sort-a-survey

an automated, optimizable & reproducible target selection algorithm for large astronomical surveys

Installation

Install sortasurvey using pip:

$ pip install sortasurvey

The survey binary should have been automatically placed in your system's path by the command. You can test your installation by using survey --help to see available command-line options:

$ survey --help
usage: sort-a-survey [-h] [-version] {setup,rank} ...

sort-a-survey: automated, optimizable and reproducible target selection

optional arguments:
  -h, --help           show this help message and exit
  -version, --version  Print version number and exit.

subcommands:
  {setup,rank}
    setup              Easy setup for directories and files
    rank               Rank targets for a given survey

Quickstart

Once you've successfully installed the package, we suggest to create a new directory to keep all your survey-related stuff in one place. The software package provides a pretty convenient setup feature that we highly recommend. Therefore, to get started in your terminal type:

$ mkdir survey
$ cd survey
$ survey setup

This last command will set up the proper directories and download a couple of example files to get you started via command line. If jupyter notebook is more your thing, use the notebook option (via -nb --nb -notebook --notebook) with the setup command which will also download our notebook tutorial:

$ survey setup -nb

Please visit the notebook tutorial if you don't feel like running it but would like to take a peek (there are pretty plots in there, I promise). To run the software via command line, simply execute:

$ survey rank

The optional -verbose command is True by default, which should show the following output (if you ran the above command):

 ------------------------------
 -- prioritization  starting --
 ------------------------------

   - loading sample and survey science information
   - 785 targets make the standard survey cuts
   - 242 have also passed various vetting steps
   - ranking algorithm initialized using 50.0 nights (10.0 hr/n)
   - algorithm took 53 seconds to run
   - 86 targets were selected, containing a total of 103 planets
   - Making data products, including:
     - a copy of the updated sample
     - algorithm history (via ranking steps)
     - the final prioritized list (via observing priorities)
     - final costs saved
     - program overlap
     - txt file w/ run info

 ------------------------------
 ----- process - complete -----
 ------------------------------

Thu 06/10/21 12:54PM

Out of the 86 total targets:
  - SC1A has 31 targets
  - SC1B has 30 targets
  - SC1C has 6 targets
  - SC1D has 4 targets
  - SC1E has 4 targets
  - SC2A has 44 targets
  - SC2Bi has 5 targets
  - SC2Bii has 2 targets
  - SC2C has 4 targets
  - SC3 has 21 targets
  - SC4 has 16 targets
  - TOA has 86 targets
  - TOB has 8 targets

And now you have our TESS-Keck Survey sample, it's as simple as that! If you'd like to see plots of this sample and/or more descriptions of what this all means, please check out the jupyter notebook or our recent paper (Chontos+2021) that discusses this all in more detail.

Features

Can't converge on a final target list? Do multiple science goals have you tripped up? Are you having a hard time balancing several programs within a set allocation? Have several nights of observing coming up?

Let sortasurvey do the heavy lifting for you.

Imagine a survey sample that can be:

• Automated
  • takes what would be an otherwise-complicated process and make it totally hands-off
• Optimized
  • can be ran many times with many different scenarios to create a sample that best fits your survey needs
• Reproduced
  • the randomized selection process and sample can be easily reproduced with our random seed feature
• Tested
  • Monte-carlo-like simulation capabilities to test how robust your survey sample is
• Visualized
  • creates helpful summary tables and stats

Tutorials

• sort-a-survey/examples/TKS.ipynb
• sort-a-survey/examples/Sample.ipynb

---

The work presented here was motivated by the TESS-Keck Survey (TKS), a large, dedicated radial velocity program using over 100 nights on Keck/HIRES to study transiting planets identified by the NASA TESS mission. TKS is a collaboration between researchers at the California Institute of Technology, the University of California, the University of Hawai'i, the University of Kansas, NASA, the NASA Exoplanet Science Institute and the W. M. Keck Observatory. Please visit this repo for more specific details on the application of this algorithm to the final TKS sample.

Attribution

Written by Ashley Chontos, with contributions from BJ Fulton, Erik Petigura, Joey Murphy, Ryan Rubenzahl, Sarah Blunt, Corey Beard, Tara Fetherolf, and Judah van Zandt.

Please cite the TKS paper if you make use of this software or the TKS sample in your work.

Acknowledgements

We recognize and acknowledge the cultural role and reverence that the summit of Maunakea has within the indigenous Hawaiian community. We are deeply grateful to have the opportunity to conduct observations from this mountain.

We thank all the observers who have spent time collecting data over the many years on Keck/HIRES. We gratefully acknowledge the efforts and dedication of the Keck Observatory staff for support of HIRES and remote observing. We thank Ken and Gloria Levy, who supported the construction of the Levy Spectrometer on the Automated Planet Finder. We thank the University of California and Google for supporting Lick Observatory and the UCO staff for their dedicated work scheduling and operating the telescopes of Lick Observatory.

We are grateful to the time assignment committees of the University of California, University of Hawai'i, the California Institute of Technology, and NASA for supporting the TESS-Keck Survey with observing time at Keck Observatory and on the Automated Planet Finder. We thank NASA for funding associated with our Key Strategic Mission Support project.