PeriodicDetectMetric

class rubin_sim.maf.metrics.PeriodicDetectMetric(mjd_col='observationStartMJD', periods=2.0, amplitudes=0.1, m5_col='fiveSigmaDepth', metric_name='PeriodicDetectMetric', filter_col='filter', star_mags=20, sig_level=0.05, sed_template='F', **kwargs)

Bases: BaseMetric

Determine if we would be able to classify an object as periodic/non-uniform, using an F-test The idea here is that if a periodic source is aliased, it will be indistinguishable from a constant source, so we can find a best-fit constant, and if the reduced chi-squared is ~1, we know we are aliased.

Parameters:

periodfloat (2) or array

The period of the star (days). Can be a single value, or an array. If an array, amplitude and starMag should be arrays of equal length.

amplitudefloar (0.1)

The amplitude of the stellar variablility (mags).

starMagfloat (20.)

The mean magnitude of the star in r (mags).

sig_levelfloat (0.05)

The value to use to compare to the p-value when deciding if we can reject the null hypothesis.

sed_templatestr (‘F’)

The stellar SED template to use to generate realistic colors (default is an F star, so RR Lyrae-like)

Returns:

1 if we would detect star is variable, 0 if it is well-fit by a constant value. If using arrays to test multiple

period-amplitude-mag combinations, will be the sum of the number of detected stars.

Methods Summary

run(data_slice[, slice_point])

Calculate metric values.

Methods Documentation

run(data_slice, slice_point=None)

Calculate metric values.

Parameters:

data_slicenumpy.recarray

Values passed to metric by the slicer, which the metric will use to calculate metric values at each slice_point.

slice_pointdict or None

Dictionary of slice_point metadata passed to each metric. E.g. the ra/dec of the healpix pixel or opsim fieldId.

Returns:

metricValue: int float or object

The metric value at each slice_point.