Issue 
A&A
Volume 602, June 2017



Article Number  A72  
Number of page(s)  8  
Section  Numerical methods and codes  
DOI  https://doi.org/10.1051/00046361/201730399  
Published online  15 June 2017 
Angpow: a software for the fast computation of accurate tomographic power spectra^{⋆}
LAL, Univ. ParisSud, CNRS/IN2P3, Université ParisSaclay, 91400 Orsay, France
email: campagne@lal.in2p3.fr
Received: 5 January 2017
Accepted: 27 April 2017
Aims. The statistical distribution of galaxies is a powerful probe to constrain cosmological models and gravity. In particular, the matter power spectrum P(k) provides information about the cosmological distance evolution and the galaxy clustering. However the building of P(k) from galaxy catalogs requires a cosmological model to convert angles on the sky and redshifts into distances, which leads to difficulties when comparing data with predicted P(k) from other cosmological models, and for photometric surveys like the Large Synoptic Survey Telescope (LSST). The angular power spectrum C_{ℓ}(z_{1},z_{2}) between two bins located at redshift z_{1} and z_{2} contains the same information as the matter power spectrum, and is free from any cosmological assumption, but the prediction of C_{ℓ}(z_{1},z_{2}) from P(k) is a costly computation when performed precisely.
Methods. The Angpow software aims at quickly and accurately computing the auto (z_{1} = z_{2}) and cross (z_{1} ≠ z_{2}) angular power spectra between redshift bins. We describe the developed algorithm based on developments on the Chebyshev polynomial basis and on the ClenshawCurtis quadrature method. We validate the results with other codes, and benchmark the performance.
Results. Angpow is flexible and can handle any userdefined power spectra, transfer functions, and redshift selection windows. The code is fast enough to be embedded inside programs exploring large cosmological parameter spaces through the C_{ℓ}(z_{1},z_{2}) comparison with data. We emphasize that the Limber’s approximation, often used to speed up the computation, gives incorrect C_{ℓ} values for crosscorrelations.
Key words: largescale structure of Universe / methods: numerical
The C++ code is available from https://gitlab.in2p3.fr/campagne/AngPow
© ESO, 2017
1. Introduction
Cosmology is entering the era of wide and deep surveys of galaxies, such as, for example, with the Dark Energy Spectroscopic Instrument (DESI; Levi et al. 2013), the Large Synoptic Survey Telescope (LSST; Ivezic et al. 2008), and the Euclid satellite (Laureijs et al. 2011), in order to investigate the mechanisms of cosmic acceleration (for a review see Weinberg et al. 2013). Cosmological models can be tested, that is, compared against actual measurements, by studying the statistical properties of galaxy clustering. Several methods exist for this, the most classical ones computing correlations in real (Landy & Szalay 1993) or Fourier space (Feldman et al. 1994). However, for wider and deeper surveys, one may also try to condense the clustering information into redshift bins (“shells”) and compute the auto and crosscorrelations between redshift shells (Asorey et al. 2012). This is known as tomography, and allows for a more precise understanding of potential systematic errors in different redshift regions. Several studies compare the merits of this kind of approach with the more classical ones (Asorey et al. 2012, 2014; Di Dio et al. 2014; Nicola et al. 2014; Lanusse et al. 2015) and try to optimize the binning to keep most of the cosmological information. All previous studies have been based on the Fisher formalism, which considers observables as Gaussian; unrepresentative, however, of real life data.
To go on further and prepare the future tomographic analyses, one needs to implement a full pipeline and test the method with, for instance, a MonteCarlo Markov chain (MCMC) exploration of the cosmological parameter space. But there is a technical bottleneck; running a typical MCMC algorithm in cosmology is already very lengthy and requires computing typically a few 10^{5} models. Each model is the result of a numerical code that solves the cosmological equations (known as “Boltzmann solvers”), such as CLASS^{1} (Blas et al. 2011), which takes typically 5–10 s on eight cores. Today this amounts to several days of computation.
For a tomographic method, one further needs to transform the output of the Boltzmann solver, the matter power spectrum, into the observable space, represented as C_{ℓ}(z_{i},z_{j}) angular power spectra between two redshift shells located at z_{i} and z_{j}. This transformation is numerically challenging because of overlapping integrals between very oscillating spherical Bessel functions.
One then often makes use of the Limber’s approximation, which essentiallyreplaces the Bessel functions by a single Dirac value. However, as we show here, this leads to a poor approximation for autocorrelations and may even be incorrect for crosscorrelations, since it cannot capture any anticorrelation.
We therefore address here the issue of accurately and quickly computing the integrals required to derive the correlations between tomographic bins. Our goal, in computational terms, is that this computation be faster than one typical Boltzmann code computation time, that is, essentially at or below the 1s level (on eight cores). Another aspect of this work is to provide a standalone library that offers a generic interface where the user can plug any matter power spectrum. This is a different approach from CLASSgal (Di Dio et al. 2013) which also provides some theoretical computations related to tomography that are deeply rooted within the CLASS software.
The integrals defining the C_{ℓ}(z_{i},z_{j}) angular power spectra are introduced in Sect. 2. In Sect. 3 we detail the algorithm implemented in Angpow, while we address some numerical tests in Sect. 4. Section 5 provides insight into the code design and we conclude in Sect. 6.
2. The position of the problem
Our aim is to compute the angular over density power spectrum C_{ℓ}(z_{1},z_{2}) as a crosscorrelation between two zshells with mean values (z_{1},z_{2}) and also the autocorrelation C_{ℓ}(z_{1}) with z_{1} = z_{2}, taking into account, in both cases, possible redshift selection functions. Following notations of reference (Di Dio et al. 2013), for a couple of redshift (z_{1},z_{2}) one computes C_{ℓ}(z_{1},z_{2}) according to (1)with on one hand P(k) the nonnormalized primordial power spectrum, and on the other hand, Δ_{ℓ}(z,k), a general function used to describe physical processes down to redshift z (Di Dio et al. 2013; Bonvin & Durrer 2011). At the lowest order, Δ_{ℓ}(z,k) can be expressed as the product of the bias b and a growth factor D(z,k) to account for matter density contribution: (2)where j_{ℓ}(x) is a first kind spherical Bessel function of parameter ℓ, and r(z) is the radial comoving distance of the shell located at redshift z.
For thick redshift shells, one introduces two normalized redshift selection functions W_{1}(z;z_{1},σ_{1}) and W_{2}(z′;z_{2},σ_{2}) around z_{1} and z_{2} with typical width σ_{1} and σ_{2}, respectively. Then, one extends Eq. (1)by (3)It is convenient to introduce the auxiliary function justified in the following section (4)where we have used the factorization of the growth factor D(k,z) from the matter density contribution to introduce the notation P(k,z) and . The dots signify that other contributions may be introduced as the redshift distortions and lensing effects that we ignore here for simplicity. Then, (5)The autocorrelation is a particular case where the redshift selection function W_{2}(z′;z_{2},σ_{2}) is reduced to W_{1}(z′;z_{1},σ_{1}) and we can use a single W function, which leads to (6)To account for infinite redshift precision at z = z_{1}, the use of a Dirac selection function for W yields (7)Angpow is designed to efficiently compute these C_{ℓ} expressions once one provides access to the power spectra P(k,z), the extra function, and the cosmological distance r(z). To simplify the notation, we do not write the width σ of the selection functions if not explicitly needed.
3. A brief description of the computational algorithm
The redshift integral computations of Eq. (5)can be conducted in practice inside the rectangle [z_{1min},z_{1max}] × [z_{2min},z_{2max}] given by the W selection functions using a Cartesian product of a onedimensional (1D) quadrature defined by the set of sample nodes z_{i} and weights w_{i}. In practice, we use the ClenshawCurtis quadrature. The corresponding sampling points (z_{1i},z_{2j}) are weighted by the product w_{i}w_{j} using the 1D quadrature sample points and weights on both redshift regions with i = 0,...,N_{z1}−1 and j = 0,...,N_{z2}−1. Then, one gets the following approximation: (8)with the notations z_{i} = z_{1i}, z_{j} = z_{2j} and r_{i} = r(z_{1i}), r_{j} = r(z_{2j}) and (9)defined with the f_{ℓ}(z,k) function of Eq. (4).
We use a piecewise integration over a userdefined range [k_{min},k_{max}] such that (10)where the bounds are related to the roots of j_{ℓ}(x) noted q_{ℓp} and the userdefined number of roots q_{ℓp} per subinterval (see Appendix A). Then, Eq. (8)may be rewritten as (11)The integral defined as (12)is computed using the 3Calgorithm of appendix A. Investigating the different steps of the algorithm, one notices that the sampling of the function f_{ℓ}(k,z_{i}) along the k axis for a given interval depends on z_{i} but is independent from z_{j} and vice versa for the f_{ℓ}(k,z_{j}) function (those samplings are independent from z_{i}). So, one may proceed to ksampling before performing the double sum over (i,j), which is particularly efficient as the CPU bottleneck is the computation of the spherical Bessel function j_{ℓ}. Angpow uses a spherical Bessel function implementation based on Numerical Recipes (Press et al. 1992). The brute force Cartesian quadrature evolves as O(N_{z1} × N_{z2}), while the optimized version reduces the CPU times scaling to O(N_{z1} + N_{z2}). Therefore, as a matter of efficiency, it is more appropriate to exchange the order of the p and (i,j) summations leading to (13)As a first hint for the 3Calgorithm, we use typically 2^{8}−2^{9} polynomial approximations for ℓ_{max} ≈ 500–1000 of the f_{ℓ}(k,r_{i}) and f_{ℓ}(k,r_{j}) functions, 100 spherical Bessel roots per subinterval, 99point ClenshawCurtis quadrature nodes, and weights for z integration in case of σ = 0.02.
4. Numerical tests
4.1. Comparison with other codes
We proceed now to a numerical comparison of the estimation of C_{ℓ}(z_{1}) and C_{ℓ}(z_{1},z_{2}) computed by CLASSgal (Di Dio et al. 2013) and Mathematica (Wolfram Research Inc. 2016) with Dirac redshift selection functions. Concerning CLASSgal (v1.1.3), we have started with the provided explanatory.ini file where we have modified the cosmological parameters to: h = 0.679, Ω_{b} = 0.0483, Ω_{cdm} = 0.2582 and Ω_{k} = Ω_{fld} = 0. We have also set k_scalar_max_tau0_over_l_max to fix the upper bound of the kintegration taking into account the maximal ℓ value and the redshift mean value. Concerning Angpow we have taken advantage of the possibility to read an external file produced by CLASSgal as an input P(k) computed at z = 0 in association to the growth function computed internally given by Lahav et al. (1991), Carroll et al. (1992). Finally, to avoid the Limber’s approximation for CLASSgal, we have set the “Limber” threshold much higher than the ℓ upper limit.
Fig. 1
Comparison of the computations of the given by Angpow, Mathematica, and CLASSgal for a Dirac selection function with k_{max} = 10 Mpc^{1}. 
Fig. 2
Computations of with Dirac selections centered at z ∈ {0.85,1.00,1.15} with Angpow alone and with Angpow and CLASSgal using a Gaussian selection function with mean z = 1, a width of σ = 0.3, and a redshift cut at ± 5σ (in all cases k_{max} = 0.44 Mpc^{1}). For Angpow we use the power spectrum produced by CLASSgal and we have varied the redshift grid sampling resolution from 9 × 9 to 159 × 159 points to reach the converged result (blue curve) in good agreement with the CLASSgal result (cyan points). Comparing the to the results we measure the effect of selfcrosscorrelation inside a thick redshift shell which washes out the matter fluctuation contrast. 
Results of the autocorrelation computations at z = 1 using Dirac selection functions are shown in Fig. 1. As the three softwares use the same primordial power spectrum, all the results agree with one other within a maximal relative error of 0.06% on the whole ℓ range.
Fig. 3
Comparison between Angpow (red/orange points) and CLASSgal (black points) for several crosscorrelation spectra with Gaussian (σ = 0.01) selection and k_{max} = 1 Mpc^{1}. The orange points are used to emphasize negative C_{ℓ}. 
The autocorrelation computation within a thick redshift band, selected by a Gaussian of mean z = 1 and σ = 0.03 cut at ± 5σ, has been used as a test bench. But, for this test we have neglected the Mathematica software, which is too slow, and have restricted testing to comparison of Angpow to CLASSgal. Figure 2 shows computation results. The orange, violet, and red curves are produced by Angpow using Dirac selection function in the range ± 5σ around the mean redshift z = 1, while the green, forest green, purple, and blue curves are results of the above mentioned Gaussian selection function but sampled using different grid sizes: 9 × 9, 19 × 19, 39 × 39 and 159 × 159 ClenshawCurtis sample points. As the number of points, or equivalently the quadrature order, increases, the computation converges towards the CLASSgal result (cyan points). We also address the crosscorrelation computations performed by Angpow and CLASSgal () using Gaussian selection functions (σ = 0.01 and a ± 5σ cut). The results are shown in Fig. 3. One should not be surprised by negative values since we are crosscorrelating two different quantities. In both tests we have used the power spectrum computed at z = 0 by CLASSgal as input to Angpow. The agreement between the two software codes is very good, keeping the relative residuals at values less than 0.02%.
4.2. Note on Limber’s approximation
The Angpow library can also be used to compute, if desired, the first order Limber’s approximation (Loverde & Afshordi 2008). In such an approximation, the spherical Bessel function is formally reduced to (14)In such conditions, the product kr(z) is constrained, and if one uses the following notation for the comoving distance computation with d_{H} = c/H_{0}, the Hubble distance (H_{0} = 100h km s^{1} Mpc^{1} and c the speed of light) and E(z), the dimensionless Hubble parameter, (15)Then, Eq. (5)is transformed to the following expression (16)This integral can be computed using a divideandconquer recursive method with the GaussKronrod quadrature (Laurie 1997). The Gauss sample points are a subset of the GaussKronrod sample points and can easily be used to set up an error estimate to drive the recursive algorithm.
Looking at Eq. (16) one realizes that all the terms are positive, indicating that such approximation is not suitable for crosscorrelation where C_{ℓ}(z_{1},z_{2}) is not guaranteed to be positive as can be explicitly seen in Fig. 3. We have proceeded to the computation of C_{ℓ}(z) in the case of a Gaussian selection function of mean z = 1 and σ = 0.03 for both Angpow and CLASSgal, with/without the Limber’s approximation. The results are shown in Fig. 4. The two software codes agree very well and show that the Limber’s approximation can give sizeable errors compared to the exact computation; of the order of the cosmic variance in the given example. So, this Limber’s approximation, even if it runs 100 times faster than the exact computation, should then be used with extreme care not only for crosscorrelation but also for autocorrelation.
Fig. 4
Comparison of the computations of the given by Angpow and CLASSgal either with the Limber’s approximation or the exact computation. 
4.3. Correlations in real space
Angpow can also quickly compute the correlation function in real space from the power spectrum (17)where P_{ℓ} denotes the ℓth order Legendre polynomial. Because the C_{ℓ}(z_{1},z_{2}) values are generally cut at a given ℓ_{max}, one needs to introduce an apodization to avoid ringing due to a sharp cutoff. We introduce a Gaussian one (which is the smoothest in both real and harmonic spaces) as in Di Dio et al. (2013) so that the term in Eq. (17)is (18)The apodization length ℓ_{a} may depend on the signal but for the standard cosmology shown here (around z = 1) we noticed that using l_{a} ≃ 0.4ℓ_{max} gives good results. Correlations in real space are generally easier to comprehend as is shownin Fig. 5, which represents the analogue of Fig. 3 but in real space. Here one may identify a peak, named the “Baryonic Acoustic Oscillation” (e.g., Weinberg et al. 2013) that decreases in the crosscorrelation when the distance between shells increases and is finally washed out.
Fig. 5
Crosscorrelations in real space corresponding to the spectra shown on Fig. 3. The points show where the function was evaluated. 
4.4. Speed tests
Angpow is designed and written in C++ and parallelization is achieved through OpenMP. To qualify the code we provide four input parameter files and their corresponding outputs obtained in one of our runs. In all tests, we used a ΛCDM reference cosmology, a P(k) at z = 0 provided by CLASSgal, ℓ_{max} = 1000, a 3Calgorithm with 2^{9} Chebyshev polynomial order, and 100 roots per subkinterval:

Test 1:
autocorrelation with a Dirac selection function at z = 1 and k_{max} = 10 Mpc^{1};

Test 2:
crosscorrelation with two Dirac selection functions at z = 1 and z = 1.05 and k_{max} = 10 Mpc^{1};

Test 3:
autocorrelation with a Gaussian selection function with (z_{mean} = 1, σ_{z} = 0.02, 5σ_{z}cut) and k_{max} = 1 Mpc^{1} and a radial quadrature based on N_{pts} = 139 sample points;

Test 4:
crosscorrelation with two Gaussian selection functions with (z_{mean,1} = 0.90, z_{mean,2} = 1.00) both with (σ_{z} = 0.02, 5σ_{z}cut) and k_{max} = 1 Mpc^{1} and a radial quadrature based on N_{pts} = 139 sample points.
We have tested the code both on laptop (Linux, MacOSX) as well as on Computer Center (CCIN2P3 in France and NERSC in the USA). We use OpenMP to distribute the computation of a given C_{ℓ} on a single thread. Table 1 gives Central Processing Unit (CPU) execution times averaged over ten processes. The code wall time decreases reasonably well with the number of threads and a wall time at the (1s) level can be reached to reconstruct these accurate spectra. Such performances are much higher than those obtained with CLASSgal when not using the Limber approximation. For example, on our Test3 setup, running the latter takes about 15s (on 16 threads), which is to be compared to about 0.5s in Table 1.
Wall time (in seconds) measured at CCIN2P3 (on Intel Xeon CPU E52640 v3 processors) for the test benches described in the text, according to the number of OpenMP threads used.
Fig. 6
Evolution of the wall time with respect to the width of the selection function (σ) illustrated in the condition of Test 3 and using 16 threads. In the upper scale of the frame is shown an indication of the radial_order used to sample the along the z direction for a given σ (see Sect. 5). 
Figure 6 shows the dependence of the wall time with respect to the width of the selection function (σ) in the conditions of Test 3 using 16 threads. For a given σ, we have chosen the minimal radial_order value such that the relative accuracy on the C_{ℓ} is of the order of 0.01% compared to a computation with a much larger radial_order value (see Sect. 5). If one uses a looser criteria on the accuracy of the C_{ℓ} or if the number of sigma is lower than 5, then one may use a lower radial_order and gain on the wall time.
5. Code design and input parameters
Angpow is written in C++ which allows both good CPU performances and keeps the code flexible. A front end interface to Python is also foreseen and the code is distributed publicly^{2}. The angpow.cc file is an example of the library usage to perform C_{ℓ} and C(θ) computations. We also provide the limber.cc file if one wants to test the Limber’s approximation (Sect. 4.2). The different files are located in selfexplained directories: src, inc/Angpow, lib, data. Finally, a README file provides details for the installation and build procedures.
The two main classes Pk2Cl and KIntegrator (located in angpow_pk2cl.h and angpow_kinteg.h files) are generic codes using abstract base classes. They define interface to the power spectrum function P(ℓ,k,z) (class PowerSpecBase), the generalisation of P(k,z) used in Eq. (5); to the comoving distance computation r(z) (class CosmoCoorBase); and to the radial/redshift selection functions W(z) (class RadSelectBase). The user can derive their own concrete classes to access a third party library or use the ones implemented by default. For instance, we have implemented a file access to a (k,P(k))tuple saved by the CLASSgal output. In this implementation, we have coded the growth factor defined in Lahav et al. (1991), Carroll et al. (1992) as minimal function (Eq. (2)).
To run the executable, one provides an ascii file defining the input parameters that drive the computational conditions of the algorithm and define the I/O locations. Some readymade input parameter files are also provided (angpow_bench<n>.ini) as well as the C_{ℓ} output files (angpow_bench<n>_cl.txt.REF) corresponding to the icpc outputs of Table 1.
Among the different input parameters some are more sensitive than others, as those that deal with the radial/redshift 1D quadrature and the 3C algorithm. Here is a closer look at these parameters:

cl_kmax: this is the maximal value of k in the kintegral. We have not set up an internal algorithm to determine this upper bound. As a hint, one may consider a relation with the factor ℓ_{max}/r(z_{min}). The lower bound on k is internally fixed using the cutoff x_{min}(ℓ) defined as x < x_{min}(ℓ) ⇔ j_{ℓ}(x) < cut (see the input parameters jl_xmin_cut and Lmax_for_xmin set as default to 5 × 10^{10} and 2000, respectively).

radial_order: if noted n, this fixes the number of sample points along one z direction, that is, N_{pts} = 2n−1. The accuracy on the selection function as well as the CPU increase with n but we keep a O(n) complexity of the ksampling (see Fig. 6);

chebyshev_order: if noted N, this fixes the degree d of the Chebyshev polynomial approximation of the f_{ℓ}(k,z_{i}) and f_{ℓ}(k,z_{j}) functions (Eq. (4)); that is, d = 2^{N}. Keeping the same degree of approximation for both functions guarantees a power of 2 for the product approximation. Even if not mandatory, this helps in getting CPU performance for the DCTI transform (using the FFTW library). When ℓ_{max} increases it may be worth updating this parameter by 1 unit step. For ℓ_{max} = 500chebyshev_order is set to 8 by default. Increasing the angular spectrum computation up to ℓ_{max} = 1000 keeping this default value leads to absolute error of the order of 10^{6} for Tests 1 and 2 and 5 × 10^{10} for Test 3 and 4, then to get better accuracy in this case we use chebyshev_order = 9.

n_bessel_roots_per_interval: this is the number of Bessel roots q_{ℓp} used to define the bounds of the integral (Eq. (12)). By default it is set to 100. There is an interplay with the chebyshev_order parameter as a lower n_bessel_roots_per_interval value is coherent with a lower chebyshev_order.

total_weight_cut and deltaR_cut: these two threshold parameters are used to avoid unnecessary 3C algorithm processing (especially for the ksampling of the f_{ℓ}(k,z_{i}) or f_{ℓ}(k,z_{j}) functions). So, we do not consider a couple (z_{i},z_{j}) for which either the product w_{i}w_{j}W(z_{i},z_{1})W(z_{j},z_{2}) is too low (total_weight_cut cut) or the radial distance  r(z_{i})−r(z_{j})  is too large to produce a sizeable contribution to the final C_{ℓ}. The deltaR_cut cut is in Mpc units and is used in conjunction with has_deltaR_cut set to 1. These two threshold parameters depend on the use case under consideration and for preliminary tests we recommend to set both total_weight_cut and has_deltaR_cut to 0.
6. Summary and outlooks
We have set up a fast and generic software to compute the tomographic C_{ℓ}(z_{1},z_{2}) with redshift selection functions. Angpow is versatile enough to accept userdefined matter power spectrum P(k), transfer functions and cosmology. The code provides an accurate computation of the auto and crosscorrelation power spectra, checked by comparison with other codes, which is fast enough to be included inside MCMC cosmology softwares. The rapidity of the software relies on the use of the 3Calgorithm, adapted to the computation of integrals over spherical Bessel functions, while other codes rely on the Limber’s approximation. We emphasize that the Limber’s approximation can lead to incorrect C_{ℓ}(z_{1},z_{2}), especially in the case of crosscorrelations, as Limber’s Dirac functions cannot model the interferences between two spherical Bessel functions at different redshifts.
This code is thus fast and accurate enough to be used to test cosmological parameters, and perform tomographic analysis of the galaxy distribution. The definition of the function is general and can accept zero order function as , but also relativistic corrections such as the redshift space distortions or the gravitational lensing; despite these corrections, it can also contain spherical Bessel functions (see e.g., Di Dio et al. 2013). Because Angpow provides the correct angular power spectra for crosscorrelations, it can be a key tool to perform an integrated approach to cosmology, as advertised in Nicola et al. (2016). In particular, we propose this tool for deriving the auto and crosscorrelation angular power spectra for galaxy clustering, but also with angular power spectra from cosmic shear and the cosmological microwave background.
Angpow is publicly available^{3} and can be interfaced to other codes; a Python interface is foreseen. At the moment the code only accepts two redshift bins but soon it will be generalized to any number of bins. Feedback from Angpow users would be greatly accepted.
Acknowledgments
The authors want to thank M. Reinecke who kindly provided pieces of the code, and J. D. McEwen for fruitful discussion on the Chebyshev transform.
References
 Asorey, J., Crocce, M., Gaztañaga, E., & Lewis, A. 2012, MNRAS, 427, 1891 [NASA ADS] [CrossRef] [Google Scholar]
 Asorey, J., Crocce, M., & Gaztañaga, E. 2014, MNRAS, 445, 2825 [NASA ADS] [CrossRef] [Google Scholar]
 Baszenski, G., & Tasche, M. 1997, Linear Algebra and its Application, 252, 1 [CrossRef] [Google Scholar]
 Blas, D., Lesgourgues, J., & Tram, T. 2011, JCAP, 2011, 034 [NASA ADS] [CrossRef] [Google Scholar]
 Bonvin, C., & Durrer, R. 2011, Phys. Rev. D, 84, 063505 [NASA ADS] [CrossRef] [Google Scholar]
 Carroll, S. M., Press, W. H., & Turner, E. L. 1992, ARA&A, 30, 499 [Google Scholar]
 Di Dio, E., Montanari, F., Lesgourgues, J., & Durrer, R. 2013, JCAP, 11, 044 [CrossRef] [Google Scholar]
 Di Dio, E., Montanari, F., Durrer, R., & Lesgourgues, J. 2014, JCAP, 1, 042 [CrossRef] [Google Scholar]
 Feldman, H. A., Kaiser, N., & Peacock, J. A. 1994, ApJ, 426, 23 [NASA ADS] [CrossRef] [Google Scholar]
 Frigo, M., & Johnson, S. G. 2005, special issue on “Program Generation, Optimization, and Platform Adaptation, Proc. IEEE, 93, 216 [Google Scholar]
 Giorgi, P. 2012, IEEE Trans. Comput., 61, 780 [CrossRef] [Google Scholar]
 Glasser, M. L., & Montaldi, E. 1993, ArXiv eprints [arXiv:math/9307213] [Google Scholar]
 Ivezic, Z., Tyson, J. A., Abel, B., et al. 2008, ArXiv eprints [arXiv:0805.2366] [Google Scholar]
 Lahav, O., Lilje, P. B., Primack, J. R., & Rees, M. J. 1991, MNRAS, 251, 128 [NASA ADS] [CrossRef] [Google Scholar]
 Landy, S. D., & Szalay, A. S. 1993, ApJ, 412, 64 [NASA ADS] [CrossRef] [Google Scholar]
 Lanusse, F., Rassat, A., & Starck, J.L. 2015, A&A, 578, A10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv eprints [arXiv:1110.3193] [Google Scholar]
 Laurie, D. P. 1997, Math. Comput., 466, 1133 [NASA ADS] [CrossRef] [Google Scholar]
 Levi, M., Bebek, C., Beers, T., et al. 2013, ArXiv eprints [arXiv:1308.0847] [Google Scholar]
 Loverde, M., & Afshordi, N. 2008, Phys. Rev. D, 78, 123506 [NASA ADS] [CrossRef] [Google Scholar]
 Lucas, S., & Stone, H. 1995, J. Comput. Appl. Math., 64, 217 [CrossRef] [Google Scholar]
 Nicola, A., Refregier, A., Amara, A., & Paranjape, A. 2014, Phys. Rev. D, 90, 063515 [NASA ADS] [CrossRef] [Google Scholar]
 Nicola, A., Refregier, A., & Amara, A. 2016, Phys. Rev. D, 94, 083517 [NASA ADS] [CrossRef] [Google Scholar]
 Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. 1992, Numerical Recipes in C, 2nd edn.: The Art of Scientific Computing (New York: Cambridge University Press) [Google Scholar]
 Waldvogel, J. 2006, BIT Numerical Mathematics, 46, 195 [CrossRef] [Google Scholar]
 Weinberg, D. H., Mortonson, M. J., Eisenstein, D. J., et al. 2013, Phys. Rep., 530, 87 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
 Wolfram Research Inc. 2016, Mathematica 11.0, Champaign, Illinois, USA [Google Scholar]
Appendix A: ClenshawCurtisChebyshev algorithm (3Calgorithm)
Each integral type of Eq. (12)involves the product of “highly” oscillatory functions. The purpose of this section is not to provide a review of all the integration methods used in the different fields of physics to tackle such a difficult task. To focus on our case, where we have to deal with (at least) the product of spherical Bessel functions, the authors point out that the reader may find either specific integral solving rules as in Glasser & Montaldi (1993) or general methods as in Lucas & Stone (1995). However we need a precise and also very fast method. We cannot rely on methods that solve the problem of a product of spherical Bessel functions multiplied by a regular function. In fact, both the primordial power spectrum and the extension beyond the matter density may show oscillation features in the form of derivative of spherical Bessel functions. So, we have searched and found a general method that meets our requirements of precision and speed.
Equation (12)is a special case of the following generic integral after a proper change of variable (A.1)To get an approximate value of this integral, we use in this section the ClenshawCurtis quadrature at order N_{cc} (noting h = f × g): (A.2)where the sampling points are defined as x_{k} = coskπ/N_{cc} (k = 0,...,N_{cc}) and the corresponding weights w_{k} are addressed later in the section. But, if the functions f and g have a highly oscillatory behavior, one needs, in principle, to use large values of N_{cc} to reach a sufficient accuracy level. In that case, dealing with the above sum may not be computationally efficient. The idea is then to use Chebyshev series to approximate both functions f and g. Then, one performs the product of both Chebyshev series, which is also a Chebyshev series but with a higher order, and finally one uses a fast ClenshawCurtis weights computation to perform the last weighted sum. We briefly describe those steps leaving the details of the demonstration that the interested reader can find in Baszenski & Tasche (1997).
Let f_{N} be a polynomial approximation of f of degree N−1. We expend f_{N} onto the following basis of the first kind of Chebyshev polynomials { T_{n};n = 0,...,N−1 } which have the property T_{n}(cosθ) = cosnθ: (A.3)To determine the a_{k} coefficients one uses the following sampling vector (A.4)of length N + 1 and related to the vector a^{(N)} = (a_{0},...,a_{N−1},0)^{T} by the linear algebra relation (A.5)with being a discrete cosine transform of typeI (DCTI) matrix of dimension (N + 1)^{2}. Similarly, we note g_{M} a polynomial approximation of g of degree M−1 from which we determine the sampling vector g^{(M)} using the sample points . The coefficient vector b^{(M)} = (b_{0},...,b_{M−1},0)^{T} is related to g^{(M)} using a relation similar to Eq. (A.5), namely (A.6)By combining the polynomial approximations f_{N} and g_{M}, the function h is then approximated by a Chebyshev series of degree N + M−2 with coefficient vector c^{(P)} of length P + 1 with P ≥ N + M−1. Using a relation of type Eq. (A.5), the vector c^{(P)} is related to the sampling vector (A.7)To get h^{(P)} it is not necessary to compute c^{(P)} and proceed to an inversion of a DCTI matrix. The key point is that if we note ⊙ , the componentwise multiplication, one has (A.8)Moreover, f^{(P)} (g^{(P)}) is obtained from a^{(N)} (b^{(M)}) of length N + 1 (M + 1) by an extension to a larger vector at least of length N + M noted () by appending with zeros: (A.9)Then, the sampling vector used in Eq. (A.2)where one identifies N_{cc} = P is determined by (A.10)using the DCTI matrix of dimension (N_{cc} + 1)^{2} and the inversion property . In some sense, for both f and g approximation sampling vectors, we have performed a Chebyshev basis change to a larger parameter space compatible with the polynomial degree involved in the product f^{(N)} × g^{(M)}.
The second key point is that the ClenshawCurtis weights associated to h^{(Ncc)} in Eq. (A.2)can also be computed with a DCTI transform from the vector (2 /N_{cc})(1,0,−1/3,0,−1/15,...,((−1)^{k} + 1)/2(1−k^{2}),... ) of length N_{cc} + 1 (Waldvogel 2006) (the normalization depends on the exact definition of the DCTI used).
So, to perform the integral given by Eq. (A.2), one needs 4 + 1 DCTI transforms, 1 for the ClenshawCurtis weights and 4 to transform the Chebyshev coefficients vectors. The DCTI transform may be implemented using O(nlog n) efficient algorithm, for example, the FFTW library (Frigo & Johnson 2005) used by Angpow. Angpow uses a power of 2 for N, M, and also P (keeping P ≥ N + M−1) for a fast implementation. The 3Calgorithm is a special case of a more general class of algorithms dealing with the product of polynomials. We note that according to reference (Giorgi 2012) an even faster algorithm (although moderate) might be implemented in a future version of Angpow if necessary. We note finally that this general method can be applied to use cases beyond the power spectrum computation in other fields of interest.
All Tables
Wall time (in seconds) measured at CCIN2P3 (on Intel Xeon CPU E52640 v3 processors) for the test benches described in the text, according to the number of OpenMP threads used.
All Figures
Fig. 1
Comparison of the computations of the given by Angpow, Mathematica, and CLASSgal for a Dirac selection function with k_{max} = 10 Mpc^{1}. 

In the text 
Fig. 2
Computations of with Dirac selections centered at z ∈ {0.85,1.00,1.15} with Angpow alone and with Angpow and CLASSgal using a Gaussian selection function with mean z = 1, a width of σ = 0.3, and a redshift cut at ± 5σ (in all cases k_{max} = 0.44 Mpc^{1}). For Angpow we use the power spectrum produced by CLASSgal and we have varied the redshift grid sampling resolution from 9 × 9 to 159 × 159 points to reach the converged result (blue curve) in good agreement with the CLASSgal result (cyan points). Comparing the to the results we measure the effect of selfcrosscorrelation inside a thick redshift shell which washes out the matter fluctuation contrast. 

In the text 
Fig. 3
Comparison between Angpow (red/orange points) and CLASSgal (black points) for several crosscorrelation spectra with Gaussian (σ = 0.01) selection and k_{max} = 1 Mpc^{1}. The orange points are used to emphasize negative C_{ℓ}. 

In the text 
Fig. 4
Comparison of the computations of the given by Angpow and CLASSgal either with the Limber’s approximation or the exact computation. 

In the text 
Fig. 5
Crosscorrelations in real space corresponding to the spectra shown on Fig. 3. The points show where the function was evaluated. 

In the text 
Fig. 6
Evolution of the wall time with respect to the width of the selection function (σ) illustrated in the condition of Test 3 and using 16 threads. In the upper scale of the frame is shown an indication of the radial_order used to sample the along the z direction for a given σ (see Sect. 5). 

In the text 
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