Filter Response Curves

The data/filters/ subdirectory contains small files with tabulated filter response curves. All files contain a single curve stored in the ASCII enhanced character-separated value format, which is used to specify the wavelength units and provide the following metadata:

Key Description
group_name Name of the group that this filter response belongs to, e.g., sdss2010.
band_name Name of the filter pass band, e.g., r.
airmass Airmass used for atmospheric transmission, or zero for no atmosphere.
url URL with more details on how this filter response was obtained.
description Brief description of this filter response.

The following sections summarize the standard filters included with the speclite code distribution. Refer to the filters module API docs for details and examples of how to load an use these filters. See below for instructions on working with your own custom filters.

SDSS Filters

SDSS filter responses are taken from Table 4 of Doi et al, “Photometric Response Functions of the SDSS Imager”, The Astronomical Journal, Volume 139, Issue 4, pp. 1628-1648 (2010), and calculated as the reference response multiplied by the reference APO atmospheric transmission at an airmass 1.3. See the paper for details.

The group name sdss2010 is used to identify these response curves in speclite. The plot below shows the output of:

sdss = speclite.filters.load_filters('sdss2010-*')
speclite.filters.plot_filters(sdss, wavelength_limits=(3000, 11000))
sdss2010 filter curves

DECam Filters

DECam filter responses are taken from this Excel spreadsheet created by William Wester in September 2014 and linked to this NOAO DECam page. Throughputs include a reference atmosphere with airmass 1.3 provided by Ting Li. These are the most recent publicly available DECam throughputs as of Feb 2016.

The group name decam2014 is used to identify these response curves in speclite. The plot below shows the output of:

decam = speclite.filters.load_filters('decam2014-*')
speclite.filters.plot_filters(
    decam, wavelength_limits=(3000, 11000), legend_loc='upper left')
decam2014 filter curves

WISE Filters

WISE filter responses are taken from files linked to this page containing the weighted mean relative spectral responses described in Wright et al, “The Wide-field Infrared Survey Explorer (WISE): Mission Description and Initial On-orbit Performance”, The Astronomical Journal, Volume 140, Issue 6, pp. 1868-1881 (2010).

Note that these responses are based on pre-flight measurements but the in-flight responses of the W3 and W4 filters are observed to have effective wavelengths that differ by -(3-5)% and +(2-3)%, respectively. Refer to Section 2.2 of Wright 2010 for details. See also Section 2.1.3 of Brown 2014 for further details about W4.

The group name wise2010 is used to identify these response curves in speclite. The plot below shows the output of the command below, and matches Figure 6 of the paper:

wise = speclite.filters.load_filters('wise2010-*')
speclite.filters.plot_filters(wise, wavelength_limits=(2, 30),
    wavelength_unit=astropy.units.micron, wavelength_scale='log')
plt.gca().set_xticks([2, 5, 10, 20, 30])
plt.gca().set_xticklabels([2, 5, 10, 20, 30])
wise2010 filter curves

HSC Filters

HSC filter responses are taken from here. These throughputs include a reference atmosphere with airmass 1.2. Refer to Kawanamoto et al. 2017 (in prep).

The group name hsc2017 is used to identify these curves in speclite. The plot below shows the output of the following command:

hsc = speclite.filters.load_filters('hsc2017-*')
speclite.filters.plot_filters(hsc)
HSC filter curves

LSST Filters

LSST filter responses are taken from tag 12.0 of the LSST simulations throughputs package and include a standard atmosphere with airmass 1.2 that is also tabulated in the same package.

The group name lsst2016 is used to identify these response curves in speclite. The plot below shows the output of the command below, and matches Figure 6 of the paper:

lsst = speclite.filters.load_filters('lsst2016-*')
speclite.filters.plot_filters(
    lsst, wavelength_limits=(3000, 11000), legend_loc='upper left')
lsst filter curves

Johnson/Cousins Filters

Reference definitions of the Johnson/Cousins “standard” filters are taken from Table 2 of Bessell, M. S., “UBVRI passbands,” PASP, vol. 102, Oct. 1990, p. 1181-1199. We use the band name “U” for the response that Table 2 refers to as “UX”. Note that these do not represent the response of any actual instrument. Response values are normalized to have a maximum of one in each band.

The group name bessell is used to identify these response curves in speclite. The plot below shows the output of the command below:

bessell = speclite.filters.load_filters('bessell-*')
speclite.filters.plot_filters(bessell, wavelength_limits=(2900, 9300))
bessell filter curves

Custom Filters

In addition to the standard filters included with the speclite code distribution, you can create and read your own custom filters. For example to define a new filter group called “fangs” with “g” and “r” bands, you will first need to define your filter responses with new speclite.filters.FilterResponse objects:

fangs_g = speclite.filters.FilterResponse(
    wavelength = [3800, 4500, 5200] * u.Angstrom,
    response = [0, 0.5, 0], meta=dict(group_name='fangs', band_name='g'))
fangs_r = speclite.filters.FilterResponse(
    wavelength = [4800, 5500, 6200] * u.Angstrom,
    response = [0, 0.5, 0], meta=dict(group_name='fangs', band_name='r'))

Your metadata dictionary must include the group_name and band_name keys, but all of the keys listed above are recommended. You can now load and use these filters with their canonical names, although the group wildcard fangs-* is not supported. For example:

fangs = speclite.filters.load_filters('fangs-g', 'fangs-r')
speclite.filters.plot_filters(fangs)
custom filter curves

Next, save these filters in the correct format to any directory:

directory_name = '.'
fg_name = fangs_g.save(directory_name)
fr_name = fangs_r.save(directory_name)

Note that the file name in the specified directory is determined automatically based on the filter’s group and band names.

Finally, you can now read these custom filters from other programs by calling speclite.filters.load_filter() with paths:

directory_name = '.'
fg_name = os.path.join(directory_name, 'fangs-g.ecsv')
fr_name = os.path.join(directory_name, 'fangs-r.ecsv')
fangs_g = speclite.filters.load_filter(fg_name)
fangs_r = speclite.filters.load_filter(fr_name)

Note that load_filter and load_filters look for the ”.ecsv” extension in the name to recognize a custom filter.