SNSLMetric

class rubin_sim.maf.metrics.SNSLMetric(metric_name='SNSLMetric', mjd_col='observationStartMJD', filter_col='filter', night_col='night', m5_col='fiveSigmaDepth', season=[-1], nfilters_min=4, min_season_obs=5, m5mins=None, maps=['DustMap'], **kwargs)

Bases: BaseMetric

Calculate the number of expected well-measured strongly lensed SN (per data_slice).

The number of expected strongly lensed SN detections with a well-measured time delay is given by:

N (lensed SNe Ia with well measured time delay) = 45.7 * survey_area / (20000 deg^2) * cumulative_season_length / (2.5 years) / (2.15 * exp(0.37 * gap_median_all_filter))

where: survey_area: survey area (in deg2) cumulative_season_length: cumulative season length (in years) gap_median_all_filter: median gap (all filters) (in days)

(reference? metric originated from Simon Huber and Phillipe Gris)

Parameters:

metricNamestr, optional

metric name Default : SNCadenceMetric

mjd_colstr, optional

mjd column name Default : observationStartMJD,

filter_colstr, optional

filter column name Default: filter

night_colstr, optional

night column name Default : night

m5_colstr, optional

individual visit five-sigma limiting magnitude (m5) column name Default : fiveSigmaDepth

season: int (list) or -1, optional

season to process (default: -1: all seasons)

nfilters_minint, optional

The number of filters to demand in a season Default: 4.

min_season_obsint, optional

Minimum number of observations per season. Default 5.

m5minsdict, optional

Minimum individual image depth for visit to ‘count’. Default None uses {‘u’: 22.7, ‘g’: 24.1, ‘r’: 23.7, ‘i’: 23.1, ‘z’: 22.2, ‘y’: 21.4}.

mapslist, optional

List of maps to use. Default is the dustmap, to reduce m5 limiting mags accordingly.

Returns:

float

Number of expected well-measured strongly lensed SN

Methods Summary

n_lensed(area, cadence, season_length)	Parameters:
run(data_slice[, slice_point])	Runs the metric for each data_slice

Methods Documentation

n_lensed(area, cadence, season_length)

Parameters:

areafloat

Area in square degrees related to this data_slice (sq deg)

gap_medianfloat

median gap between nights with visits (days) - any filter

cumul_seasonfloat

length of the season or period of consideration (years)

Returns:

float

Number of strongly lensed SN expected in this area

run(data_slice, slice_point=None)

Runs the metric for each data_slice

Parameters:

data_slicesimulation data

slice_point: slice_point(default None)

Returns:

number of SL time delay supernovae