SpectroCov#

class cosmicjellyfish.LSSsurvey.spectro_cov.SpectroCov(fiducialpars, fiducial_specobs=None, bias_samples=['g', 'g'], configuration=None)[source]#

Bases: object

Tsys_func(z)[source]#

Calculates Tsys in mK

Parameters:

z (float, numpy.ndarray) – Redshift at which to compute Tsys

Returns:

Tsys at z in milli Kelvin

Return type:

float, numpy.ndarray

volume_bin(zi, zj)[source]#

Calculates the comoving volume of a spherical shell

Parameters:
Returns:

Volume of the comoving spherical shell between zj and zi

Return type:

float, numpy.ndarray

d_volume(ibin)[source]#

Calculates the comoving volume of a redshift bin

Parameters:

i (int) – Index of the survey redshift bin

Returns:

Comoving volume of the redshift bin

Return type:

float

volume_survey(ibin)[source]#

Calculates the survey volume of a redshift bin

Parameters:

i (int) – Index of the survey redshift bin

Returns:

survey volume of the redshift bin

Return type:

float

n_density(zi)[source]#

calculate the comoving number density of the probe

Parameters:

i (int) – Index of the survey redshift bin

Returns:

comoving number density of the probe

Return type:

float

veff(zi, k, mu)[source]#

calculate the effective volume entering the covariance of the galaxy clustering probe

Parameters:

  • zi (float) – Redshift of the inner sphere

  • k (float, numpy.ndarray) – wave number at which the effective volume should be calculated

  • mu (float, numpy.ndarray) – The cosine of angle between the wavevector and the line-of-sight direction.

  • Returns

  • float – The effective volume for a given wavenumber, angle and redshift

  • numpy.ndarray – The effective volume for a given wavenumber, angle and redshift

cov(ibin, k, mu)[source]#

Function to calculate the covariance the galaxy clustering probe

Parameters:

  • ibin (int) – Index of the redshift bin for which the covariance is to be computed

  • k (float, numpy.ndarray) – Wavenumber

  • mu (float, numpy.ndarray) – The cosine of angle between the wavevector and the line-of-sight direction.

Returns:

Covariance of the Galaxy clustering probe

Return type:

float, numpy.ndarray

P_noise_21(z, k, mu, temp_dim=True, beam_term=False)[source]#

Compute the shotnoise of the 21 centimeter intensity mapping probe

Parameters:

  • z (float, numpy.ndarray) – Redshift at which the noise is to be computed

  • k (float, numpy.ndarray) – Wavenumber

  • mu (float, numpy.ndarray) – The cosine of angle between the wavevector and the line-of-sight direction.

  • temp_dim (bool) – If true the Temperature terms is in units of Kelvin^2

  • beam_term (bool) – If true will add the beam term to the computation of the power spectrum

Returns:

Additional shotnoise of the 21 cm intensity mapping

Return type:

float, numpy.ndarray

veff_II(zi, k, mu)[source]#

calculate the effective volume entering the covariance of the line intensity mapping probe

Parameters:

  • zi (float) – Redshift

  • zi – Redshift

  • k (float, numpy.ndarray) – wave number at which the effective volume should be calculated

  • mu (float, numpy.ndarray) – The cosine of angle between the wavevector and the line-of-sight direction.

  • Returns

  • float – The effective volume for a given wavenumber, angle and redshift

  • numpy.ndarray – The effective volume for a given wavenumber, angle and redshift

veff_Ig(zi, k, mu)[source]#

calculate the effective volume entering the covariance of the cross correlation of galaxy clustering and intensity mapping

Parameters:

  • zi (float) – Redshift

  • zi – Redshift

  • k (float, numpy.ndarray) – wave number at which the effective volume should be calculated

  • mu (float, numpy.ndarray) – The cosine of angle between the wavevector and the line-of-sight direction.

  • Returns

  • float – The effective volume for a given wavenumber, angle and redshift

  • numpy.ndarray – The effective volume for a given wavenumber, angle and redshift