GRBTransientMetric

class rubin_sim.maf.maf_contrib.GRBTransientMetric(alpha=1, apparent_mag_1min_mean=15.35, apparent_mag_1min_sigma=1.59, metric_name='GRBTransientMetric', mjd_col='expMJD', m5_col='fiveSigmaDepth', filter_col='filter', trans_duration=10.0, survey_duration=10.0, survey_start=None, detect_m5_plus=0.0, n_per_filter=1, n_filters=1, min_delta_mag=0.0, n_phase_check=1, **kwargs)

Bases: BaseMetric

Detections for on-axis GRB afterglows decaying as

F(t) = F(1min)((t-t0)/1min)^-alpha. No jet break, for now.

Derived from TransientMetric, but calculated with reduce functions to

enable-band specific counts.

Burst parameters taken from 2011PASP..123.1034J.

Simplifications: no color variation or evolution encoded yet. no jet breaks. not treating off-axis events.

Parameters:

alphafloat,

temporal decay index Default = 1.0

apparent_mag_1min_meanfloat,

mean magnitude at 1 minute after burst Default = 15.35

apparent_mag_1min_sigmafloat,

std of magnitudes at 1 minute after burst Default = 1.59

trans_durationfloat, optional

How long the transient lasts (days). Default 10.

survey_durationfloat, optional

Length of survey (years). Default 10.

survey_startfloat, optional

MJD for the survey start date. Default None (uses the time of the first observation).

detect_m5_plusfloat, optional

An observation will be used if the light curve magnitude is brighter than m5+detect_m5_plus. Default 0.

n_per_filterint, optional

Number of separate detections of the light curve above the detect_m5_plus theshold (in a single filter) for the light curve to be counted. Default 1.

n_filtersint, optional

Number of filters that need to be observed n_per_filter times, with differences min_delta_mag, for an object to be counted as detected. Default 1.

min_delta_magfloat, optional

magnitude difference between detections in the same filter required for second+ detection to be counted. For example, if min_delta_mag = 0.1 mag and two consecutive observations differ only by 0.05 mag, those two detections will only count as one. (Better would be a SNR-based discrimination of lightcurve change.) Default 0.

n_phase_checkint, optional

Sets the number of phases that should be checked. One can imagine pathological cadences where many objects pass the detection criteria, but would not if the observations were offset by a phase-shift. Default 1.

Methods Summary

light_curve(time, filters)	given the times and filters of an observation, return the magnitudes.
reduce_band1_filt_avg(bandfraction)	Average fraction detected in single filter
reduce_bandany_nfilters(bandfraction)	Fraction of events detected in Nfilters or more
reduce_bandg(bandfraction)
reduce_bandi(bandfraction)
reduce_bandr(bandfraction)
reduce_bandu(bandfraction)
reduce_bandy(bandfraction)
reduce_bandz(bandfraction)
run(data_slice[, slice_point])	"

Methods Documentation

light_curve(time, filters)

given the times and filters of an observation, return the magnitudes.

reduce_band1_filt_avg(bandfraction)

Average fraction detected in single filter

reduce_bandany_nfilters(bandfraction)

Fraction of events detected in Nfilters or more

reduce_bandg(bandfraction)

reduce_bandi(bandfraction)

reduce_bandr(bandfraction)

reduce_bandu(bandfraction)

reduce_bandy(bandfraction)

reduce_bandz(bandfraction)

run(data_slice, slice_point=None)

” Calculate the detectability of a transient with the specified lightcurve.

Parameters:

data_slicenumpy.array

Numpy structured array containing the data related to the visits provided by the slicer.

slice_pointdict, optional

Dictionary containing information about the slice_point currently active in the slicer.

Returns:

float

The total number of transients that could be detected.