Working with Light Curves#

Performing a Light Curve Query#

Light curves can be fetched in the same way as in previous tutorials. For this tutorial, an ASAS-SN query will be performed:

from ATK import query

asassn_query = query("lightcurve", targets=6050296829033196032, survey="asassn", path="example_lightcurve.fits.gz")
asassn_query.show(show_types=True)

<Lightcurve DataSet>

.kind (str):        Lightcurve
.targets (list):    6050296829033196032 | 245.447° -22.886° (icrs, 2016-01-01T00:00:00.000, 3.0″)
.exception (bool):  False
.data (list):
          <asassn v-band Lightcurve>
                    survey (str):                  asassn
                    band (str):                    v
                    correction (str):              full
                    search_pos (astropy.SkyCoord): 245.447° -22.886° (icrs, 2015-01-01T00:00:00.000)
                    separation (astropy.Quantity): 0.026″
                    mjd (astropy.Quantity):        [56783.46, 56785.411, ..., 58382.027, 58384.005] d
                    mag (astropy.Quantity):        [15.352, 14.668, ..., 14.913, 15.388] mag
                    mag_err (astropy.Quantity):    [0.022, 0.023, ..., 0.03, 0.038] mag
                    ra (astropy.Quantity):         [245.447, 245.447, ..., 245.447, 245.447] °
                    dec (astropy.Quantity):        [-22.886, -22.886, ..., -22.886, -22.886] °

          <asassn g-band Lightcurve>
                    survey (str):                  asassn
                    band (str):                    g
                    correction (str):              full
                    search_pos (astropy.SkyCoord): 245.447° -22.886° (icrs, 2015-01-01T00:00:00.000)
                    separation (astropy.Quantity): 0.026″
                    mjd (astropy.Quantity):        [58143.538, 58145.531, ..., 60841.348, 60844.164] d
                    mag (astropy.Quantity):        [15.537, 15.752, ..., 15.134, 14.93] mag
                    mag_err (astropy.Quantity):    [0.066, 0.046, ..., 0.071, 0.016] mag
                    ra (astropy.Quantity):         [245.447, 245.447, ..., 245.447, 245.447] °
                    dec (astropy.Quantity):        [-22.886, -22.886, ..., -22.886, -22.886] °


Available Methods: .add(), .apply(), .from_target(), .merge(), .open(), .plot(), .save(), .show(), .split(), .store()

The returned DataSet’s data attribute is a list of Lightcurve objects (one per photometric band per target, subject to data availability).

Note

ATK supports queries to the following lightcurve surveys and bands:

ZTF - ztf (g, r, i)
Gaia - gaia (g, bp, rp)
ATLAS - atlas (c, o)
ASAS-SN - asassn (g, v)
TESS - tess (Tmag)
CRTS - crts (v)

For a refresher on query() fundamentals, see previous tutorials.

If the desired survey is not listed above, see here for a tutorial on utilising external data.

Plotting the Returned Data#

To plot a Lightcurve and open it in the default browser:

asassn_query.open()

Bokeh Figure

Note

For a refresher on plotting fundamentals, see previous tutorials.

Splitting by Survey ID#

By default, light curves are returned as forced-photometry (i.e. all photometric detections in the requested radius are returned as a single Lightcurve per band). However, in some cases - e.g. in crowded regions or when using a large search radius - this assumption breaks down:

import astropy.units as u

asassn_query = query("lightcurve", targets=587316166180416640, survey="asassn", radius = 60 * u.arcsec, path="example_lightcurve_2.fits.gz")
asassn_query.show(show_types=True)
asassn_query.open()

<Lightcurve DataSet>

.kind (str):        Lightcurve
.targets (list):    587316166180416640 | 141.185° 8.031° (icrs, 2016-01-01T00:00:00.000, 60.0″)
.exception (bool):  False
.data (list):
          <asassn v-band Lightcurve>
                    survey (str):                  asassn
                    band (str):                    v
                    correction (str):              full
                    search_pos (astropy.SkyCoord): 141.185° 8.031° (icrs, 2015-01-01T00:00:00.000)
                    separation (astropy.Quantity): 36.096″
                    mjd (astropy.Quantity):        [55946.483, 55954.478, ..., 58431.588, 58441.629] d
                    mag (astropy.Quantity):        [17.58, 17.911, ..., 17.287, 17.574] mag
                    mag_err (astropy.Quantity):    [0.152, 0.177, ..., 0.153, 0.164] mag
                    ra (astropy.Quantity):         [141.185, 141.185, ..., 141.201, 141.201] °
                    dec (astropy.Quantity):        [8.031, 8.031, ..., 8.026, 8.026] °

          <asassn g-band Lightcurve>
                    survey (str):                  asassn
                    band (str):                    g
                    correction (str):              full
                    search_pos (astropy.SkyCoord): 141.185° 8.031° (icrs, 2015-01-01T00:00:00.000)
                    separation (astropy.Quantity): 35.333″
                    mjd (astropy.Quantity):        [58056.48, 58080.455, ..., 60704.982, 60706.938] d
                    mag (astropy.Quantity):        [17.774, 18.085, ..., 18.242, 18.153] mag
                    mag_err (astropy.Quantity):    [0.181, 0.204, ..., 0.19, 0.191] mag
                    ra (astropy.Quantity):         [141.185, 141.185, ..., 141.201, 141.201] °
                    dec (astropy.Quantity):        [8.031, 8.031, ..., 8.026, 8.026] °


Available Methods: .add(), .apply(), .from_target(), .merge(), .open(), .plot(), .save(), .show(), .split(), .store()

Bokeh Figure

It is clear that the returned Lightcurve objects contain photometry of at least two stars. In this case, it may be helpful to split the returned photometry by a per-survey observation/object ID. This can be done by passing split=True to query():

import astropy.units as u

asassn_query = query("lightcurve", targets=587316166180416640, survey="asassn",  radius = 60 * u.arcsec, split=True, path="example_lightcurve_3.fits.gz")
asassn_query.show(show_all=True)

<Lightcurve DataSet>

.kind:      Lightcurve
.targets:   587316166180416640 | 141.185° 8.031° (icrs, 2016-01-01T00:00:00.000, 60.0″)
.exception: False
.data:
      <asassn v-band Lightcurve>
            survey:     asassn
            band:       v
            correction: full
            search_pos: 141.185° 8.031° (icrs, 2015-01-01T00:00:00.000)
            separation: 36.55″
            obj_id:     103080678062
            mjd:        [55946.483, 55950.478, ..., 58446.596, 58447.606] d
            mag:        [14.405, 14.491, ..., 14.446, 14.509] mag
            mag_err:    [0.008, 0.011, ..., 0.048, 0.025] mag
            ra:         [141.195, 141.195, ..., 141.195, 141.195] °
            dec:        [8.034, 8.034, ..., 8.034, 8.034] °

      <asassn g-band Lightcurve>
            survey:     asassn
            band:       g
            correction: full
            search_pos: 141.185° 8.031° (icrs, 2015-01-01T00:00:00.000)
            separation: 36.55″
            obj_id:     103080678062
            mjd:        [58054.5, 58055.488, ..., 60837.983, 60837.987] d
            mag:        [14.596, 14.673, ..., 14.77, 14.678] mag
            mag_err:    [0.028, 0.008, ..., 0.049, 0.03] mag
            ra:         [141.195, 141.195, ..., 141.195, 141.195] °
            dec:        [8.034, 8.034, ..., 8.034, 8.034] °

      <asassn v-band Lightcurve>
            survey:     asassn
            band:       v
            correction: full
            search_pos: 141.185° 8.031° (icrs, 2015-01-01T00:00:00.000)
            separation: 0.243″
            obj_id:     661428731552
            mjd:        [55946.483, 55954.478, ..., 58407.619, 58431.588] d
            mag:        [17.58, 17.911, ..., 17.3, 17.53] mag
            mag_err:    [0.152, 0.177, ..., 0.179, 0.202] mag
            ra:         [141.185, 141.185, ..., 141.185, 141.185] °
            dec:        [8.031, 8.031, ..., 8.031, 8.031] °

      <asassn g-band Lightcurve>
            survey:     asassn
            band:       g
            correction: full
            search_pos: 141.185° 8.031° (icrs, 2015-01-01T00:00:00.000)
            separation: 0.243″
            obj_id:     661428731552
            mjd:        [58056.48, 58080.455, ..., 60674.38, 60704.982] d
            mag:        [17.774, 18.085, ..., 17.467, 18.217] mag
            mag_err:    [0.181, 0.204, ..., 0.097, 0.188] mag
            ra:         [141.185, 141.185, ..., 141.185, 141.185] °
            dec:        [8.031, 8.031, ..., 8.031, 8.031] °

      <asassn v-band Lightcurve>
            survey:     asassn
            band:       v
            correction: full
            search_pos: 141.185° 8.031° (icrs, 2015-01-01T00:00:00.000)
            separation: 58.99″
            obj_id:     661436975834
            mjd:        [55946.483, 55954.478, ..., 58431.588, 58441.629] d
            mag:        [17.702, 17.777, ..., 17.287, 17.574] mag
            mag_err:    [0.143, 0.161, ..., 0.153, 0.164] mag
            ra:         [141.201, 141.201, ..., 141.201, 141.201] °
            dec:        [8.026, 8.026, ..., 8.026, 8.026] °

      <asassn g-band Lightcurve>
            survey:     asassn
            band:       g
            correction: full
            search_pos: 141.185° 8.031° (icrs, 2015-01-01T00:00:00.000)
            separation: 58.99″
            obj_id:     661436975834
            mjd:        [58056.48, 58059.471, ..., 60704.982, 60706.938] d
            mag:        [17.605, 18.065, ..., 18.242, 18.153] mag
            mag_err:    [0.158, 0.185, ..., 0.19, 0.191] mag
            ra:         [141.201, 141.201, ..., 141.201, 141.201] °
            dec:        [8.026, 8.026, ..., 8.026, 8.026] °


Available Methods: .add(), .apply(), .from_target(), .merge(), .open(), .plot(), .save(), .show(), .split(), .store()

Note

By default, show() truncates printing of the data attribute if it contains a large number of containers. This can be disabled by passing show_all=True to show().

The split light curves can then be plotted in the same way as before - now producing a grid with three figures (one per unique source):

asassn_query.open()

Bokeh Figure

Warning

As split utilises a per-survey object ID to separate detections, its efficacy depends heavily on the specific implementation provided by each survey.

Data Quality Filtering#

By default, basic quality filtering is enabled for the following surveys:

ATLAS: Filtering is performed as advised here.

TESS: Flux significance > 3, quality flag = 0.

To disable all unrequired filtering, pass filter=False to query() when retrieving data from one the above surveys.

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Total running time of the script: (0 minutes 1.839 seconds)

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