Functions
def	get_Nstar_estimate (nsegs, tref, minStartTime, maxStartTime, prior, detector_names, earth_ephem_file="earth00-40-DE405.dat.gz", sun_ephem_file="sun00-40-DE405.dat.gz")
	Returns N* estimated from the super-sky metric. More...

Function Documentation

◆ get_Nstar_estimate()

def lalpulsar.NstarTools.get_Nstar_estimate	(	nsegs,
		tref,
		minStartTime,
		maxStartTime,
		prior,
		detector_names,
		earth_ephem_file = `"earth00-40-DE405.dat.gz"`,
		sun_ephem_file = `"sun00-40-DE405.dat.gz"`
	)

Returns N* estimated from the super-sky metric.

Nstar is the approximate number of unit-mismatch templates, see https://dcc.ligo.org/P1700455 for further details.

Parameters

nsegs : int Number of semi-coherent segments tref : int Reference time in GPS seconds minStartTime, maxStartTime : int Minimum and maximum SFT timestamps prior: dict For each key in 'Alpha', 'Delta', 'F0', 'F1', 'F2', either a scalar value (not searched over), or a pair of the upper and lower limit. detector_names : list List of detectors to average over, e.g. ['H1']

Returns

Nstar: int The estimated approximate number of templates to cover the prior parameter space at a mismatch of unity, assuming the normalised thickness is unity.

Example

An example for a directed search where the Nstar can be estimated from the metric directly. This estimate is used to define the frequency and spin-down uncertainty. The calculated estimate using get_Nstar_estimate agrees with this input value.

from lalpulsar import NstarTools nsegs = 1 minStartTime = 1000000000 duration = 10 * 86400 maxStartTime = minStartTime + duration tref = minStartTime + .5*duration detector_names = ['H1'] F0 = 30 F1 = -1e-10 F2 = 0 Alpha = 0.5 Delta = 1.5 Nstar = 1e3 DeltaF0 = np.sqrt(Nstar) * np.sqrt(3)/(np.pi*duration) DeltaF1 = np.sqrt(Nstar) * np.sqrt(180)/(np.pi*duration**2) prior = {'F0': [F0-DeltaF0/2., F0+DeltaF0/2], 'F1': [F1-DeltaF1/2., F1+DeltaF1/2], 'F2': F2, 'Alpha': Alpha 'Delta': Delta} print NstarTools.get_Nstar_estimate( nsegs, tref, minStartTime, maxStartTime, prior, detector_names)

1000.00000009

Note

To see detailed information about each set of dimensions Nstar, add the following before calling get_Nstar_estimate()

import logging logging.basicConfig(level=logging.DEBUG)