A&A 465, 725-747 (2007)
DOI: 10.1051/0004-6361:20066832
P. Valageas
Service de Physique Théorique, CEA Saclay, 91191 Gif-sur-Yvette, France
Received 28 November 2006 / Accepted 26 January 2007
Abstract
We develop a path-integral formalism to study the formation of large-scale
structures in the universe. Starting from the equations of motion of
hydrodynamics (single-stream approximation) we derive the action which
describes the statistical properties of the density and velocity fields
for Gaussian initial conditions. Then, we present large-N expansions
(associated with a generalization to N fields or with a semi-classical
expansion) of the path-integral defined by this action. This provides a
systematic expansion for two-point functions such as the response function
and the usual two-point correlation. We present the results of two such
expansions (and related variants) at one-loop order for a SCDM and
a CDM cosmology.
We find that the response function exhibits fast oscillations in the non-linear
regime with an amplitude which either follows the linear prediction (for the
direct steepest-descent scheme) or decays (for the 2PI effective action
scheme). On the other hand, the correlation function agrees with the
standard one-loop result in the quasi-linear regime and remains well-behaved
in the highly non-linear regime. This suggests that these large-N expansions
could provide a good framework to study the dynamics of gravitational
clustering in the non-linear regime. Moreover, the use of various expansion
schemes allows one to estimate their range of validity without the need of
N-body simulations and could provide a better accuracy in the weakly
non-linear regime.
Key words: gravitation - cosmology: theory - cosmology: large-scale structure of Universe
The large-scale structures we observe in the present universe (such as galaxies and clusters of galaxies) have formed thanks to gravitational instability (Pebbles 1980) which amplified the small density perturbations created in the early universe (e.g. through quantum fluctuations generated during an inflationary phase, Liddle & Lyth 1993). Moreover, the power increases at small scales as in the CDM model (Peebles 1982) which leads to a hierarchical scenario where small scales become non-linear first. Then, small structures merge in the course of time to build increasingly large objects. Therefore, at large scales or at early times one can use a perturbative approach to describe the density field (e.g. Bernardeau et al. 2002, for a review). This is of great practical interest for observations such as weak-lensing surveys (e.g. Bernardeau et al. 1997) and CMB studies (e.g. Seljak & Zaldarriaga 1996). At smaller scales one uses N-body numerical simulations to obtain fits to the density power-spectrum (e.g. Smith et al. 2003) which can be compared with observations.
In the weakly non-linear regime one may apply the standard perturbative
expansion over powers of the initial density fluctuations (e.g., Fry 1984;
Goroff et al. 1986). This procedure is used within the hydrodynamical
framework where the system is described by density and velocity fields
rather than by the phase-space distribution
,
that is one
starts from the Euler equation of motion rather than the Vlasov equation.
This is quite successful at tree-order where one can
obtain the leading-order contribution to all p-point correlations and
reconstruct the density probability distribution (Bernardeau 1994), assuming
Gaussian initial fluctuations as in usual inflationary scenarios
(Liddle & Lyth 1993). However, several problems show up when one tries to
compute higher-order corrections. First,ultraviolet divergences appear
for linear power-spectra
with
(Scoccimarro & Frieman 1996b) which would break the self-similarity seen
in numerical simulations. This may also be interpreted as a consequence of
the breakdown of the hydrodynamical description beyond shell-crossing
(Valageas 2002). However, for n<-1 the one-loop correction yields a good
agreement with numerical simulation (Scoccimarro & Frieman 1996b). Secondly,
being an expansion over powers of
this standard perturbative approach
yields a series of terms which grow increasingly fast into the non-linear
regime at higher orders and cannot relax at small scales. This makes the
series badly behaved and it seems difficult to go deeper into the non-linear
regime in this manner.
On the other hand, a good description of the weakly
non-linear regime becomes of great practical interest as cosmological probes
now aim to constrain cosmological parameters with an accuracy of the order
of
to keep pace with CMB observations. In particular, weak-lensing surveys
mainly probe this transition regime to non-linearity and a good accuracy
for the matter power-spectrum is required to make the most of observations and
constrain the dark energy equation of state (Munshi et al. 2007).
Another probe of the expansion history of the Universe is provided by the
measure of the baryon acoustic oscillations which also requires a good
description of weakly non-linear effects
(Seo & Eisenstein 2007; Koehler et al. 2007).
As a consequence, it is worth investigating other approaches which may partly cure some of these problems and provide a better description of the non-linear regime. For instance, McDonald (2006) proposed a renormalization group method to improve the standard perturbative approach. On the other hand, Crocce & Scoccimarro (2006a,b) presented a diagrammatic technique to organize the various terms which arise in the standard perturbative expansion and to perform some infinite partial resummations. Besides, using the asymptotic behavior of the vertices of the theory they managed to complete the resummation in the small-scale limit for the response function R (also called the propagator).
In this article, we apply to the hydrodynamical framework the method
presented in Valageas (2004) for the Vlasov equation. This approach
introduces a path-integral formalism to derive the statistical properties
of the system from its action S. Then, one applies a "large-N expansion''
(which can be seen as similar to the semi-classical expansion over powers
of
or can be derived from a generalization to N fields) to compute
the quantities of interest such as the two-point correlation.
This offers a different perspective to address the non-linear regime of
gravitational clustering which complements other approaches and may also
serve as a basis for other approximation schemes than the ones described in this
article.
For instance, previous approaches can be recovered by expanding the path-integral
obtained in this paper in different manners (e.g. the usual perturbative expansion
corresponds to the expansion over the non-Gaussian part of the action S). The two
large-N expansions discussed in this article are two other means of performing
systematic expansions to compute the required correlation functions. This also
amounts to reorganize the standard perturbative expansion by performing some partial
infinite resummations. In this fashion, one can hope to obtain new expansion schemes
which are better suited to describe the non-linear regime of gravitational
clustering.
First, we recall in Sect. 2 the equations of motion
obtained within the hydrodynamical framework. Next, we derive the path-integral
formulation of the problem in Sect. 3 and we describe
two possible large-N expansions in Sect. 4.
Finally, we present our numerical results in
Sects. 5-7,
focusing on a critical-density universe. Since our formalism applies equally
well to any cosmology we describe our results for a CDM universe
in Sect. 8. Finally, we conclude in Sect. 9.
At scales much larger than the Jeans length both the cold dark matter and the
baryons can be described as a pressureless dust. Moreover, before orbit
crossing one can use a hydrodynamical approach (in the continuum limit where
the mass m of particles goes to zero). Then, at scales much smaller than the
horizon where the Newtonian approximation is valid, the equations of motion
read (Peebles 1980):
At large scales or at early times where the density and velocity fluctuations
are small one can linearize the equation of motion (11) which yields
.
Then, the linear growing mode is merely:
As in Valageas (2004) we can apply a path-integral approach to the
hydrodynamical system (11) since we are only interested in the
statistical properties of the density and velocity fields (we do not look for
peculiar solutions of the equations of motion). Let us briefly recall how this
can be done (also Martin et al. 1973; Phythian 1977).
In order to include explicitly the initial conditions we rewrite
Eq. (11) as:
The path-integral (35) can be computed by expanding
over powers of its non-Gaussian part (i.e. over powers of ). This
actually yields the usual perturbative expansion over powers of the
linear power-spectrum
(see also Valageas 2001, 2004, for the
case of the Vlasov equation of motion).
On the other hand, the path-integral (35) may also be studied
through a large-N expansion as in Valageas (2004). Thus, one considers
the generating functional ZN[j,h] defined by:
A first approach to handle the large-N limit of
Eq. (38) is to use a steepest-descent method (also called a
semi-classical or loopwise expansion in the case of usual Quantum field
theory with ). This yields for auxiliary correlation and response
functions G0 and R0 the equations (Valageas 2004):
As described in Valageas (2004) a second approach is to first introduce the
double Legendre transform
of the functional
(with
respect to both the field
and its two-point correlation G) and next
to apply the 1/N expansion to
.
This "2PI effective action''method yields the
same Eqs. (42)-(44) and the self-energy shows the same
structure as (45)-(46) where G0 and R0 are replaced by G and R. Thus, the direct steepest-descent method yields a series of
linear equations which can be solved directly whereas the 2PI effective
action method gives a system of non-linear equations (through the dependence
on G and R of
and
)
which must usually be solved numerically
by an iterative scheme. However, thanks to the Heaviside factors appearing
in the response R and the self-energy
these equations can be solved
directly by integrating forward over time
.
As discussed in Valageas (2004)
both the steepest-descent and 2PI effective action methods agree with the
standard perturbative analysis over powers of
up to the order
used for the self-energy (e.g. up to order
if we only consider
the one-loop terms (45)-(46)). As compared with the standard
perturbative approach, the two schemes described above also include
two different infinite partial resummations, as can be seen from
Eqs. (42)-(44) which clearly generate terms at all orders over
for G and R even if
and
are only linear or
quadratic over
.
We can note that the equations we obtain for the hydrodynamical system (1)-(3) are simpler than for the collisionless
system studied in Valageas (2004) which is described by the Vlasov-Poisson
system. Indeed, here the mean
vanishes at all orders.
This can be explicitly
checked from Eqs. (1)-(3) and in the derivation
of Eqs. (39)-(46).
This is not the case for the Vlasov dynamics where Eq. (47)
does not hold.
Using a diagrammatic technique Crocce & Scoccimarro (2006a,b) derived
Eqs. (42)-(46) in an integral form (i.e. without the
differential operator
in the l.h.s.) by first integrating the equation
of motion (11). Of course, our path-integral approach can also
be applied to the integral form of Eq. (11). This amounts to
write Eq. (11) as
where
is the linear growing mode and
the integral form
of the vertex
.
Then, the procedure presented in Sect. 3
can be applied to this integral form of the equation of motion, as described
for instance in Valageas (2001). In this article we preferred to keep
Eq. (11) in its differential form so that the r.h.s. does not contain
an integral over time. Then the self-energy terms
and
only depend on the response and correlation at the
same times
,
see Eqs. (45), (46). By contrast,
the integrated vertex
would lead to self-energies which depend
on the values of the response and correlation at all past times which would
entail less efficient numerical computations.
Thanks to statistical homogeneity and isotropy the matrices G0,G and are symmetric and of the form:
Alternatively, Crocce & Scoccimarro (2006a) pointed out that the expression (51) is somewhat similar to the result obtained within a phenomenological halo model (Seljak 2000). Indeed, the latter model splits the non-linear power-spectrum into two terms, one that dominates in the linear regime (2-halo term) and the other that dominates in the highly non-linear regime (1-halo term). They could be identified with the first and second terms of Eq. (51). However, it is not clear whether the analogy can be pursued much further. In particular, Eq. (51) is explicitly dynamical (i.e. it explicitly involves integrations over past events) whereas the halo model gives a static expression (it writes the two-point correlation as integrals over the current halo distribution, which is the basic time-dependent quantity).
Note that within the 2PI effective action method, even if R would be
treated independently of G the expression (51) does not give a
quadratic dependence of G on R since
depends quadratically on G.
Besides, solving Eq. (51) perturbatively over G0 at fixed R
actually generates terms at all orders q over powers G0q.
By contrast, in the steepest-descent approach where
only depends on G0the expression (51) is fully explicit and quadratic over both
and R if the latter are treated independently. This is a
signature of the additional resummations involved in the 2PI effective action
method as compared with the direct steepest-descent approach.
An advantage of Eq. (51) is that once
and R are known, we obtain an explicit expression for G. Hence
we do not need to solve Eq. (42) (by solving
for R we have already performed the "inversion'' of
).
A second advantage of Eq. (51) is that it provides an expression for
G which is clearly symmetric. Moreover, since we start from a two-point
correlation
which is positive, as shown by Eq. (21),
we see from Eqs. (46) and (51) that both
and G are
positive. This also holds for the 2PI effective action approach which may be
obtained by iterating the system (42)-(46) (substituting G and
R into the self-energy).
Using the symmetry (49) for
and the form (50) for
we see that
and
only need to be computed at
times
.
Besides, thanks to the various Dirac factors
the expressions (45)-(46) only involve an integral over one
wavenumber
.
Thus, using the expression (14) of the kernel
we can write from Eqs. (45), (46) the self-energy as:
At equal times
we can obtain from Eq. (53):
We present in the following sections our numerical results which we compare
to fits obtained from direct numerical simulations and to the standard
perturbative analysis.
We consider an Einstein-de-Sitter cosmology with
,
a shape parameter
and a normalization
for the linear power-spectrum
and a Hubble constant
H0=50 km s-1 Mpc-1 (i.e. the reduced Hubble parameter is h=0.5)
as in Smith et al. (2003) so as to compare our results with N-body simulations.
Since we mainly wish to investigate in this
article the properties of the large-N expansions described in
Sect. 4 we first focus on the Einstein-de-Sitter
cosmology where both
and
have simple analytical forms (21)-(24). We shall discuss the case of a
CDM cosmology
in Sect. 8 below.
We first study in this section the results obtained from the direct steepest-descent method of Sect. 4.1 which involves the auxiliary two-point functions G0 and R0. Since the latter are equal to their linear counterparts (47) the correlation G0 is given by Eq. (21) whereas R0 is obtained by solving Eq. (40) forward in time, in agreement with the discussion below Eq. (23). For the Einstein-de-Sitter cosmology which we consider here R0 is actually given by the explicit expression (24).
From G0 and R0 we need to compute the self-energy from
Eqs. (45), (46).
In fact, for the Einstein-de-Sitter cosmology the time-dependence of the
self-energy can be factorized as follows using Eqs. (21), (24).
First, the auxiliary response R0 can be written from Eq. (24) as:
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Figure 1:
The self-energy terms
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Figure 2:
The self-energy
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We show in Fig. 2 the evolution forward over time
of the
self-energy
for time
(i.e. z2=3) and wavenumbers k=0.1,1 and
Mpc-1
(from bottom to top).
The absolute value of the self-energy is larger for higher k in agreement
with Fig. 1.
All components are negative (except for
at
in the case k=0.1 h Mpc-1) and exhibit a smooth dependence with time
following Eq. (59) which goes to
at large
.
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Figure 3:
The self-energy
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We show in Fig. 3 the evolution backward over time
of the
self-energy
for time
(i.e. z1=0) and wavenumbers k=0.1,1 and
Mpc-1
(from bottom to top). The behavior agrees with
Figs. 1, 2.
Following Eq. (59)
converges to a constant for
.
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Figure 4:
The self-energy ![]() ![]() |
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Finally, using the factorized form (21) of the auxiliary two-point
correlation the self-energy
can be written as:
Since the time-dependence (65) is quite simple we only display
the self-energy term
of Eq. (66) as a function of
wavenumber k in Fig. 4.
At high k all components are very close and positive.
The dependence on wavenumber k follows the smooth growth at small scales
of the logarithmic power
.
As for
the self-energy term
decreases very fast at low
k so that the two-point functions obtained from Eqs. (42)-(44)
will converge to the linear asymptotics at large scales.
From the self-energy obtained in Sect. 5.1 we compute the
response function from Eq. (43). Thanks to the Heaviside
factor within the term
the r.h.s. only involves earlier times
hence Eq. (43) can be integrated forward over time
at
fixed
to give
.
Note that each wavenumber k
evolves independently thanks to the Dirac factors
within both
and R as in Eq. (50). Thus all processes
associated with mode-coupling between different wavenumbers
are
contained in the calculation of the self-energy
(and
),
as in Eqs. (60), (66).
Equation (43) also writes for
:
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Figure 5:
The non-linear response
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We can note that Eqs. (70), (71) are somewhat similar to
Bessel functions in terms of the scale factor
but are of
degree three rather than two. In the small-scale limit
we can look for an asymptotic solution of
the form:
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Figure 6:
The non-linear response
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Figure 7:
The non-linear response function
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Figure 8:
The density two-point correlation
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We first display in Fig. 5 the evolution forward over time
of the non-linear response
for wavenumbers k=1
(left panel) and
Mpc-1 (right panel). We do not show the case
k=0.1 h Mpc-1 where the response R closely follows the linear response
given in Eq. (24). In agreement with the analysis performed
through Eqs. (75)-(80) we find that the non-linear response
R exhibits oscillations
with a frequency
which grows at higher k and an amplitude which follows the linear response
.
Moreover, all components Rij oscillate in phase.
Next, we show in Fig. 6 the evolution backward over time of the non-linear response R. It shows a behavior similar to the
forward evolution displayed in Fig. 5 with respect to
.
At large scales we recover the smooth increase (in absolute values) at early
times
of linear theory (24) whereas at small scales we obtain
fast oscillations
related to
Eqs. (75)-(80) with an amplitude which follows again
the linear response
.
Finally, since at equal times the response function obeys
Eq. (23) we only display in Fig. 7 the response at
unequal times
.
At low k we recover the linear
response
.
At higher k above 0.2 h Mpc-1 the non-linear response obtained
at one-loop order within this steepest-descent approach departs from the
linear prediction and shows oscillations with increasingly high frequencies
while their amplitude follows the linear response
at high k.
This behavior is a result of the oscillatory behavior with time
displayed in Fig. 5 above and analyzed in
Eqs. (75)-(80).
Finally, from Eq. (51) we compute the non-linear two-point correlation
function G obtained through the steepest-descent method at one-loop order.
We compare our results in Figs. 8-11 at redshifts
z=0,3 with a fit
from numerical simulations (Smith et al. 2003),
the linear prediction
(Eq. (21)) and the usual one-loop result
obtained from the standard perturbative expansion over powers
of the linear density field.
The standard one-loop power-spectrum can be written
(e.g., Jain & Bertschinger 1994; Scoccimarro & Frieman 1996a,b) as:
The dependence
merely reflects the order
of the
perturbative expansion. In order to go deeper into the non-linear regime one
may consider higher order terms but the latter grow increasingly fast and
one would need at least partial resummations to obtain a well-behaved series
(e.g. Crocce & Scoccimarro 2006a). This is precisely what the large-N
expansions discussed in this article attempt to perform.
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Figure 9:
The logarithmic power
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Figure 10:
The logarithmic power
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Figure 11:
The logarithmic power
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We first show in Fig. 8 the evolution over time
of
the density two-point correlation
,
which is
symmetric with respect to
.
We actually
display the "logarithmic power''
defined as
in Eq. (19) by:
The oscillations show that G11 can indeed be negative at unequal times.
At these small scales the non-linear power
is already significantly
different from the linear power
but we can check in the left panel
that
matches the usual one-loop power
of Eq. (81) at small
.
As recalled in
Sect. 4.3 the one-loop large-N expansions indeed match
the usual perturbative expansion up to one-loop order (i.e.
). At later
times while
grows very fast as
from
Eq. (83) the prediction
of the one-loop steepest-descent
approach remains well-behaved of order
.
Thus, we can see in the right panel that at very small scales where
has become exceedingly large
remains
well-controlled. In fact, following the behavior of the response function
analyzed in Sect. 5.2 it exhibits fast oscillations with
an amplitude close to
.
We also show in Fig. 8
the power
obtained from the first term alone of Eq. (51)
which is detailed in Eq. (52). This corresponds to the "non-linear
transport'' of the initial linear fluctuations without taking into
account the fluctuations
generated at all times by the non-linear
dynamics. We can see that this term is sub-dominant in the mildly non-linear
regime (left panel) but happens to be again of same order as
in the highly non-linear regime.
Next, we show in Fig. 9 the logarithmic power
today at equal redshifts z1=z2=0 (i.e. equal times
).
In agreement with Eqs. (51), (52) and the subsequent discussion
both
and
are positive (
is the square of
an oscillating function).
At large scales
converges to the linear regime
.
At smaller scales we can check again that
first follows the usual
one-loop power
and next goes back to
whereas
keeps increasing very fast in magnitude.
Unfortunately,
does not agree with the fit
from
numerical simulations (Smith et al. 2003) on a larger range of k as compared
with
(although the rough agreement between
and
at small scale is purely
coincidental).
On the other hand, whereas adding higher-order terms in the standard
perturbative expansion may not improve much the agreement with N-body
simulations (especially since it would lead to increasingly steep terms at
high k) the series obtained in the large-N approach is likely to be
well-behaved as various terms do not "explode'' at small scales.
However, this would require intricate calculations.
We can note that although
is of
order
one cannot neglect the fluctuations
generated by
the non-linear dynamics and the sum yields a smooth power
.
We also display in Fig. 10 the logarithmic power
at equal redshifts z1=z2=3. We recover the same behaviors as those
obtained at redshift z=0.
Finally, we show in Fig. 11 the logarithmic power
at unequal times
,
that is
at redshifts
z1=0,z2=3. We find again that
matches the usual
one-loop power
at large scales and follows its change
of sign at
Mpc-1. Note indeed that at unequal times
need not be positive. At smaller scales, the steepest-descent
large-N result
departs from the fast growing
and shows
a series of oscillations of moderate amplitude, which again follow
.
These features suggest that the first change of sign in
Fig. 11, which is common to both
and
,
may be real and not a mere artefact of the
one-loop expansion. Note that in spite of the oscillations with time
seen in Figs. 8, 11 the large-N approach
automatically ensures that
at equal times thanks to the
structure of Eq. (51). This property holds at all orders over 1/N
since Eq. (51) is independent of the expressions of the self-energies
.
We now present the results obtained at one-loop order from the 2PI
effective action approach recalled in Sect. 4.2.
Thus, we need to solve the system of coupled Eqs. (42)-(46)
where in the expression (45)-(46) of the self-energy the
auxiliary two-point functions G0 and R0 are replaced by G and R.
Thanks to causality, which leads to the Heaviside factor
of Eq. (50) within both R and
,
we solve the system (42)-(46) by moving forward over time.
Thus, we use a grid
with
for the time variables. At the earliest time-step
we initialize
all matrices by their linear value at
.
Next, once we have obtained all two-point functions
up to time
(i.e. over
and
,
initially n=1) we advance to the next time-step
as follows. The response R at equal times
is first obtained from Eq. (23).
Next, we move backward over time
at
fixed
by using Eqs. (43) and (45).
That is, to compute
with i=n,..,1 we use for
each value of i the integro-differential Eq. (43) to move over
from
up to
at fixed
.
Thanks to the Heaviside factors the r.h.s. of
Eq. (43) only involves
and
with
which are already known. Besides, once
has been obtained we compute
from Eq. (45) before moving downward
to step i-1. Since R and
vanish for
we have
actually obtained in this fashion R and
for all
(indeed
for
).
Next, we compute
and G from Eqs. (46) and (51).
At fixed
we now move forward over time
.
Indeed, the r.h.s of Eq. (51)
only involves
with
and
which is already known. Besides, once
has been obtained we compute
from Eq. (46) before moving forward
to step i+1 and we use the symmetry (49) to derive
as well as
.
In fact, at each step i for
the integrals
in the r.h.s. of Eqs. (43), (51) also involve the values
and
which are
being computed if we use the boundary points in the numerical computation
of the time-integrals. Rather than using open formulae for the integrals we
perform a few iterations at each time-step
.
This procedure
converges over a few loops.
Finally, to speed-up the numerical computation we use finite elements over
Fourier space
to store the structure of the self-energy terms
and
.
Thus, since all matrices only depend on the wavenumber modulus
we use a grid k(n) with
n=1,...,Nk. Next, in order to
interpolate for all values of k we can write for instance any matrix such
as G(k) as:
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Figure 12:
The non-linear response
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We first display in Fig. 12 the evolution forward over time of the response
.
We can see that the non-linear response
exhibits oscillations as for the steepest-descent result of Fig. 5
but its amplitude now decays at large times
instead of following the
linear envelope. This can be understood analytically from the
following simple model. Let us consider the equation:
On the other hand, we can see in Fig. 12 that the first quarter
of oscillation, from 1 to 0, of the response R follows rather closely
the curves labeled "sd'' obtained from the steepest-descent method
presented in Sect. 5 which were also shown in
Fig. 5. Besides this agreement remains valid at quite large
k (right panel) where two-point functions obtained from both methods
are generically very different as shown by the figures below.
This property can be understood from Eq. (56) which shows
that the dependence on wavenumber
of the self-energy
at equal times is identical for both approaches
(at one-loop order). Moreover, Eq. (56) shows that the
normalization is governed by the value of the two-point correlation
at the point where the logarithmic slope is n=-1(this integral actually corresponds to the mean square velocity
). At redshift z=0 this wavenumber is still within the linear
regime (for the SCDM cosmology which we consider here) therefore the
self-energy
at equal times
obtained within the
steepest-descent method and the 2PI effective action approach are very close
until z=0. Then, the early time-evolutions at
of the
response R obtained within both methods from Eq. (43) are
very close. We can see from Fig. 12 that this agreement holds
until the response R first vanishes. Beyond this point the steepest-descent
method yields increasingly large oscillations (Fig. 5) whereas
the 2PI effective action yields small oscillations which decay to
zero (Fig. 12).
Next, we show in Fig. 13 the evolution backward over time of the response R. In a manner consistent with Fig. 12 it
exhibits oscillations which are strongly damped at large time separations
|a1-a2|, whence at early times
,
while the frequency is also
larger than for the steepest-descent result shown in Fig. 6.
Again the behavior at nearly
equal times
is close to the prediction of the
steepest-descent method presented in Sect. 5
until the response first vanishes. The response at unequal times
is shown as a function of wavenumber k in
Fig. 14. At large scales we recover the linear regime whereas at small
scales we obtain oscillations which show a fast decay into the non-linear
regime. This is consistent with the time-dependence displayed in
Figs. 12, 13.
Therefore, in contrast with the
steepest-descent approach we find that within the 2PI effective action method
the memory of initial conditions is in some sense erased as the response
function decays for large time separation. This agrees with the intuitive
expectation that within the real collisionless gravitational dynamics
the details of the initial conditions are erased at small scales. Indeed,
after shell-crossing one can expect for instance that virialization processes
build halos which mainly depend on a few integrated quantities which
characterize the collapsed region (such as the mass, initial overdensity,
angular momentum, ...) and exhibit relaxed cores (e.g. isotropic velocity
distributions), as suggested by numerical simulations. However, we must note
that the system studied in this paper is defined from the hydrodynamical
equations of motion (1)-(3) which break
down at shell-crossing. Therefore, there is no guarantee a priori that
such small-scale relaxation processes should come out from
Eqs. (1)-(3). It appears through the
2PI effective action method presented in this paper and
Figs. 12-14 that this is actually the case. In fact,
studying the large-k behavior of the kernel
Crocce & Scoccimarro
(2006b) managed to resum all dominant diagrams in this large-k limit
and obtained a Gaussian decay
.
In our case, the
simple model (95) suggests that the one-loop 2PI effective action
method only yields a power-law decay as in Eq. (104). Since at the
one-loop order considered in this article we do not resum all diagrams it is
not really surprising that we do not recover the "exact'' Gaussian decay.
However, note that the large-N expansion does not give a mere Taylor series
expansion of such a Gaussian decay (which would blow up at large k or large
times) but already captures at a qualitative level the damping of the response
R at small scale or at large time separation thanks to the non-linearity of
the evolution equation obeyed by R.
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Figure 13:
The non-linear response
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Figure 14:
The non-linear response function
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Figure 15:
The self-energy
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Figure 16:
The self-energy
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Figure 17:
The self-energy ![]() |
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We display in Figs. 15 and 16 the evolution with times
and
of the self-energy
.
At large scales
(k<0.1 h Mpc-1) and small time separations we recover the results
obtained within the direct steepest-descent method but at smaller scales
we obtain a series of oscillations with an amplitude which decreases at
large time separations.
This follows from the behavior of the non-linear response R analyzed in
Figs. 12-14 above.
Indeed, the self-energy
depends linearly on the response R, see
Eq. (93). Again the behavior at nearly equal times
is similar for both approaches (see in particular
the left panel of Fig. 15).
The right panel of Fig. 15 shows that at high wavenumbers the
envelope of the oscillations of
decays as a power-law of
at late times. This agrees with the analysis of the simple
model (95).
In a similar fashion, Fig. 17 which displays the dependence
on wavenumber k of
,
at fixed times
,
matches the results obtained from the
direct steepest-descent method at low k (except for
)
and
exhibits decaying oscillations at high k.
We can note in Fig. 17 that the term
(dotted line)
does not match the steepest-descent result in the limit
.
This is due to the behavior of the associated kernels
of Eq. (91). More precisely,
using Eqs. (15)-(15), Eq. (53) reads in this case:
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|
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(105) |
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(108) |
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Figure 18:
The density two-point correlation
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Figure 19:
The logarithmic power
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Figure 20:
The logarithmic power
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Finally, we present in this section our results for the two-point correlation
G and the self-energy .
We first display in Fig. 18 the
evolution with time
of the density "logarithmic power''
defined in Eq. (87). As shown in the left
panel at early times and large scales we recover the steepest-descent result
since we must also match the usual one-loop expansion (81).
At later times or at smaller scales we obtained oscillations which are
strongly damped at large time separations in contrast with Fig. 8.
This difference of behaviors of the correlations G between the direct
steepest-descent method and the 2PI effective action scheme follows from the
difference already seen in terms of the response R analyzed in
Sect. 6.1.
The right panel of Fig. 18 is particularly interesting as it shows
that in the highly non-linear regime the two-point correlation exhibits a peak
at equal times
but whereas both the standard one-loop
expansion and the steepest-descent method yield large oscillations
at larger time separations the 2PI effective action approach leads to a
strong damping. Thus, this is the only method (among those presented in this
paper) which yields (at one-loop order)
a damping of correlations at small scales and large time separations which
is expected to reflect the qualitative behavior of the exact gravitational
dynamics.
Next, we show in Figs. 19, 20 the logarithmic power
at equal redshifts z1=z2=0 and z1=z2=3as a function of wavenumber k.
We find again that our results match the steepest-descent prediction as well
as the usual one-loop power (81) at large scales.
At small scales, despite the damping at unequal times shown in
Fig. 14 for the response function and in Fig. 18 for the
correlation function we obtain a steady growth of the power
in between the linear prediction
and the usual one-loop
prediction
.
Besides, the agreement with the results
from numerical simulations is better than for the steepest-descent prediction.
Of course, because of the damping of the response R at large time
separations analyzed above we can check that the contribution
from Eq. (52) associated with the transport of the initial fluctuations
becomes negligible in the non-linear regime (contrary to what happens within
the steepest-descent approach, see Figs. 9, 10).
However, the continuous generation of fluctuations, described by the
self-energy
in Eq. (51) and associated with the non-linearity
of the dynamics, sustains a high level of density fluctuations
into the non-linear regime. The latter seen at equal times
are related to the values of
at nearly equal times
.
We show in Fig. 21 the logarithmic power
at unequal times as a function of wavenumber k.
We find again that our result matches the steepest-descent prediction as well
as the usual one-loop power (81) at large scales.
However, the correlation now quickly decays at small scales in the non-linear
regime. This follows from the damping at large time separations analyzed above
for the response function which leads to a decorrelation at small scales and
large time separations.
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Figure 21:
The logarithmic power
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Figure 22:
The self-energy
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Figure 23:
The self-energy ![]() |
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Figure 24:
The self-energy ![]() |
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We display in Fig. 22 the self-energy
as a function
of forward time
.
We do not show the results of the steepest-descent
method which follow a mere exponential growth from Eq. (65)
At weakly non-linear scales (
Mpc-1) we recover at early times
the exponential growth of the steepest-descent prediction whereas at late
times we obtain a fast decay. At highly non-linear scales (k=10 h Mpc-1)
we obtain a peak at equal times
and strongly damped
oscillations at large time separations. This is clearly consistent with the
results obtained for the correlation G11 displayed in Fig. 18.
Next, we show in Fig. 23 the self-energy
at equal
redshifts z1=z2=0 as a function of wavenumber k. We again recover the
steepest-descent prediction at large scales. However, at small scales we
now obtain a steady growth whereas the steepest-descent method yields a smooth
decline. Therefore, in agreement with Figs. 19, 20
we find that the coupled system of Eqs. (46) (with G0 replaced
by G) and (51) entails at equal times a stronger growth of the
correlation G and of the self-energy
into the non-linear regime as
compared with the non-coupled equations associated with the steepest-descent
method.
Finally, we display in Fig. 24 the self-energy
at unequal
redshifts
z1=0,z2=3 as a function of wavenumber k. In agreement with
our results for other two-point functions we recover the steepest-descent
prediction at large scales and decaying oscillations at small scales.
The results obtained in the previous sections suggest that a convenient
approximation would be to use a simple analytical form for the response
function R and to compute the correlation G and the self-energy from Eqs. (46), (51). This avoids the computation of the
self-energy term
and the numerical integration of the
integro-differential Eq. (43) which requires rather small
time-steps. By contrast, Eq. (51) is a Volterra integral equation
of the second kind which is usually better behaved. We shall investigate
two such approximations associated with the direct steepest-descent approach
and the 2PI effective action scheme.
Within the framework of the direct steepest-descent scheme studied in
Sect. 5 we found that the response function
exhibits in the non-linear regime a series of oscillations with an envelope
given by the linear theory prediction. Therefore, following Eq. (80)
we consider the approximate response
defined as:
Here it is interesting to see how expression (109) may be interpreted.
In the linear regime the density and velocity fluctuations
are merely
amplified by a time-dependent factor (proportional to the scale-factor) which
implies in particular that the physics is exactly local:
only depends on the initial conditions (and a possible
external noise) at the same location
.
Thus in real space we have
and in Fourier space,
using the factorization (50) associated with statistical homogeneity,
is constant with respect to k, in agreement with Eq. (24).
On the other hand, the cosine dependence of Eq. (109) yields in real
space:
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Figure 25:
The logarithmic power
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Next, we use the steepest-descent prescription (65)-(66)
for the self-energy
and we compute numerically the correlation Gfrom the explicit expression (51). In fact, using Eq. (65) the
integrals over time may be performed analytically which yields:
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Figure 26:
The ratio
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We show our results for the equal-time density correlation G11 (i.e. the
power
)
in Figs. 25, 26 at
redshifts z=0 and z=3. We display the ratio to the linear power
to zoom over the weakly non-linear region and
we also plot the exact one-loop steepest-descent prediction ("sd'')
shown in Fig. 9 for comparison.
Figures 25, 26 clearly show that the
exact one-loop steepest-descent prediction matches the standard one-loop
perturbative result at large scales. Moreover, Fig. 25
suggests that at z=0 the fit from numerical simulations (Smith et al. 2003)
may be off by
at
Mpc-1. The power
follows roughly the behavior of the "exact'' one-loop
steepest-descent prediction but it is smaller by up to
and does
not match very well the one-loop perturbative result. This failure
at intermediate scales is not really surprising since Eq. (80)
was derived in the large-k limit using the asymptotic behaviors
(63)-(64). However, as can be seen in Fig. 1
they are only reached for k>10 h Mpc-1. Therefore, the approximation (109) is not sufficient to obtain accurate results over weakly
non-linear scales. This shows that the results obtained for the two-point
correlation are rather sensitive to the approximations used for the response R. In fact, since the direct steepest-descent method is quite
simple and easy to compute the approximation (109) is not very useful
for precise quantitative predictions and it is best to use the rigorous method
presented in Sect. 5.
Within the 2PI effective action method we found in
Sect. 6
that the response function exhibits oscillations as for the steepest-descent
prediction but their amplitude decays at large times. Following the analysis
of the simple model (95) and Eq. (104) we consider the
approximate response
defined as:
Then, we use Eq. (46) (with G0 replaced by G) and Eq. (51)
to compute the self-energy
and the correlation G, following the
procedure described in Sect. 6.
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Figure 27:
The logarithmic power
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Figure 28:
The ratio
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We show our results for the power
)
in
Figs. 27, 28 at redshifts z=0 and z=3.
We again display the ratio to the linear power
and
we also plot the exact 2PI effective action prediction ("2PI'')
shown in Fig. 19 for comparison.
Figures 27, 28 again clearly show that the
exact 2PI effective action prediction matches the standard one-loop
perturbative result at large scales. In a fashion similar to the
approximation (109) we find that the power
follows roughly the behavior of the exact one-loop 2PI effective action
prediction but it is smaller by up to
and does not match very well
the one-loop perturbative result. This shows again that the two-point
correlation is rather sensitive to the value of the response function.
Finally, we consider in this section mixed approaches which use elements from
both the steepest-descent and 2PI effective action schemes.
The idea is to simplify the computation involved in the
2PI effective action method by separating the computations of the pairs
and
.
That is, we first compute the self-energy
and the response R from the coupled Eqs. (43), (45),
where we use G0 and R in the expression (45) for
(the steepest-descent scheme uses G0 and R0 whereas the 2PI effective
action scheme uses G and R). Thus, the pair
becomes
independent of
and can be computed in a first step.
Since we keep the explicit dependence on R in the self-energy
the evolution Eq. (43) for the response function is no longer
linear but quadratic, hence we expect to recover the damping discussed in
Sect. 6.1 (see the analysis of
Eq. (95)).
We display our results in Fig. 29 which shows that the non-linear
response exhibits indeed a damping close to the one obtained in
Fig. 14 for the full 2PI effective action scheme.
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Figure 29:
The non-linear response function
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Next, we compute the pair
from the Eqs. (46) and (51). Here we can consider two procedures as we may use either G0 or G in the computation of
.
Clearly, these mixed schemes still agree with the usual one-loop
perturbative results. These two-step approaches are slightly faster than the
full 2PI effective action method for numerical computations. Indeed, as
explained in Sect. 6, within the 2PI effective action approach the equations obtained for two-point functions and
self-energies form a coupled system of non-linear equations. This leads to
an iterative scheme for their numerical computation (which converges within
7 steps at worst for the scales we consider here). On the other hand,
at each step the computation of the correlation G takes most of the computer
time (especially at late times) because of the double integral over times
in Eq. (51) but it converges faster (at fixed R) than the response R (at fixed G). Therefore, it saves time to separate the computations
of R (many fast iteration steps) and G (few long iteration steps).
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Figure 30:
The logarithmic power
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Figure 31:
The logarithmic power
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Figure 32:
The ratio
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Figure 33:
The ratio
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We display in Figs. 30-33
our results for the power
at redshifts z=0 and z=3 for
both schemes. The dashed curve
shows the power obtained
when the self-energy term
is used (as in the steepest-descent
approach) so that the correlation G is directly given by the explicit
expression (51). The solid curve
corresponds to
the case where we use the non-linear correlation G in the expression (46) of the self-energy, so that Eqs. (46) and (51)
are a coupled non-linear system which we solve iteratively (as in the
2PI effective action scheme).
We can see that both methods exhibit a similar behavior until
.
Farther into the non-linear regime
the "
-scheme'' breaks from the non-linear growth of the power-spectrum
and shows oscillations which converge close to the linear power,
in a manner reminiscent of the steepest-descent
approximation, whereas the coupled "
-scheme'' follows this non-linear
growth. On the other hand, in the weakly non-linear regime displayed in
Figs. 32-33 the predicted power
is suppressed as compared with the usual one-loop result. Depending on the
redshift this can improve or worsen the global agreement with numerical
simulations.
However, Fig. 33 suggests that when the usual one-loop
result shows a better global agreement with simulations it is a mere
coincidence as the simulation result separates last from the large-N
predictions (and the closer agreement with the usual one-loop result
at smaller scales has no theoretical basis). Nevertheless, the limited
accuracy of the numerical simulations prevents one from drawing definite
conclusions.
We can note in Fig. 30 that in the highly non-linear regime
the approximation
grows larger than for the full 2PI
effective action prediction displayed in Fig. 19. Indeed,
in the latter case the non-linear response is further damped by the
strong growth of the two-point correlation G (this would correspond to
a larger
whence
in the simple model (104)) and
this "negative feedback'' within the coupled system R,G leads to a smaller
G as compared with the approximation
where this feedback
is neglected. In fact, at very high k (beyond the range shown in
Fig. 30) the power
appears to diverge. It is
not clear whether this is due to the finite resolution of the numerical
computation or to a true divergence. However, for practical purposes this is
not a serious shortcoming since it occurs at small scales beyond the range of
validity of this approach (one would need to go beyond one-loop order and
probably beyond the hydrodynamical approach as shell-crossing can no longer
be neglected).
The good agreement in the weakly non-linear regime of these various schemes
enables one to evaluate their range of validity without the need to compare
their prediction with N-body simulations, which could be of great practical
interest. In fact, Fig. 32 suggests that this procedure
can provide a better accuracy than numerical simulations in this regime,
where the latter may still be off by .
We finally describe in this section how the results obtained in the previous
sections can be applied to more general cosmologies, such as a
CDM universe. As seen in Sect. 2.1 our formalism applies
equally well to any cosmology. We only need to use the relevant matrix
,
that is to take into account the time dependence of the ratio
in
Eq. (13), and to recall that
is the logarithm of the linear
growth factor, see Eq. (9). In practice, a widely used approximation
is to take
so that the results obtained for the
critical-density universe directly apply to the case
and the
only change is the time-redshift relation
.
We shall use this simple approximation here and compare our results to
numerical simulations. Thus, we consider the cosmological parameters
and H0=70 km s-1 Mpc-1as in Smith et al. (2003).
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Figure 34:
The non-linear response function
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Figure 35:
The non-linear response function
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We first plot in Figs. 34, 35 the non-linear
response functions we obtain within the one-loop steepest-descent
and 2PI effective action methods. We can check that we recover the behavior
obtained for the critical-density universe displayed in
Figs. 7, 14. The steepest-descent approach yields again
oscillations in the non-linear regime with an amplitude given by the
linear response at high k whereas the 2PI effective action method
gives damped oscillations. The transition to the non-linear regime
occurs at slightly smaller wavenumber because of the larger normalization
of the linear power-spectrum.
We show in Figs. 36-39 our results
for the two-point correlation G11, that is the non-linear density
power spectrum. We again recover the behavior analyzed in previous sections
for the critical-density universe. Both large-N expansion schemes agree
with the standard one-loop expansion at large scales. At small scales the
steepest-descent result goes back to values close to the linear power
whereas the 2PI effective action result keeps growing in a manner
similar to the usual perturbative result and numerical simulations.
The degree of agreement between the N-body simulations and either one
of the standard and 2PI results depends on redshift, that is on the slope of
the linear power-spectrum at the relevant scales.
Thus, the large-N expansions developed in this article apply in the same manner for different cosmologies and exhibit the same properties.
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Figure 36:
The logarithmic power
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Figure 37:
The logarithmic power
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Figure 38:
The ratio
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Figure 39:
The ratio
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In this article we have studied the predictions at one-loop order of large-Nexpansions for the two-point correlations associated with the formation of large-scale structures in the expanding universe. Focussing on weakly non-linear scales we have used the hydrodynamical equations of motion as a starting point. Then, we have recalled the path-integral formalism which describes the statistical properties of the system, assuming Gaussian initial conditions, and we have presented two possible large-N expansions. Next, we have described in details the numerical predictions obtained for a SCDM cosmology.
First, we have presented a direct steepest-descent approach where the
self-energy is expressed in terms of the linear response and correlation.
This allows simple computations as the time-dependence of the
self-energy can be factorized (for a critical density universe). Moreover,
the non-linear response R can be computed directly from this self-energy as
it is not coupled to the non-linear correlation G and it obeys a simple
linear equation. This also allows a detailed analysis of its behavior.
We have found that at one-loop order this
approach yields a non-linear response which exhibits strong oscillations
in the non-linear regime (small scales or late times) with an amplitude
which is given by the linear response. Therefore, the response does not
decay but only exhibits increasingly fast oscillations of the form
.
However, once the response is integrated over
time such oscillations lead to some damping as compared with the linear
response. Then, we have computed the non-linear correlation which can be
explicitly written in terms of this non-linear response which transports
forward over time the initial density and velocity fluctuations
as well as the fluctuations generated at all previous times by non-linear
couplings. We have checked that in the quasi-linear regime the non-linear
correlation agrees with the usual one-loop prediction (obtained from the
standard perturbative expansion over powers of the initial fluctuations).
In the highly non-linear regime the correlation separates from the
steep growth displayed by the standard one-loop result and converges
back to the linear amplitude. Unfortunately, this behavior does not improve
the agreement with N-body numerical simulations. Nevertheless, it suggests
that this large-N expansion is well-behaved as the two-point functions
do not "explode'' in the highly non-linear regime. Contrary to the
standard perturbative expansion, where higher-order terms grow as increasingly
large powers of k and a(t), the higher-order terms obtained within this
method may remain of finite amplitude.
Secondly, we have described a 2PI effective action approach where all
two-point functions are coupled. The numerical computation requires an
iterative procedure but thanks to causality the equations can be solved
by integrating forward over time with the help of a few iterations at each
time-step (keeping an implicit scheme in the non-linear integro-differential
equations). Then, we have found that the one-loop non-linear response again
exhibits fast oscillations in the non-linear regime but its amplitude now
shows a fast decay. We have explained how this damping is produced by the
non-linearity of the evolution equation obeyed by the response function.
This yields
a power-law decay such as
.
In the quasi-linear regime the non-linear correlation agrees again with the
usual one-loop prediction but in the highly non-linear regime it no longer
converges back to the linear power. It rather shows a moderate growth
intermediate between the linear prediction and the result of numerical
simulations, with an amplitude which depends on the redshift (whence on the
slope of the linear power-spectrum). Therefore, this method appears to be
superior to the direct steepest-descent approach which could not recover
any non-linear damping (at one-loop order) but it requires more complex
numerical computations.
Thirdly, we have described other approximation schemes built from these two approaches. Thus, we have found that using a simple explicit approximation for the non-linear response within either large-N scheme gives correct qualitative results but does not manage to reproduce accurately the quantitative results obtained from the full large-N approaches. Next, we have investigated the approximations obtained by separating the computations of the response and the correlation (but keeping a non-linear dynamics for the response). As expected we have found that this yields a non-linear response which closely follows the damping obtained by the full 2PI effective action approach and it gives good results (as compared with the full large-N schemes) for the correlation.
Finally, we have shown that these results also apply to more general
cosmologies such as a CDM universe, since our formalism extends
to any expansion history of the Universe provided we use the correct
time-redshift relation and linear growth factors.
Thus, we have shown in this article that large-N expansions provide an interesting systematic approach to the formation of large-scale structures in the expanding universe. They already show at one-loop order some damping in the non-linear regime for the response function and the amplitude of the two-point correlation remains well-behaved. Unfortunately, for practical purposes the accuracy provided by these large-N expansions at one-loop order is not significantly better than the usual perturbative result for weakly non-linear scales. Nevertheless, the improvement may become greater at higher orders since the expansion is likely to be better behaved, but this requires rather complex computations which are beyond the scope of this paper.
On the other hand, one of the goals of this article was to describe a different
approach to the problem of non-linear gravitational clustering than the standard
expansion schemes, namely a path-integral formalism. An important feature of
this approach is that the statistical nature of the problem is already included
in the definition of the system, that is the action
describes
both the equations of motion (here in the hydrodynamical limit) and the average
over Gaussian initial conditions. This also means that one directly works with
correlation functions (such as the power spectrum). By contrast, in usual
expansion schemes (or in N-body numerical simulations) one first works
with the density field associated with a specific initial condition, that is one
tries to express the non-linear density field in terms of the linear density
field (e.g. as a truncated power series) and next performs the average over the
initial condition. In principles, this formalism can present several advantages.
First, the statistical quantities such as the power-spectrum are precisely
the objects of interest for practical purposes. Secondly, they exhibit symmetries
(such as homogeneity and isotropy) which are not satisfied by peculiar realizations
of the initial conditions. Moreover, they are smooth functions (such as power laws)
rather than singular distributions. This could facilitate the numerical
computations and the building of various approximations.
Of course, one can recover the standard perturbative results
by expanding over the cubic part of the action, as recalled in this paper and
discussed in more details in Valageas (2004). However, the advantage of this
formalism is that it may serve as a starting point to other approximation schemes
by applying the methods of statistical mechanics and field theory. Thus, we have
described in details in this paper two large-N expansion schemes. In addition
to the interesting properties of these two methods discussed above, we can note
that it is useful to have several rigorous expansion schemes which only
differ by higher-order terms than the truncation order. Indeed, since one can expect
that they should be correct up to the scale where they start to depart from one
another, this should allow one to estimate their range of validity without computing
explicitly higher-order terms or performing N-body simulations.
In fact, in the weakly non-linear regime where all expansion schemes agree this
procedure should provide a better accuracy than N-body simulations which may still be
inaccurate by up to .
Farther into the non-linear regime, it seems
that the usual one-loop result and the 2PI effective action scheme
give better results than the direct steepest-descent method.
On the other hand, one can hope that other methods than these two expansion schemes
could be applied to the action
.
In regard to recent works we can
note that the Schwinger-Dyson equations obtained in Crocce & Scoccimarro (2006a)
can also be obtained from the approach described in this paper, provided we use
the equations of motion in their integral form (that is we first integrate the
time-derivative), as also described in Valageas (2001). Whereas
Crocce & Scoccimarro (2006a) used a diagrammatic technique and the resummation
of all diagrams gives back the usual Schwinger-Dyson equations the path-integral
formalism directly gives these equations by expanding the action (or its Legendre
transform) about a saddle-point. On the other hand, the diagrammatic approach
also provides a direct expression of the response function
(in the
limit
)
as a series of diagrams (which could be recovered
here by writing the self-energy
as a series of diagrams). Thus,
Crocce & Scoccimarro (2006a) managed to resum these diagrams in the high-k limit
and to obtain the asymptotic behavior of
.
It is not clear yet how this result could be directly obtained from the
path-integral formalism without expanding over diagrams.
Among the points which deserve further studies, it would be interesting to apply these approaches to higher-order correlations beyond the two-point functions investigated here. On the other hand, the analysis presented in this article is based on the hydrodynamical equations of motion which break down at small scales beyond shell-crossing. It is possible to apply these large-N expansions to the Vlasov equation (Valageas 2004) but the numerical computations would be significantly more complex (since one needs to add velocities to space coordinates). Nevertheless, the results obtained within the hydrodynamical framework (damping and well-behaved non-linear asymptotics) can be expected to remain valid in the collisionless case and studies such as the present one may serve as a first step towards an application to the Vlasov dynamics. Such large-N expansions could also be applied to simpler effective dynamics which attempt to go beyond shell-crossing (based for instance on a Schroedinger equation, Widrow & Kaiser 1993). These works would be of great practical interest as several cosmological probes (such as weak lensing surveys and measures of the baryon acoustic oscillations) which aim to measure the recent expansion history of the Universe in order to constrain the dark energy equation of state (as well as other cosmological parameters) are very sensitive to the weakly non-linear scales where non-linear effects can no longer be neglected.