- Improving the accuracy of cosmic magnification statistics
- 1 Introduction
- 2 Formalism
- 3 Magnification statistics from numerical simulations
- 4 Applications to quasar-galaxy correlations
- 5 Conclusion
- Appendix A: Bispectrum and non-linear evolution
- References

A&A 403, 817-828 (2003)

DOI: 10.1051/0004-6361:20030406

**B. Ménard ^{1,2} - T. Hamana^{3,1} - M.
Bartelmann^{1} - N. Yoshida^{4,1}**

1 - Max-Planck-Institut für Astrophysik, PO Box 1317,
85741 Garching, Germany

2 -
Institut d'Astrophysique de Paris, 98 bis Bld Arago, 75014,
Paris, France

3 -
National Astronomical Observatory of Japan, Mitaka, Tokyo
181-8588, Japan

4 -
Harvard-Smithsonian Center for Astrophysics, 60 Garden Street,
Cambridge MA 02138, USA

Received 4 October 2002 / Accepted 17 March 2003

**Abstract**

The systematic magnification of background sources by the weak
gravitational-lensing effects of foreground matter, also called
*cosmic magnification*, is becoming an efficient tool both for
measuring cosmological parameters and for exploring the
distribution of galaxies relative to the dark matter. We extend
here the formalism of magnification statistics by estimating the
contribution of second-order terms in the Taylor expansion of the
magnification and show that the effect of these terms was previously
underestimated. We test our analytical predictions against numerical
simulations and demonstrate that including second-order terms allows
the accuracy of magnification-related statistics to be substantially
improved. We also show, however, that both numerical and analytical
estimates can provide only lower bounds to real correlation
functions, even in the weak lensing regime.
We propose to use count-in-cells estimators rather than
correlation functions for measuring cosmic magnification since they
can more easily be related to correlations measured in numerical
simulations.

**Key words: **cosmology: gravitational lensing -
cosmology: large-scale structure of Universe

Gravitational lensing by large-scale structures magnifies sources and
distorts their images. The systematic distortion of faint background
galaxies near matter overdensities, the *cosmic shear*, has been
measured by several groups in the past few years (Bacon et al. 2000,
2002; Hämmerle et al. 2002; Hoekstra et al. 2002; Kaiser et al. 2000; Maoli et al. 2001; Réfrégier et al. 2002; Rhodes et al. 2001; Van Waerbeke et al. 2000, 2001, 2002; Wittman et al. 2000). It was found to be in remarkable agreement with theoretical
predictions based on the Cold Dark Matter model, and has already provided new constraints on cosmological parameters (Van Waerbeke et al. 2001).

In a similar way, systematic magnifications of background sources near
foreground matter overdensities, the *cosmic magnification*, can
be measured and can provide largely independent constraints on
cosmological parameters (Ménard & Bartelmann 2002; Ménard et al. 2002). Gravitational magnification has two effects: first, the
flux received from distant sources is increased, and the solid angle
in which they appear is stretched, thus their density is diluted. The
net result of these competing effects depends on how the loss of
sources due to dilution is balanced by the gain of sources due to flux
magnification. Sources with flat luminosity functions, like faint
galaxies, are depleted by cosmic magnification, while the number
density of sources with steep luminosity functions, like quasars, is
increased. Thus, cosmic magnification gives rise to apparent angular
cross-correlations between background sources and foreground matter
overdensities which are physically completely uncorrelated with the
sources. These overdensities can be traced by using the distribution
of foreground galaxies.

Numerous studies have demonstrated the existence of quasar-galaxy correlations on angular scales ranging from one arcminute to about one degree, as expected from cosmic lensing (for a review, see Bartelmann & Schneider 2001; also Guimarães et al. 2001). In many cases, the measured correlation amplitudes have been higher than the theoretical predictions, however a number of non-detections have also been reported, leaving the true amplitude of the effect unclear from the observational point of view.

While cosmic shear can directly be related to observable quantities like image ellipticities, the theoretical interpretation of cosmic magnification involves several approximations:

- the luminosity function of the sources is described by a
power-law over the range probed by the flux limit of the
observation; and
- the magnification is assumed to fall into the weak lensing
regime, i.e. to deviate weakly from unity. Thus, the magnification
can with sufficient accuracy be approximated by its first-order
Taylor expansion and its deviation from unity becomes proportional
to the lensing convergence alone.

Our paper is structured as follows: first, we introduce the formalism of the effective magnification and its Taylor expansion in Sect. 2. We then describe a number of statistics related to the lensing convergence, and evaluate the amplitude of the second-order terms which appear in the Taylor expansion. In Sect. 3, we describe the numerical simulations we use to test our analytical results and estimate the accuracy of several approximations for the magnification. As an application, we investigate second-order effects on quasar-galaxy correlations in Sect. 4, and we summarise our results in Sect. 5.

2 Formalism

Cosmic magnification can be measured statistically through characteristic changes in the number density of the background sources. Along a given line-of-sight, this effect depends on two quantities:

- the magnification factor ,
which describes whether sources
are magnified or demagnified, depending on whether the matter along
their lines-of-sight is preferentially over- or underdense compared
to the mean,
- and the logarithmic slope
of the source counts as a
function of flux, which quantifies the amplitude of source
number-count modifications due to flux magnification. As mentioned
in the introduction, magnification by gravitational lensing not only
increases the observed flux, but also stretches the sky, thus the
number density of sources on a magnified patch of the sky is
reduced. The net magnification effect, called
*magnification bias*, depends on the balance between the number of sources lost by dilution and gained by flux magnification. The steeper the number-count function of the sources is, the more pronounced is the magnification bias.

(1) |

where

The local properties of the gravitational lens mapping are
characterised by the convergence ,
which is proportional to
the surface mass density projected along the line-of-sight, and the
shear ,
which is a two-component quantity and describes the
gravitational tidal field of the lensing mass distribution. The
effective magnification is related to
and
through

where is taken as the absolute value of the shear. In the weak-lensing regime, both and are small compared to unity, and the previous expression can be expanded in a Taylor series:

Previous studies using analytical formulae for magnification statistics focused only on the first-order term of this expansion, i.e. they used the approximation , which potentially causes the amplitude of the effect to be underestimated. In this section, we investigate the second-order terms in the expansion and estimate their contribution.

In doing so, we first note that
and
share the same statistical properties
(e.g. Blandford et al. 1991), because both
and
are
linear combinations of second-order derivatives of the lensing
potential. The identity of their statistics is most easily seen in
Fourier space. Since we will only deal with ensemble averages of the
magnification later on,
and
can be combined
into a single variable, which we denote by
for
simplicity. Thus, we can write for our purposes,

(4) |

Observable effects are due to departures from the mean value of the magnification. Therefore, the relevant quantity to correlate is . Then, up to second order in , the autocorrelation function of the effective magnification is

and the corresponding power spectrum can be expanded in a similar way,

(6) |

the power spectrum will be defined in Eq. (16) below. The last two equations show that the importance of the second-order terms in the expansion (3) increases as the number-count function of the background sources steepens, i.e. as increases. In the following, we will use unless stated otherwise. This value applies, for instance, to the number counts of bright quasars with (Pei 1995). For simplicity, we abbreviate by .

We will now estimate several -related statistical quantities
needed in the Taylor expansion of the magnification. For this purpose,
we first introduce the projector such that

(7) |

can be written as a weighted line-of-sight projection of the density contrast from the observer to the Hubble distance . The projector is

where

where

(10) |

As indicated by Eq. (5), the estimation of second-order terms requires the computation of the cross-correlation between and . We do this by first introducing a three-point correlation function for and then identifying two of its three points. As usual, we define the three-point function by

Using the projector defined in (8), we can then write

Next, we employ the approximation underlying Limber's equation, which asserts that the coherence length of the density fluctuation field is much smaller than the scales on which the projector varies appreciably. Finally, we insert the expression for the bispectrum of the dark-matter fluctuations detailed in Appendix A, and find

= | |||

(13) |

where is defined by

(14) |

Then, using Eq. (12) and identifying two points of the three-point correlation function (or equivalently or ), we find

The term is a function of only. Its contribution to the power spectrum of the magnification is given by the inverse Fourier transform of Eq. (15), which reads

2.3 Results and predictions

We can now numerically evaluate the first two contributions to the Taylor expansion of the magnification autocorrelation function defined in Eq. (5). As mentioned before, we use here.

For evaluating the correlation functions, we use a CDM power spectrum
in a spatially flat Universe parameterised with
,
,
*h*=0.7 and
.
The non-linear evolution of
the power spectrum and the bispectrum are computed according to the
formalisms developed by Peacock & Dodds (1996) and Scoccimarro et al. (2000), see Appendix A. The upper panel of
Fig. 1 shows the first- and second-order
contributions (dashed and dotted lines, respectively) to the Taylor
expansion of the magnification for a fixed source redshift of
.
The sum of the two contributions is shown by the
solid line. The figure shows that the contribution of the second-order
term reaches an amplitude of more than 30% of the first-order term
on angular scales smaller than one arcminute. According to
Eq. (5) which describes the Taylor expansion of the
magnification autocorrelation, we define the contribution of
the second-order relative to the first-order term as

(17) |

The lower panel shows this ratio in per cent for different source redshifts as a function of angular scale. From the lower to the upper curves, the source redshifts are 1, 1.5, 2 and 3. For each source redshift, the contribution of the second term exhibits a similar dependence on angular scale:

- on scales larger than a few degrees, the contribution drops to
negligible values;
- effects become relevant on smaller scales, with a fairly
constant amplitude from a few degrees down to around 10 arcmin;
- on yet smaller scales, the second-order contribution increases
steeply, due to the non-linear evolution of the density field. For
sources at redshift 2, the amplitude of the second term reaches
*half*of the amplitude of the first term below one arcmin.

So far, we have only investigated the amplitude contributed by the second-order term. In order to estimate the remaining contributions of all missing terms of the magnification expansion, we will now use numerical simulations allowing a direct computation of as a function of the convergence and the shear .

3 Magnification statistics from numerical simulations

On sub-degree scales, lensing effects due to non-linearities
in the density field can only be *approximated* using analytical
fitting formulae (Peacock & Dodds 1996; Scoccimarro & Couchman 2001)
as seen above. A full description requires numerical simulations
(White & Hu 2000).

For testing the theoretical predictions we performed ray-tracing
experiments in a Very Large *N*-body Simulation (VLS) recently carried
out by the Virgo Consortium (Jenkins et al. 2001; and see also Yoshida
et al. 2001 for simulation details)^{}.

The simulation was performed using a parallel P^{3}M code (MacFarland
et al. 1998) with a force softening length of
.
The simulation employed 512^{3} CDM particles
in a cubic box of
on a side. It uses a flat
cosmological model with a matter density
,
a
cosmological constant
,
and a Hubble constant *h*=0.7. The initial matter power spectrum was computed using CMBFAST
(Seljak & Zaldarriaga 1996) assuming a baryonic matter density of
.
The particle mass
(
)
of the simulation
is sufficiently small to guarantee practically no discreteness effects
on dark-matter clustering on scales down to the softening length in
the redshift range of interest for our purposes (Hamana et al. 2002).

The multiple-lens plane ray-tracing algorithm we used is detailed in
Hamana & Mellier (2001; see also Bartelmann & Schneider 1992; Jain et al. 2000 for the theoretical basics); we thus
describe only aspects specific to the VLS *N*-body data in the
following. In order to generate the density field between *z*=0 and
,
we use a stack of ten snapshot outputs from two runs of the
*N*-body simulation, which differ only in the realisation of the
initial fluctuation field. Each cubic box is divided into 4 sub-boxes
of
with the shorter box side
being aligned with the line-of-sight direction. The *N*-body particles
in each sub-box are projected onto the plane perpendicular to the
shorter box side and thus to the line-of-sight direction. In this way,
the particle distribution between the observer and
is
projected onto 38 lens planes separated by
.
Note that in order to minimise the
difference in redshift between a lens plane and an output of *N*-body
data, only one half of the outputs (i.e. two sub-boxes) at *z*=0 are
used.

The particle distribution on each plane is converted into the surface
density field on either a 1024^{2} or 2048^{2} regular grid using the
triangular shaped cloud (TSC) assignment scheme (Hockney & Eastwood
1988). The two grid sizes are adopted for the following reasons:

- the 1024
^{2}grid is chosen to maintain the resolution provided by the*N*-body simulation and removing at the same time the shot noise due to discreteness in the*N*-body simulation. Its computation follows the procedure described in Hamana & Mellier (2001) and Jain et al. (2000). The corresponding outputs will be labelled with*large-scale smoothing*in the following. - the 2048
^{2}grid is also chosen to examine effects of small-scale nonlinear structures which are smoothed in the*large-scale smoothing*simulation. We should, however, note that in this case the shot noise is not sufficiently removed. Actually, the shot-noise power spectrum amplitude exceeds the convergence power spectrum on scales below 1 arcmin. In the following, therefore, we will only consider measured correlation functions on scales larger than 1 arcmin. The corresponding outputs will be labelled with*small-scale smoothing*below.

We point out that second and higher-order statistics of point-source magnifications are generally ill-defined in presence of caustic curves because the differential magnification probability distribution asymptotically decreases as for large (see Fig. 2). This is a generic feature of magnification near caustics and is thus independent of the lens model. Strong lensing effects on point sources near caustic curves give rise to rare, but arbitrarily high magnification values in the simulations, and therefore the variance of the measured statistics of cannot be defined. However, the smoothing procedure introduced above allows this problem to be removed because it smoothes out high density regions in the dark matter distribution and thus the fractional area of high magnification decreases. In reality, infinite magnifications do not occur, for two reasons. First, each astrophysical source is extended and its magnification (given the surface brightness-weighted point-source magnification across its solid angle) remains finite. Second, even point sources would be magnified by a finite value since for them, the geometrical-optics approximation fails near critical curves and a wave-optics description leads to a finite magnification (Schneider et al. 1992, Chap. 7).

3.2 Filtering

The computation of correlation functions from numerical simulations is mainly affected by two effects; on large scales by the finite box size of the dark matter simulation, and on small scales by the grid size used for computing the surface density field from the particle distribution. These boundaries set the limits for the validity of correlation functions measured in numerical simulations. In other words, this means that measuring a correlation function on a given scale is relevant only if this scale falls within the range of scales defined by the simulation. As shown in the previous section, our method for computing the cross-correlation between and consists of first computing a three-point correlation function , and then identifying two of its three points. In such a case, one of the correlation lengths of the triple correlator becomes zero, thus necessarily smaller than the smallest relevant scales in any simulation. This prevents us from using any numerical simulation for directly comparing the results.

In order to avoid this problem, and for comparing our analytical with
numerical results, we will introduce an effective smoothing into the
theoretical calculations, such that each value of
at a given
position
is evaluated by averaging the -values in
a disk of radius
centred on
.
Indeed, the limit imposed by the grid size of the simulation gives
rise to an unavoidable smoothing-like effect which cancels all
information coming from scales smaller than a corresponding smoothing
scale
.
For this purpose, we introduce a
smoothed three-point correlator,

(18) | |||

where the function is a normalised top-hat window of radius . Introducing this smoothing scheme into the expression for yields

= | |||

(19) | |||

where . Similarly, introducing the smoothing scheme into the two-point correlation function gives

The effective smoothing scale depends on two parameters:

- the evolution of the apparent grid size of the simulation as a
function of redshift, and
- the radial selection function of the dark-matter field whose correlation function has to be measured.

where

Figure 3:
Smoothing angle of the simulation as a function of redshift
for the two ray-tracing schemes. In order to show the relevant
quantities leading to the effective smoothing angle, we overplot the
weighting function
(see
Eqs. (8) and (21)). |

small-scale smoothing | large-scale smoothing | |

The second important difference between analytical calculations and measurements in numerical simulations is the finite box size effect. Indeed, the analytical correlation functions presented above were computed taking into account all modes in the power spectrum. However, the finite size of the box used in the simulation introduces an artificial cutoff in the power spectrum since wavelengths larger than the box size are not sampled by the simulation. This effect can also be taken into account in the analytical calculations by simply cancelling all the power on wavelengths with wave number . The boxes we use have a comoving size of which corresponds to .

With the help of the filtering schemes introduced in the previous section, we can now compare our theoretical predictions with correlation functions measured from the numerical simulations. We first compare the amplitude and angular variation of the two first terms of the Taylor expansion of the magnification separately. In the next section, we will then compare their sum to the total magnification fully computed from the simulation.

In Fig. 4, we overplot analytical and numerical results. The upper curve shows the autocorrelation function of as a function of angular scale. We plot in circles the average measurement from 36 realisations of the simulation, and the corresponding 1- error bars to show the accuracy of the numerical results as a function of angular scale. The solid line shows the analytical prediction, including effective smoothing and an artificial cut of the power at scales below . The agreement is good on all scales. For comparison, the dotted line shows the result if we do not impose the large-wavelength cut, and the dashed line is the result if no cut and no smoothing are applied. In both cases, the deviations from the fully filtered calculation remain small since we are probing angular scales within the range allowed by the simulation.

The lower curves in Fig. 4 show a quantity proportional to the second-order correction of the Taylor expansion, namely the correlation function . In the same way as before, the circles show average measurements from 36 realisations, and the error bars denote the corresponding 1- deviation. The prediction including smoothing and small-wavelength cut (solid line) shows a relatively good agreement given the expected accuracy of the bispectrum fitting formula, which is approximately 15% (Scoccimarro & Couchman 2000). This time, including smoothing changes the amplitude dramatically, and this effect affects all scales (see the dashed line). As discussed before, this is expected since we are measuring a three-point correlator on triangles which have one side length smaller than the angular grid size of the simulation. Finally, as shown by the difference between the dotted and solid lines, cancelling the power on scales where again improves the agreement on large scales.

The agreement between our analytical and numerical computations of and demonstrates the validity of the formalism introduced in Sect. 2 as well as the choice of the effective smoothing scale (Eq. (21)) for describing the second-order term in the Taylor expansion of the magnification.

We now want to investigate how well the second-order expansion describes the full magnification expression (2) which can be computed using maps of , and (a net rotation term which arises from lens-lens coupling and the lensing deflection of the light ray path; see Van Waerbeke et al. 2001b) obtained from the simulations (see Hamana et al. 2000 for more detail).

Before doing so, we recall that the amplitude of the magnification autocorrelation measured from the simulation depends on the smoothing scale, as seen in Sect. 3.2, since is nonlinear in the density field. Therefore, all the following comparisons are valid for a given effective smoothing length only.

We further emphasise that two problems will complicate this comparison. First, our analytical treatment is valid in the weak-lensing regime only, i.e. as long as convergence and shear are small compared to unity, , . While most light rays traced through the numerical simulations are indeed weakly lensed, a non-negligible fraction of them will experience magnifications well above two, say. Such events are restricted to small areas with high overdensities and thus affect the magnification statistics only at small angular scales. Second, a separate problem sets in if and where caustics are formed. The magnification of light rays going through caustics is infinite, and the magnification probability distribution near caustics drops like for . As noted above, second- or higher-order statistics of then become meaningless because they diverge.

Departures of the numerical from the analytical results will thus have
two distinct reasons, viz. the occurrence of non-weak magnifications
which causes the analytical to underestimate the numerical results on
small angular scales; and the formation of caustics, which causes
second-order magnification statistics to break down entirely. Both
effects will be demonstrated below. They can be controlled or
suppressed in numerical simulations by smoothing, which makes lensing
weaker, or by masking highly magnified light rays or regions
containing caustics.

In Fig. 5, we plot with circles the autocorrelation function measured from the large- and small-scale smoothing simulations in the left and right panels, respectively. The presence of caustics is more pronounced in the case of small-scale smoothing than in the large-scale smoothing simulations. The dotted line shows the theoretical prediction given by the first-order term of the Taylor expansion, namely . This yields a low estimate of the correlation, with a discrepancy of order 10% on large scales, and more than 20% below a few arcminutes.

As expected from the preceding discussion, this level of discrepancy also depends on the effective smoothing scale and can increase if simulations with a smaller grid size are used. Estimating the contribution of the two lowest-order terms of , we computed in Sect. 2.3 a lower bound to this discrepancy for a real case without smoothing, and found it to reach a level of 25% at large scales, and above 30% below a few arcminutes. The smoothed results taking the additional contribution of the second-order term into account are plotted as solid lines, and give a much better agreement, as expected. To quantify this in more detail, the lower panels of the figure show several contributions compared to the first-order term, i.e. to .

- The symbols show the additional amplitude of the magnification
statistics measured from the simulation, compared to the first-order
term also obtained from the simulation,

(22)

The error bars indicate the 1- deviation across 36 realisations. - The solid line shows the contribution of the second-order
relative to the first-order term computed from the analytical
expression including the effective smoothing,

(23)

with .

As the lower panel of the large-scale smoothing simulation shows, the
simple
estimate of the magnification
misses 20% of the real amplitude near one arcminute. This
discrepancy almost vanishes after adding the contribution of the
second-order term, which gives at all scales a final agreement on the
per cent level: the additional amplitude reaches 19% at the smallest
scales of the figure, compared to a value of 20% given by the
simulation, and agrees within better than one per cent on larger
scales. Therefore, taking into account the
correction allows the
accuracy to be increased by a factor of 20 compared to the
approximation
,
in the case of our
*large-scale smoothing* simulation. On the largest scales,
between 6 and 30 arcmin, the agreement even improves. Above
these scales, the numerical results do not allow any relevant
comparison because the number of available independent samplings
corresponding to a given separation decreases. On scales below a few
arcminutes, the offset between the measured points and the analytical
estimate gives the amplitude of all higher-order terms neglected in
the Taylor expansion of the magnification. As we can see, their
contribution is on the one per cent level for the large-scale
smoothing simulation.

The curves shown in the right panel demonstrate how the use of a smaller smoothing scale increases the discrepancy between the analytical and the numerical results. The fraction of non-weakly magnified light rays increases, and caustics appear which give rise to a power-law tail in the magnification probability distribution. We investigate the impact of the rare highly magnified light rays by masking pixels where the simulated magnification exceeds 4 or 8, and show that caustics have no noticeable effect on the amplitude of the magnification autocorrelation function determined from these simulated data. Note, however, that the impact of the caustics depends on the source redshift. The higher the redshift, the more caustics appear, and the larger is their impact on the correlation amplitude.

Imposing lower masking thresholds removes a significant fraction of the area covered by the simulation, changing the spatial magnification pattern and thus the magnification autocorrelation function. The corresponding measurements are represented by the dashed error bars in the lower right panel of Fig. 5. We note that the error bars of computed with the small-scale smoothing simulation become larger at small scales compared to the lower left panel. This reflects the fact that second-order magnification statistics are ill-defined once caustics appear. In the next section, we will investigate similar smoothing effects on cross-correlations between magnification and dark matter fluctuations. These quantities are not affected by problems of poor definition when the smoothing scale becomes small, and therefore do not show larger error bars at small scales when the smoothing scale decreases.

These comparisons show that the approximation misses a non-negligible part of the total amplitude of weak-lensing magnification statistics. The formalism introduced in Sect. 2 allows second-order corrections to be described with or without smoothing of the density field. This provides a better description of the correlation functions, but still gives a lower amplitude than the simulation results. As we noticed, the analytic computation based on the Taylor expansion is sufficiently accurate only in the weak lensing regime. In reality, however, the strong lensing, which can not be taken into account in the analytic formalism, has a significant impact on the magnification correlation especially at small scales as shown in the small-scale smoothing simulation. Therefore, one should carefully take the strong lensing effect into consideration when one interprets the magnification related correlation functions. However, we will see in the next section that counts-in-cells estimators are less affected by the strong lensing than correlation functions and thus enable better comparisons of observations with results from simulations.

4 Applications to quasar-galaxy correlations

As a direct application of the formalism introduced previously, we now investigate the effects of second-order terms on a well-known magnification-induced correlation, namely the quasar-galaxy cross-correlation (the results can also be applied to galaxy-galaxy correlations induced by magnification; Moessner & Jain 1998). In order to estimate cosmological parameters from this kind of correlations, we then suggest the use of a more suitable estimator using counts-in-cells rather than two-point correlation functions. It has the advantage of making the observational results more easily reconciled with the ones from numerical simulations.

The magnification bias of large-scale structures, combined with galaxy biasing, leads to a cross-correlation of distant quasars with foreground galaxies. The existence of this cross-correlation has firmly been established (e.g. Benítez & Martínez-González 1995; Williams & Irwin 1998; Norman & Impey 1999; Norman & Williams 2000; Benítez et al. 2001; Norman & Impey 2001). Ménard & Bartelmann (2002) showed that the Sloan Digital Sky Survey (York et al. 2000) will allow this correlation function to be measured with a high accuracy. Its amplitude and angular shape contain information on cosmological parameters and the galaxy bias factor. Thus, it is important to accurately describe these magnification-related statistics in order to avoid a biased estimation of cosmological parameters as well as the amplitude of the galaxy bias.

As shown in Bartelmann (1995), the lensing-induced cross-correlation
function between quasars and galaxies can be written as

= | (24) |

Using the above formalism, we can expand the effective magnification fluctuation up to second order and find the correcting term:

(25) |

The second term is proportional to (contrary to the factor in Eq. (5)), since there is only one contribution of the magnification. Therefore, the expected effects will be roughly half of those on the autocorrelation of the effective magnification seen in the previous section. Assuming a linear bias

where is the normalised distance distribution of the galaxies. For this example, we will use

(27) |

with and

The results are shown in Fig. 6. As we can see, previous
estimates using the approximation
missed
approximately 15% of the amplitude on small scales for quasars at
redshift unity. Using quasars at redshift 2, these effects reach up
to 25%. These offsets, which are only lower limits, would lead to
biased estimates of
or *b*, for example.

As for the magnification autocorrelation, we can compare our
theoretical estimates against numerical estimations. We can first introduce
a coefficient
describing the accuracy of our second-order
correction:

(28) |

We plot the results in Fig. 7. Note that contrary to the magnification autocorrelation, this quantity does not suffer from poor definition, even without smoothing. The difference can be seen by the same size of the error bars between the two simulation results at small scales, whereas they were larger in the case of for the small-scale smoothing simulation (Fig. 5). The results for are very similar those obtained for : for the large-scale smoothing ray-tracing we find very good agreement which reaches the one percent level on small scales. However, when the smoothing length decreases, we see from the small-scale smoothing outputs that we are missing a part of the total amplitude on small scales, which shows that higher-order terms play a non negligible role on those scales.

For precisely estimating cosmological parameters as well as the
amplitude of the galaxy bias, it is necessary to employ theoretical
magnification statistics that closely describe the observables.
However, we have seen in Sect. 3 that
analytical estimates as well as numerical simulations have intrinsic
limitations and prevent us from accurately describing usual *n*-point
correlation functions related to magnification statistics.

Besides, it is possible to focus on another estimator closely related
to correlation functions, namely a count-in-cells estimator, which
naturally smoothes effects originating from the density field and can
thus more easily be reconciled with numerical simulations. So far,
quasar-galaxy or galaxy-galaxy correlations have been quantified
measuring the excess of background-foreground pairs at a given angular
separation. Instead, we can correlate the amplitude of the background
and foreground fluctuations, both measured inside a given aperture. We
will therefore introduce a count-in-cells estimator,

= | |||

(29) |

where the subscript indicates averaging of and inside a cell of radius . In practice, this estimator is intended to be applied to galaxy-galaxy rather than to quasar-galaxy correlations, since the average angular separation between bright distant quasars is of order one degree for current surveys, thus averaging the source counts inside cells with radii of several arcminutes will not be relevant. Using galaxies as background sources, this limitation occurs at much smaller scales.

Using a first-order Taylor expansion for the magnification, the new
estimator
can be written

(30) |

where . This expression differs from the 2-point correlation function (9) by its Fourier-space filtering of the power spectrum. The additional smoothing wipes out the power on scales smaller than the physical scale corresponding to the angular smoothing scale . For any observational result to be compared to a numerical simulation, and the smoothing scale used in the simulation will have to be carefully adapted to each other and to the redshift distribution of the foreground galaxy distribution.

In practice, masking always makes correlation functions easier to measure than counts-in-cells. However, in a large survey with short exposures like the SDSS, masking is not a real issue to measure counts-in-cells since unusable regions are quite rare and their area is small compared to the total survey size. This is different for cosmic shear surveys for which images are deeper and saturation occurs more frequently.

Note that gravitational lensing by the foreground galaxies themselves is entirely irrelevant here. The angular scale on which galaxies act as efficient lenses is on the order of one arc second and below, much smaller than the angular scales we are concerned with. Moreover, the probability for a quasar to be strongly lensed by a galaxy is well below one per cent. Bartelmann & Schneider (1991) demonstrated this point explicitly by including galaxies into their numerical simulations and showing they had no noticeable effect.

5 Conclusion

As surveys mapping the large-scale structure of the Universe become wider and deeper, measuring cosmological parameters as well as the galaxy bias with cosmic magnification will become increasingly efficient and reliable. Therefore, an accurate theoretical quantification of magnification statistics becomes increasingly important.

Previous estimates of cosmic magnification relied on the assumption that the magnification deviates sufficiently little from unity that it can be accurately approximated by its first-order Taylor expansion about unity, i.e. . In this paper, we have tested the validity of this assumption in the framework of magnification statistics, by investigating the second-order terms in the Taylor expansion of . We have shown that:

- Second-order terms can be related to the cross-correlation
between
and ;
- their importance increases as the number-count function of the
background sources steepens, i.e. as
increases;
- their amplitude is
*not*negligible: for the magnification autocorrelation, their contribution is typically on the order of 30%-50% at scales below one degree. Therefore, previous estimates of cosmic magnification were systematically low.

Using a simulation with an effective smoothing scale of 0.8 arcmin, we found that our second-order formalism is accurate to the percent level for describing magnification autocorrelations. Compared to previous estimates, this improves the accuracy by a factor of 20. For smaller effective smoothing scales, the contribution of third- and higher-order terms becomes important on scales below a few arcminutes.

Finally we have applied our formalism to observed correlations, like quasar-galaxy and galaxy-galaxy correlations due to lensing. We have shown that second-order corrections increase their amplitude by 15% to 25% on scales below one degree. These correlations are valuable tools to probe cosmological parameters as well as the galaxy bias. However, even including our correcting terms, analytical or numerical estimates of magnification statistics can only provide lower bounds to the real amplitude of the correlation functions in the weak-lensing regime. Thus, we propose using count-in-cells estimators rather than correlation functions since the intrinsic smoothing in determining counts-in-cells allows the observational results to be more directly related to those obtained in numerical simulations.

Thus, some care is required in using cosmic magnification as described by a Taylor expansion for constraining cosmological parameters, especially for interpreting measurements on small angular scales. Therefore, describing magnification statistics using the halo-model formalism will be of great interest in order to achieve a precise and direct description of observational quantities.

We thank Francis Bernardeau and Stéphane Colombi for helpful discussions. This work was supported in part by the TMR Network "Gravitational Lensing: New Constraints on Cosmology and the Distribution of Dark Matter'' of the EC under contract No. ERBFMRX-CT97-0172.

Appendix A: Bispectrum and non-linear evolution

The bispectrum can be estimated using second-order perturbation
theory. Indeed, an expansion of the density field to second nonlinear
order as

(A.1) |

where is of order and represents departures from Gaussian behaviour, yields the bispectrum

The first term in Eq. (A.2) vanishes because the density fluctuation field is Gaussian to first order, hence the third moment of is zero. Thus, the leading term in Eq. (A.2) is of the order of and can be quantified using second-order perturbation theory.

The bispectrum
is defined only for closed
triangles formed by the wave vectors
.
It can be
expressed as a function of the second-order kernel
and the power spectrum

For describing the bispectrum on all scales, we use the fitting formula derived by Scoccimarro & Couchman (2000) for the non-linear evolution of the bispectrum in numerical simulations of CDM models, extending previous work for scale-free initial conditions. In that case, we have

with

a(n,k) |
= | ||

b(n,k) |
= | ||

c(n,k) |
= | (A.5) |

and , where , and is the linear power spectrum at the desired redshift. The effective spectral index is taken from the linear power spectrum as well. The function

For more detail, see Scoccimarro & Couchman (2000).

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