2
include the effect of bias in the nonlinear perturbation theory
(for a recent review, see Ref. [14]). Understanding the bias
from the first principle is extremely difficult because of the
full nonlinearity of the problem and extremely complicated
astrophysical processes in the galaxy formation, and so forth.
The concept of bias has usually been discussed in the con-
text of number density fields of astronomical objects such as
galaxies, as probes of the underlying matter density field. In
this case, the bias corresponds to a function, or more prop-
erly a functional, of the underlying mass density field to give
a number density field of the biased objects. Thus the func-
tion(al) of the bias has a scalar value in accordance with that
the density of biased objects is a scalar field. In the previ-
ous work of Paper I [1], the concept of bias in the nonlinear
perturbation theory is generalized to the case that the bias is
given to a tensor field. The number densities of objects are
not the only probes of the density fields in the LSS. For ex-
ample, galaxy spins and shapes are in principle determined by
the mass density fields through, e.g., tidal gravitational forces,
and other physical quantities. Recently, interests in statistics
of the galaxy sizes and shapes, or intrinsic alignments, are
growing as probes of the LSS of the Universe [15–19], and
analytical modelings of galaxy shape statistics by the nonlin-
ear perturbation theory have also been introduced [20–24].
Motivated by these recent developments, we generalize the
nonlinear perturbation theory to predict statistics of biased
fields with an arbitrary rank of tensor in Paper I [1]. We adopt
the spherical decomposition of the tensor field, which plays an
important role in the formalism. This method of decomposi-
tion has been already adopted in the perturbation theory in lit-
erature to investigate the clustering of density peaks [25] and
galaxy shapes [23]. In the last two references, the coordinates
system of the spherical basis is chosen so that the third axis is
aligned with a radial direction of the correlation function, or a
direction of wave vector of perturbations in Fourier space. In
contrast, we do not fix the coordinates system in the spherical
basis, and explicitly keep the rotational covariance apparent
throughout the formulation. The basic formalism to calculate
the power spectrum and higher-order spectra of tensor fields
of arbitrary ranks by the nonlinear perturbation theory to arbi-
trary orders is described in Paper I.
Many different methods have been considered in the liter-
ature to include the bias in the nonlinear perturbation theory
[14]. Most methods rely on a local or semilocal ansatz of the
bias function which relates the mass density field and the bi-
ased density field. The locality or semilocality of the relation
is given in either Eulerian or Lagrangian space of the density
field. However, (semi)local biases in Eulerian and Lagrangian
spaces are not compatible with each other in general, because
the dynamically nonlinear evolution by gravity is essentially
nonlocal. Therefore, the bias relation should be given by a
nonlocal functional, in either Eulerian or Lagrangian space,
and the (semi)local Ans¨atze are approximations to the reality.
A general formulation to systematically incorporate the non-
local bias into the nonlinear perturbation theory is provided
by the integrated perturbation theory (iPT) [26, 27]. The local
and semilocal Ansatz of the bias can also be derived from this
formulation by restricting the form of bias functional in the
class of local or semilocal function. Moreover, the iPT also
provides a natural way of including the effect of redshift space
distortions, which should be taken into account for predicting
observable statistics in redshift surveys. Our formulation of
Paper I is built upon and generalizes the method of iPT and
establishes a nonlinear perturbation theory of tensor fields in
general. Paper I describes the basic formulation of the theory
and gives some results of lowest-order approximations of the
perturbation theory.
In this second paper of the series, we apply the formula-
tion of Paper I to concretely calculate the one-loop correc-
tions of the perturbation theory. The strategy of the calcula-
tion is fairly straightforward according to Paper I. Some tech-
niques are introduced to reduce the higher-dimensional inte-
grals to the lower ones, which are generalizations of an ex-
isting method using a fast Fourier transform applied to the
nonlinear perturbation theory [28]. In particular, all the nec-
essary integrations to evaluate the one-loop corrections in the
perturbation theory with the (semi)local models of tensor bias
reduce to essentially one-dimensional Hankel transforms. As
an illustrative example, we calculate the power spectrum and
correlation function with one-loop corrections for a simple
model of a rank-2 tensor which is biased from spatially second
derivatives of the gravitational potential in Lagrangian space.
This paper is organized as follows. In Sec. II, the propa-
gators, elements of the nonlinear perturbation theory, in the
spherical basis of our formalism are calculated, up to neces-
sary orders for evaluating one-loop corrections of the power
spectrum and correlation function. In Sec. III, our main result,
the one-loop approximations of the power spectra of the ten-
sor field are explicitly derived in analytic forms, both in real
space and in redshift space. In Sec. IV, a simple example of
the tensor bias with a semilocal model is explicitly calculated
and numerically evaluated. Conclusions are given in Sec. V.
In the Appendix, a formal expression of the all-order power
spectrum of the tensor field is derived beyond the one-loop
approximation.
II. PROPAGATORS OF TENSOR FIELDS AND LOOP
CORRECTIONS
The fundamental formulation of the iPT of tensor fields is
described in Paper I [1]. One of the essential ingredients of the
theory is the evaluation of propagators, with which statistics of
tensor fields, such as the power spectrum, bispectrum, corre-
lation functions, etc. are represented. Several examples in rel-
atively simple cases with lowest-order approximation are ex-
plicitly derived in Sec. V of Paper I. In this section, we further
derive the propagators that are required to evaluate next-order
approximation with loop corrections. We cite many equations
from Paper I, which readers are assumed to have in hand.
A. Invariant propagators
The propagators of tensor fields can be represented by ro-
tationally invariant functions as well as the renormalized bias