icecube_tools.point_source_likelihood.point_source_likelihood module

class icecube_tools.point_source_likelihood.point_source_likelihood.EnergyDependentSpatialPointSourceLikelihood(direction_likelihood, ras, decs, energies, source_coord, band_width_factor=3.0)

Bases: object

Calculate the point source likelihood for a given neutrino dataset - in terms of reconstructed arrival directions.

This class is exactly as in PointSourceLikelihood, but without the energy depedence.

get_test_statistic()

Calculate the test statistic for the best fit ns

class icecube_tools.point_source_likelihood.point_source_likelihood.PointSourceLikelihood(direction_likelihood: SpatialLikelihood, energy_likelihood: MarginalisedEnergyLikelihood, ras: Sequence[float], decs: Sequence[float], energies: Sequence[float], ang_errs: Sequence[float], source_coord: Tuple[float, float], which: str = 'both', vary_atmo: bool = False, vary_astro: bool = False, bg_energy_likelihood=None, bg_spatial_likelihood=None, index_prior=None, band_width_factor: float = 5.0, cosz_bins: ndarray | None = None)

Bases: object

Calculate the point source likelihood for a given neutrino dataset - in terms of reconstructed energies and arrival directions. Based on what is described in Braun+2008 and Aartsen+2018.

angular_distance()

get_test_statistic()

Calculate the test statistic for the best fit ns

property source_coord

update_events(ra, dec, reco_energy, ang_err)

Provide new events and call self._select_nearby_events()

class icecube_tools.point_source_likelihood.point_source_likelihood.SimplePointSourceLikelihood(direction_likelihood, event_coords, source_coord)

Bases: object

class icecube_tools.point_source_likelihood.point_source_likelihood.SimpleWithEnergyPointSourceLikelihood(direction_likelihood, energy_likelihood, event_coords, source_coord)

Bases: object

class icecube_tools.point_source_likelihood.point_source_likelihood.SpatialOnlyPointSourceLikelihood(direction_likelihood, event_coords, source_coord)

Bases: object

Calculate the point source likelihood for a given neutrino dataset - in terms of reconstructed arrival directions.

This class is exactly as in PointSourceLikelihood, but without the energy depedence.

Should be removed at some point, this case is already included in the main class with the keyword “which”, defaulting to “both”.

get_test_statistic()

Calculate the test statistic for the best fit ns

class icecube_tools.point_source_likelihood.point_source_likelihood.TimeDependentPointSourceLikelihood(source_coord: Tuple[float, float], data_periods: List[str], ra: Dict, dec: Dict, reco_energy: Dict, ang_err: Dict, energy_llh: Dict | None = None, times: Dict | None = None, path=None, index_list=None, vary_atmo: bool = False, vary_astro: bool = False, which: str = 'both', emin: float = 10.0, emax: float = 1000000000.0, min_index: float = 1.5, max_index: float = 5.0, new_reco_bins: ndarray = array([1., 1.33333333, 1.66666667, 2., 2.33333333, 2.66666667, 3., 3.33333333, 3.66666667, 4., 4.33333333, 4.66666667, 5., 5.33333333, 5.66666667, 6., 6.33333333, 6.66666667, 7., 7.33333333, 7.66666667, 8., 8.33333333, 8.66666667, 9.]), sigma: float = 2.0, band_width_factor: float = 5.0)

Bases: object

property N

property N_dict

property Nprime

property Nprime_dict

property Ntot

property Ntot_dict

get_test_statistic()

Calculate test statistic

ns_to_flux(ns: float, index: float)

Convert some given ns and spectral index to the average flux over the detector livetime.

reset_events(ra: Dict, dec: Dict, reco_energy: Dict, ang_err: Dict)

property source_coord