from the team.
Attributes Used:
- Kepler id – Sequential number used to identify the planet/star
- Effective temperature – Temperature of a body such as a star or planet. It is the temperature of a black body that would emit the same total amount of electromagnetic radiation. It is expressed in Kelvins.
- Right Ascension – Stellar or galactic coordinate equivalent to terrestrial longitude. It is expressed in hours, minutes and seconds related to positions on the celestial sphere. It is also measured in degrees equal to Descenscion or Declination (Each hour in RA equals an angle of 15 degrees in the celestial sphere, totaling 24 hours).
- Descenscion – Stellar or galactic coordinate equivalent to terrestrial latitude. It is expressed in degrees to time (minutes and seconds).
- Surface Gravity – Gravitational Acceleration at the surface of the astronomical object.
- Stellar Radius – Center to edge of a planet/star. Not a reliable measure due to gaseous nature of some astronomical objects.
- Stellar Mass – The mass of a star usually expressed in terms of the sun’s mass (solar = 1).
- Kepler Name – Name for confirmed planets.
- Kepler Object of Interest Type – Type or classification for planets:
- Candidate, Candidate FOP (Follow-up Program), Confirmed or False Positive.
- Galaxy – Flag to identify a Star or a Galaxy
- Planet/ Star Distance (AU): astronomical unit (AU) or 149.6 million kilometers.
- Planet Radius – It is usually measured in Jupiter radii (44400 miles). Earth’s radius is 0.09 Jupiter radii.
- Planet Radius Uncertainty – Factor to support variability in results due to lack of precise measurement (related to distance and variability caused by an expanding universe).
- Planet/Star Distance Uncertainty - Factor to support variability in results due to lack of precise measurements (related to distance and variability caused by an expanding universe). Measured in Aus.
Derived Data:
Colors used in the visualization reflect the nature of astronomical data:
- A dark color was used for the background depicting space as we see it.
- Exoplanet colors used correspond to astronomical color classification based on temperature, excluding subsystem categorization in order to simplify the artistic rendition in this visualization.
- A more precise mass/temperature color coding sequence (including subsystem) was not pursued, due to mass data unavailability within file.
- Stars/planets within the Kepler project scope are similar to those in our solar system. Hence, no blue or blue white stars shown in the Goldilocks (habitable) zone.
- http://www.astrophysical.org/starclassification.php
| Code | Star Color | Temperature (K) | Hexadecimal Color Code |
| O | Blue Star | 30,000-60,000 | #FF4A14 |
| B | Blue White Star | 10,000-30,000 | FFBCBD |
| A | White Stars | 7,500-10,000 | #FFFFFF |
| F | Yellow White Stars | 6,000-7,500 | #DDFFFF |
| G | Yellow Stars | 5,000-6,000 | #AAFFFF |
| K | Yellow Orange Stars | 3,500-5,000 | #FFFEB2 |
| M | Red Stars | < 3,500 | #FFFF00 |
Scientists express a planets size by comparing them to the size of the planets in our own solar system. The most common references are as follows:
- Rp = Radius of the planet and Re = Radius of the Earth
- Earth - exoplanets with a radius (Rp) of less than 1.25 Earth radius (Re)
- Super Earth - exoplanets with 1.25 x Re < Rp = 2.0 x Re
- Neptune - exoplanets with 2.0 x Re < Rp = 6.0 x Re
- Jupiter Size - exoplanets with 6.0 x Re < Rp = 15 x Re
- Super Jupiter Size – exoplanets with 15.0 x Re < Rp = 22 x Re
Data Lessons
This section focuses on key lessons learned during the development our visualization project.
- One lesson learned is that data is obviously easier to manage and use when a qualitative control exists. Data utilized in this project is controlled by NASA (National and Aeronautics and Space Administration) and participating institutions thus providing reliability and accuracy, and reducing data cleansing/normalization processes.
- Varied ways of calculating and interpreting data poses a challenge in selecting and representing data factually and objectively. During research, the team noticed that some attributes can be represented in different ways depending on the analysis method in use. Ultimately, attributes used were selected with the most accurate representation in mind for our visualization.
- Some of the data collected as part of the Kepler program is still under study. Our finding during research is that many attributes are still in flux; either these are being developed or require further observation and confirmation. This situation also represented a challenge for the team related to design-content decisions for the visualization, limiting the selection of attributes used.
- Astrophysics expertise: Team members are not experts in the subject field and the complexity of related data. The team feels pleased with the concepts learned and applied during the project. The team relied on the extensive amounts of information available to succeed in overcoming our data learning curve and “astrophysics expertise”.
References
http://archive.stsci.edu/kepler/manuals/archive_manual.pdf
http://kepler.nasa.gov/Science/ForScientists/keplerconference/sessions/
http://keplergo.arc.nasa.gov/
http://adsabs.harvard.edu/full/1997IAUS..189...51S
http://exoplanetarchive.ipac.caltech.edu/docs/API_kepcandidate_columns.html
http://exoplanetarchive.ipac.caltech.edu/cgi-bin/ExoTables/nph-exotbls?kepler=1
http://www.astronomy.ohio-state.edu/~pogge/Ast162/Unit1/binaries.html
http://archive.stsci.edu/kepler/planet_candidates.html
http://keplergo.arc.nasa.gov/News.shtml
http://hyperphysics.phy-astr.gsu.edu/hbase/astro/para.html
http://www.planetary.org/exoplanets/glossary.php