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Community Atlas of Tidal Streams

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# CATS 🐈

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Community Atlas of Tidal Streams

## Python dependencies

We have included a number of useful Python packages in the
[requirements.txt](https://github.com/stellarstreams/cats/blob/main/requirements.txt)
file — to install these, you can use `pip`:

pip install -r requirements.txt

Python 3.11 was _just_ released, but not all packages have built wheels for
this, so we recommend using Python 3.9 or 3.10.

## Contributor guide

- Fork the repository
- Clone the fork
- Set up pre-commit locally: `pre-commit install

## Kiyan Updates:

General Note: This pipeline is specifically designed to be applied to streams
for which we have all (or at least most) of the input information. It cannot be
used to detect new streams and I would anticipate some issues if it is used to
derive parameters for which we have very broad priors. Luckily, galstreams is
fantastic and we have the requisite phase space track information for nearly all
the streams. And we generally have isochrone information from the discovery
papers, although not always. Because of this, the process currently relies
heavily on galstreams and assumes that it is accurate enough to be used as a
good starting point for fine-tuning.

I believe there are two goals to using this process to make polygons and cuts:

1. Create a slightly cleaned version of the data that can be used for the
   membership probability model that Adrian and I have built for GD-1. Note: For
   GD-1, this used a very rough proper motion cut and a rough ischrone cut so
   that the overdensity in the sky position and proper motions became visible
   enough that the algorithm would work.
2. Create polygons that, when used together, give a relatively pure sample of
   the stream. It should be possible to get an initial guess at the stream
   members without going through the whole membership probability process.

#### Possible Next Improvements:

1. Use a background subtracted hess diagram when fitting for best isochrone
   instead of just the data. Just the data works fine once a proper motion cut
   has been applied in most cases but not beforehand.
2. Find a way to improve the extrapolation of galstreams tracks so that we can
   use the purest proper motion cuts (#4 below) in regions
3. How can I update one of the galstreams tracks without affecting the other
   tracks. (E.g. if I want to use the Starkman stream track in ($\phi1, \phi2$)
   but the Ibata track in the other coordinates.)
4. Using the functions that WG1 had originally written for adding footprints (I
   am currently just saying `pmprint= ...` instead of `add_new_pmprint(...)`
   which should improve things.
5. In general, cleaning up the code and making it legible/understandable for the
   community.
6. Testing on many other streams to see where improvements still need to be
   made.

#### More specifics on current code:

Making the polygons and stream member cuts is now a fully automated process that
uses the pawprint class. The process takes in the following inputs for each
stream:

- Galstreams (uses `pawprint_from_galstreams` function to create initial sky
  polygon)
  - short_name
  - pawprint_id
  - stream width (in degrees)
- Isochrone parameters:
  - Age (in Gyrs)
  - Metallicity
  - Distance (in kpc; don't think I actually use this, uses galstreams distance
    track instead)
  - alpha (not currently used either)
  - base error (half-width of CMD polygon if magnitudes had no errors)
  - scale error (scaling the width of the CMD polygon given errors on the
    magnitudes from data)
  - bin sizes (CMD color and magnitude bin sizes)
- Data parameters:
  - survey (name of survey)
  - band1 (generic name of band1 (e.g. 'g', 'r', 'i', etc))
  - band2
  - mag1 (specific name of band1 in survey (e.g. 'g0', 'r0', 'i0' for Panstarrs)
  - mag2
  - minmag
  - maxmag

The process will automatically make the following polygons to achieve these two
objectives:

1. Rough PM cut polygon (see `proper_motions.py` for documentation)
   - Takes the minima and maxima of the two proper motion galstreams tracks
   - Creates a rectangle in proper motion space out of those four numbers
   - Adds a "buffer" to each side of the rectangle (buffer defaults to 2 mas/yr
     but is tunable)
2. Sky polygon
   - Created in `pawprint` class using galstreams track and the input width
3. Isochrone polygon (see `CMD.py` for documentation)
   - Take any existing masks in sky position and proper motion
     - In the first pass, these should come directly from the galstreams track
       and the rough proper motion cut desscribed above. In later passes, it can
       be more specific.
   - Plot the masked data in CMD space, accounting for distance changes along
     the stream (using the distance track from galstreams)
   - Compare the theoretical isochrone to the data and make any small shifts in
     color or magnitude necessary to create a better match
     - In the first pass (before pm cuts), I do not make any shifts because the
       stream population is not distinguishable from the background. So I take
       the isochrone parameters to be the truth. In later passes, I allow
       shifts.
   - Fit a spline to this corrected theoretical isochrone and create the polygon
     by making upper and lower color bounds from that spline fit
4. Proper motion polygon in pm space
   - Take any existing masks in sky position and CMD (including an "offstream
     mask")
   - Make an initial guess at the mean proper motion (either an input or taken
     to be proper motion in middle of stream)
   - Use this guess to fit for the mean and standard deviation in each proper
     motion coordinate. This uses the residual pm histogram between the
     on-stream and off-stream regions.
   - Create polygon by fitting the best ellipse around the mean proper motion
     using a 2D Gaussian
   - Then, take galstreams proper motion tracks and "move" the ellipse along the
     stream to create a mask (includes extrapolation). This does not create a
     polygon but it can make a useful mask. See the next polygon for a way to
     make polygons that encodes this information
5. Proper motion polygon in each proper motion coordinate
   - Take any existing masks in sky position and CMD (including an "offstream
     mask")
   - Make an initial guess at the mean proper motion (either an input or taken
     to be proper motion in middle of stream)
   - Use this guess to fit for the mean and standard deviation in each proper
     motion coordinate. This uses the residual pm histogram between the
     on-stream and off-stream regions.
   - Take the galstreams tracks in each pm coordinate and create a polygon with
     half-width equal to the fitted standard deviation.
   - Limitation for now: does not extrapolate to outside the galstreams track so
     would not capture any potential stream stars outside this region.

Notes:

1. There is currently an issue when trying to use g vs g-i rather than g vs g-r
   for Pal 5. Not sure where this is coming from, I'll try to look into it but
   would appreciate any ideas.
2. In general, I think it's probably possible to do better with Pal 5 but I was
   trying to do this in as non-specific a way as possible, so that it could be
   generalized.
3. I've not added footprints in the way that it was originally designed. I
   couldn't figure out the add_new_footprint functions especially for the CMD
   footprints. So I am just updating the pawprint by saying
   `pawprint.cmdprint = new_cmdprint` (as opposed to
   `pawprint.add_new_cmdprint(new_cmd_print,...)` as originally designed. This
   can probably be easily updated by someone who originally helped write the
   pawprint functions.
4. I have only updated parts of the package that are related to the output that
   I was attempting to generate.
5. I can definitely improve the legibility of the code to make it more
   understandable. I will work on that next but could use help here also from
   those who have written packages before.

[actions-badge]:            https://github.com/stellarstreams/cats/workflows/CI/badge.svg
[actions-link]:             https://github.com/stellarstreams/cats/actions
[conda-badge]:              https://img.shields.io/conda/vn/conda-forge/cats
[conda-link]:               https://github.com/conda-forge/cats-feedstock
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[github-discussions-link]:  https://github.com/stellarstreams/cats/discussions
[pypi-link]:                https://pypi.org/project/cats/
[pypi-platforms]:           https://img.shields.io/pypi/pyversions/cats
[pypi-version]:             https://img.shields.io/pypi/v/cats
[rtd-badge]:                https://readthedocs.org/projects/cats/badge/?version=latest
[rtd-link]:                 https://cats.readthedocs.io/en/latest/?badge=latest

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