exotic 0.45.3

EXOTIC: EXOplanet Transit Interpretation Code

EXOTIC (EXOplanet Transit Interpretation Code)

A Python 3 package for analyzing photometric data of transiting exoplanets into lightcurves and retrieving transit epochs and planetary radii.

The EXOplanet Transit Interpretation Code relies upon the transit method for exoplanet detection. This method detects exoplanets by measuring the dimming of a star as an orbiting planet transits, which is when it passes between its host star and the Earth. If we record the host star’s emitted light, known as the flux, and observe how it changes as a function of time, we should observe a small dip in the brightness when a transit event occurs. A graph of host star flux vs. time is known as a lightcurve, and it holds the key to determining how large the planet is, and how long it will be until it transits again.

(NASA Ames)

The objective of this pipeline is to help you reduce your images of your transiting exoplanet into a lightcurve, and fit a model to your data to extract planetary information that is crucial to increasing the efficiency of larger observational platforms, and futhering our astronomical knowledge.

Installation

While EXOTIC can run on a Windows, Mac, or Unix computer, we recommend that you use EXOTIC via the free Google Colab, as it is much easier to get installed and working. However, if you are a user with many images or large images, please message the Exoplanet Watch Team on Slack for guidance.

• Google Colab Cloud - RECOMMENDED

  • Requirements: A no-cost Google Drive account (GDrive) is required.
  • Features: No software installations are needed on your own computer.
  • Limitations: Images must be uploaded to GDrive over the internet, so fast network connections help.
  • Recommendations: If you run out of space in an existing GDrive account, you can sign up again. Some users even make a new Google account for every new dataset to avoid running out of space.
  • How to use EXOTIC on the Colab video
  • How to use EXOTIC on the Colab written instructions
  • EXOTIC: Google Colab Cloud Version (includes step-by-step instructions)

• Locally On Your Own Computer

  • Requirements: A Python 3 installation is required. (Many systems use Python 2 by default.)

  • Features: Images are read directly from your own computer's hard drive which can be fast and convenient -- no uploads to GDrive. This may be helpful for users with large filesizes, numerous files, or a slow internet connection. EXOTIC can be run through an iPython Jupyter Notebook (similar to the Google Colab interface) or directly at the command line.

  • Limitations: Extra software may be required (Python 3) and multiple subpackages must be downloaded and installed with EXOTIC. Using the Jupyter Notebook or command line to run EXOTIC can be confusing to some new users.

  • Installation Instructions:

  • Macintosh and Linux

  • Windows

  • How to Run EXOTIC On Your Own Computer

  • The easiest way to install exotic is with pip:

    $ pip3 install exotic

    Python 3 may use a different pip command (e.g. pip vs. pip3). If you're having trouble installing exotic from pip, please see our documentation for additional installation steps including setting up dependencies for Mac, Windows and Linux

• We also recommend that you download our sample transiting exoplanet dataset to confirm that EXOTIC is running correctly on the Google Colab Cloud or your own computer.

• How EXOTIC Works

  • Document
  • Video

• Lastly, we offer these documents in other languages

Requirements

FITS files with a modern header including parameters for UT time, exposure time, WCS coordinations (optional) are required for EXOTIC.

Sample Data and Outputs

We provide a sample dataset consisting of 142 fits files taken by a 6” telescope of the exoplanet HAT-P-32 b (V-mag = 11.44) observed on December 20, 2017. The telescope used to collect this dataset is part of the MicroObservatory Robotic Telescope Network operated by the Harvard-Smithsonian Center for Astrophysics.

Sample Data

A lightcurve from the sample dataset is shown below:

For the full output of EXOTIC please see the example output

*********************************************************
FINAL PLANETARY PARAMETERS

              Mid-Transit Time [BJD]: 2458107.714007 +- 0.000856
  Radius Ratio (Planet/Star) [Rp/Rs]: 0.1503 +- 0.0009
 Semi Major Axis/ Star Radius [a/Rs]: 5.146 +- 0.059
               Airmass coefficient 1: 7397.280 +- 19.7116
               Airmass coefficient 2: -0.1161 +- 0.0021
The scatter in the residuals of the lightcurve fit is: 0.5414 %

*********************************************************

Initializaton File

Get EXOTIC up and running faster with a json file. Please see the included file (inits.json) meant for the sample data. The initialization file has the following fields:

{
    "user_info": {
            "Directory with FITS files": "sample-data/HatP32Dec202017",
            "Directory to Save Plots": "sample-data/",
            "Directory of Flats": null,
            "Directory of Darks": null,
            "Directory of Biases": null,

            "AAVSO Observer Code (N/A if none)": "RTZ",
            "Secondary Observer Codes (N/A if none)": "N/A",

            "Observation date": "December 17, 2017",
            "Obs. Latitude": "+31.68",
            "Obs. Longitude": "-110.88",
            "Obs. Elevation (meters)": 2616,
            "Camera Type (CCD or DSLR)": "CCD",
            "Pixel Binning": "1x1",
            "Filter Name (aavso.org/filters)": "V",
            "Observing Notes": "Weather, seeing was nice.",

            "Plate Solution? (y/n)": "n",

            "Target Star X & Y Pixel": [424, 286],
            "Comparison Star(s) X & Y Pixel": [[465, 183], [512, 263]]
    },
    "planetary_parameters": {
            "Target Star RA": "02:04:10",
            "Target Star Dec": "+46:41:23",
            "Planet Name": "HAT-P-32 b",
            "Host Star Name": "HAT-P-32",
            "Orbital Period (days)": 2.1500082,
            "Orbital Period Uncertainty": 1.3e-07,
            "Published Mid-Transit Time (BJD-UTC)": 2455867.402743,
            "Mid-Transit Time Uncertainty": 4.9e-05,
            "Ratio of Planet to Stellar Radius (Rp/Rs)": 0.14856104152345367,
            "Ratio of Planet to Stellar Radius (Rp/Rs) Uncertainty": 0.004688608636917226,
            "Ratio of Distance to Stellar Radius (a/Rs)": 5.344,
            "Ratio of Distance to Stellar Radius (a/Rs) Uncertainty": 0.04,
            "Orbital Inclination (deg)": 88.98,
            "Orbital Inclination (deg) Uncertainity": 0.68,
            "Orbital Eccentricity (0 if null)": 0.159,
            "Star Effective Temperature (K)": 6001.0,
            "Star Effective Temperature (+) Uncertainty": 88.0,
            "Star Effective Temperature (-) Uncertainty": -88.0,
            "Star Metallicity ([FE/H])": -0.16,
            "Star Metallicity (+) Uncertainty": 0.08,
            "Star Metallicity (-) Uncertainty": -0.08,
            "Star Surface Gravity (log(g))": 4.22,
            "Star Surface Gravity (+) Uncertainty": 0.04,
            "Star Surface Gravity (-) Uncertainty": -0.04
    },
    "optional_info": {
            "Pixel Scale (Ex: 5.21 arcsecs/pixel)": null,
            "Filter Minimum Wavelength (nm)": null,
            "Filter Maximum Wavelength (nm)": null
    }
}

Features and Pipeline Architecture

• Automatic Plate Solution from http://nova.astrometry.net

• Resolve targets with NASA Exoplanet Archive + retrieve light curve priors

• Hot Pixel Masking

• Aperture Photometry with PSF centroiding (2D Gaussian + rotation)

• Stellar masking in background estimate

• Multiple comparison star + aperture size optimization

• Non-linear 4 parameter limb darkening with LDTK

• Light curve parameter optimization with Nested Sampling

Contributing to EXOTIC

EXOTIC is an open source project that welcomes contributions. Please fork the repository and submit a pull request to the develop branch for your addition(s) to be reviewed.

Citation

If you use any of these algorithms in your work, please cite our 2020 paper: Zellem, Pearson, Blaser, et al. 2020

Please also include the following statement in your paper's Acknowledgements section:

This publication makes use of data products from Exoplanet Watch, a citizen science project managed by NASA’s Jet Propulsion Laboratory on behalf of NASA’s Universe of Learning. This work is supported by NASA under award number NNX16AC65A to the Space Telescope Science Institute.

Exoplanet Watch

Contribute to Exoplanet Watch, a citizen science project that improves the properties of exoplanets and their orbits using observations processed with EXOTIC. Register with AAVSO and input your Observer Code to help track your contributions allowing for proper credit on future publications using those measurements. Ask about our Exoplanet Watch Slack Channel!

Acknowledgements

Exoplanet Watch is a project by NASA's Universe of Learning. NASA's Universe of Learning materials are based upon work supported by NASA under award number NNX16AC65A to the Space Telescope Science Institute, working in partnership with Caltech/IPAC, Center for Astrophysics | Harvard & Smithsonian, and the Jet Propulsion Laboratory.