The ALHAMBRA survey: An empirical estimation of the cosmic variance for merger fraction studies based on close pairs^{⋆,}^{⋆⋆}
^{1}
Centro de Estudios de Física del Cosmos de Aragón, Plaza San Juan
1,
44001
Teruel,
Spain
email:
clsj@cefca.es
^{2}
Instituto de Astrofísica de Andalucía (IAACSIC),
Glorieta de la astronomía
s/n, 18008
Granada,
Spain
^{3}
Institute for Computational Cosmology, Department of Physics,
Durham University, South
Road, Durham
DH1 3LE,
UK
^{4}
Institut de Ciències de l’Espai (IEECCSIC), Facultat de Ciéncies,
Campus UAB,
08193
Bellaterra,
Spain
^{5}
Instituto de Física de Cantabria, Avenida de los Castros s/n, 39005
Santander,
Spain
^{6}
Observatori Astronòmic, Universitat de València,
C/ Catedrático José Beltrán
2, 46980
Paterna,
Spain
^{7}
GEPI, Paris Observatory, 77 av. Denfert Rochereau, 75014
Paris,
France
^{8}
University Denis Diderot, 4 rue Thomas Mann, 75205
Paris,
France
^{9}
Departament d’Astronomia i Astrofísica, Universitat de
València, 46100
Burjassot,
Spain
^{10}
Instituto de Astrofísica de Canarias, vía Láctea s/n, La Laguna, 38200
Tenerife,
Spain
^{11}
Departamento de Astrofísica, Facultad de Física, Universidad de la
Laguna, 38200
La Laguna,
Spain
^{12}
Observatório NacionalMCT, Rua José Cristino 77, CEP 20921400, Rio de Janeiro  RJ, Brazil
^{13}
Department of Theoretical Physics, University of the Basque
Country UPV/EHU, 48080
Bilbao,
Spain
^{14}
Departamento de Física Atómica, Molecular y Nuclear, Facultad de
Física, Universidad de Sevilla, 41012
Sevilla,
Spain
^{15}
Departamento de Astronomía y Astrofísica, Facultad de Física,
Pontificia Universidad Católica de Chile, 7820436
Santiago,
Chile
Received:
12
August
2013
Accepted:
17
January
2014
Aims. Our goal is to estimate empirically the cosmic variance that affects merger fraction studies based on close pairs for the first time.
Methods. We compute the merger fraction from photometric redshift close pairs with 10 h^{1} kpc ≤ r_{p} ≤ 50 h^{1} kpc and Δv ≤ 500 km s^{1} and measure it in the 48 subfields of the ALHAMBRA survey. We study the distribution of the measured merger fractions that follow a lognormal function and estimate the cosmic variance σ_{v} as the intrinsic dispersion of the observed distribution. We develop a maximum likelihood estimator to measure a reliable σ_{v} and avoid the dispersion due to the observational errors (including the Poisson shot noise term).
Results. The cosmic variance σ_{v} of the merger fraction depends mainly on (i) the number density of the populations under study for both the principal (n_{1}) and the companion (n_{2}) galaxy in the close pair and (ii) the probed cosmic volume V_{c}. We do not find a significant dependence on either the search radius used to define close companions, the redshift, or the physical selection (luminosity or stellar mass) of the samples.
Conclusions. We have estimated the cosmic variance that affects the measurement of the merger fraction by close pairs from observations. We provide a parametrisation of the cosmic variance with n_{1}, n_{2}, and V_{c}, σ_{v} ∝ n_{1}^{0.54}V_{c}^{0.48} (n_2/n_1)^{0.37} . Thanks to this prescription, future merger fraction studies based on close pairs could properly account for the cosmic variance on their results.
Key words: galaxies: interactions / galaxies: fundamental parameters / galaxies: statistics
Based on observations collected at the GermanSpanish Astronomical Center, Calar Alto, jointly operated by the MaxPlanckInstitut für Astronomie (MPIA) at Heidelberg and the Instituto de Astrofísica de Andalucía (IAACSIC).
Appendix is available in electronic form at http://www.aanda.org
© ESO, 2014
1. Introduction
Our understanding of the formation and evolution of galaxies across cosmic time has been greatly improved in the last decade thanks to deep photometric and spectroscopic surveys. Some examples of these successful deep surveys are SDSS (Sloan Digital Sky Survey, Abazajian et al. 2009), GOODS (Great Observatories Origins Deep Survey, Giavalisco et al. 2004), AEGIS (AllWavelength Extended Groth Strip International Survey, Davis et al. 2007), ELAIS (European LargeArea ISO Survey, RowanRobinson et al. 2004), COSMOS (Cosmological Evolution Survey, Scoville et al. 2007), MGC (Millennium Galaxy Catalogue, Liske et al. 2003), VVDS (VIMOS VLT Deep Survey, Le Fèvre et al. 2005, 2013), DEEP (Deep Extragalactic Evolutionary Probe, Newman et al. 2013), zCOSMOS (Lilly et al. 2009), GNS (GOODS NICMOS Survey, Conselice et al. 2011), SXDS (Subaru/XMMNewton Deep Survey, Furusawa et al. 2008), or CANDELS (Cosmic Assembly NIR Deep Extragalactic Legacy Survey, Grogin et al. 2011; Koekemoer et al. 2011).
One fundamental uncertainty in any observational measurement derived from galaxy surveys is the cosmic variance (σ_{v}), arising from the underlying largescale density fluctuations and leading to variances larger than those expected from simple Poisson statistics. The most efficient way to tackle the cosmic variance is to split the survey in several independent areas in the sky. This minimises the sampling problem and is better than increasing the volume in a wide contiguous field (e.g., Driver & Robotham 2010). However, observational constraints (depth vs. area) lead to many existing surveys that have observational uncertainties dominated by the cosmic variance. Thus, a proper estimation of σ_{v} is needed to fully describe the error budget in deep cosmological surveys.
The impact of the cosmic variance in a given survey and redshift range can be estimated using two basic methods: theoretically, by analysing cosmological simulations (e.g., Somerville et al. 2004; Trenti & Stiavelli 2008; Stringer et al. 2009; Moster et al. 2011), or empirically, by sampling a larger survey (e.g., Driver & Robotham 2010). Unfortunately, previous studies only estimate the cosmic variance affecting number density measurements and do not tackle the impact of σ_{v} in other important quantities as the merger fraction. Merger fraction studies based on close pair statistics measure the correlation of two galaxy populations at small scales (≤100 h^{1} kpc), so the amplitude of the cosmic variance and its dependence on galaxy properties, probed volume, etc. should be different than those in number density studies. In the present paper, we take advantage of the unique design, depth, and photometric redshift accuracy of the ALHAMBRA^{1} (Advanced, Large, Homogeneous Area, MediumBand Redshift Astronomical) survey (Moles et al. 2008) to estimate empirically, for the first time, the cosmic variance that affects close pair studies. The ALHAMBRA survey has observed eight separate regions of the northern sky, comprising 48 subfields of ~180 arcmin^{2} each that can be assumed as independent for our purposes. Thus, ALHAMBRA provides 48 measurements of the merger fraction across the sky. The intrinsic dispersion in the distribution of these merger fractions, which we characterise in the present paper, is an observational estimation of the cosmic variance σ_{v}.
The paper is organised as follows. In Sect. 2, we present the ALHAMBRA survey and its photometric redshifts, and in Sect. 3, we review the methodology to measure close pair merger fractions when photometric redshifts are used. We present our estimation and characterisation of the cosmic variance for close pair studies in Sect. 4. In Sect. 5, we summarise our work and present our conclusions. Throughout this paper, we use a standard cosmology with Ω_{m} = 0.3, Ω_{Λ} = 0.7, H_{0} = 100h km s^{1} Mpc^{1}, and h = 0.7. Magnitudes are given in the AB system.
2. The ALHAMBRA survey
The ALHAMBRA survey provides a photometric data set over 20 contiguous, equalwidth (~300 Å), nonoverlapping, mediumband optical filters (3500 Å−9700 Å) plus 3 standard broadband nearinfrared (NIR) filters (J, H, and K_{s}) over 8 different regions of the northern sky (Moles et al. 2008). The survey has the aim of understanding the evolution of galaxies throughout cosmic time by sampling a large enough cosmological fraction of the universe, for which reliable spectral energy distributions (SEDs) and precise photometric redshifts (z_{p}) are needed. The simulations of Benítez et al. (2009), which relates the image depth and the accuracy of the photometric redshifts to the number of filters, have demonstrated that the filter set chosen for ALHAMBRA can achieve a photometric redshift precision that is three times better than a classical 4 − 5 optical broadband filter set. The final survey parameters and scientific goals, as well as the technical properties of the filter set, were described by Moles et al. (2008). The survey has collected its data for the 20+3 opticalNIR filters in the 3.5 m telescope at the Calar Alto observatory, using the widefield camera LAICA (Large Area Imager for Calar Alto) in the optical and the OMEGA2000 camera in the NIR. The full characterisation, description, and performance of the ALHAMBRA optical photometric system were presented in AparicioVillegas et al. (2010). A summary of the optical reduction can be found in CristóbalHornillos et al. (in prep.), while that of the NIR reduction is in CristóbalHornillos et al. (2009).
The ALHAMBRA survey has observed eight wellseparated regions of the northern sky. The widefield camera LAICA has four chips with a 15′ × 15′ fieldofview each (0.22 arcsec/pixel). The separation between chips is also 15′. Thus, each LAICA pointing provides four separated areas in the sky (black or red squares in Fig. 1). Six ALHAMBRA regions comprise two LAICA pointings. In these cases, the pointings define two separate strips in the sky (Fig. 1). In our study, we assumed the four chips in each strip as independent subfields. The photometric calibration of the field ALHAMBRA1 is currently onongoing, and the fields, ALHAMBRA4 and ALHAMBRA5, comprise of one pointing each (see Molino et al. 2013, for details). We summarise the properties of the seven ALHAMBRA fields used in the present paper in Table 1. At the end, ALHAMBRA comprises 48 subfields of ~180 arcmin^{2}, which we assumed to be independent, in which we measured the merger fraction following the methodology described in Sect. 3. When we searched for close companions near the subfield boundaries, we did not consider the observed sources in the adjacent fields to keep the measurements independent. We prove the independence of the 48 ALHAMBRA subfields in Sect 4.6.
Fig. 1 Schematic view of the ALHAMBRA field’s geometry in the sky plane. We show the eight subfields (one per LAICA chip) of the field ALHAMBRA6. The black and red squares mark the two LAICA pointings in this particular field. The geometry of the other seven fields is similar. A colour version of this plot is available in the electronic edition. 

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ALHAMBRA survey fields.
2.1. Bayesian photometric redshifts in ALHAMBRA
We rely on the ALHAMBRA photometric redshifts to compute the merger fraction (Sect. 3). The photometric redshifts used all over the present paper are fully presented and tested in Molino et al. (2013), and we summarise their principal characteristics below.
The photometric redshifts of ALHAMBRA were estimated with BPZ2.0, a new version of BPZ (Benítez 2000). The BPZ is a SEDfitting method based in a Bayesian inference, where a maximum likelihood is weighted by a prior probability. The library of 11 SEDs (4 ellipticals, 1 lenticular, 2 spirals, and 4 starbursts) and the prior probabilities used by BPZ2.0 in ALHAMBRA are detailed in Benítez (in prep.). The ALHAMBRA photometry used to compute the photometric redshifts is PSFmatched aperturecorrected and based on isophotal magnitudes. In addition, a recalibration of the zero point of the images was performed to enhance the accuracy of the photometric redshifts. Sources were detected in a synthetic F814W filter image, as noted i in the following, defined to resemble the HST/F814W filter. The areas of the images affected by bright stars, as well as those with lower exposure times (e.g., the edges of the images), were masked following ArnalteMur et al. (2013). The total area covered by the ALHAMBRA survey after masking is 2.38 deg^{2}. Finally, a statistical star/galaxy separation is encoded in the variable Stellar_Flag of the ALHAMBRA catalogues, and throughout this paper, we keep those ALHAMBRA sources with Stellar˙Flag≤ 0.5 as galaxies.
The photometric redshift accuracy, as estimated by comparison with spectroscopic redshifts (z_{s}), is δ_{z} = 0.0108 at i ≤ 22.5 with a fraction of catastrophic outliers of η = 2.1%. The variable δ_{z} is the normalized median absolute deviation of the photometric versus spectroscopic redshift distribution (Ilbert et al. 2006; Brammer et al. 2008), (1)The variable η is defined as the fraction of galaxies with  z_{p} − z_{s}  / (1 + z_{s}) > 0.2. We illustrate the high quality of the ALHAMBRA photometric redshifts in Fig. 2. We refer to Molino et al. (2013) for a more detailed discussion.
Fig. 2 Photometric redshift (z_{p}) versus spectroscopic redshift (z_{s}) for the 3813 galaxies in the ALHAMBRA area with i ≤ 22.5 and a measured z_{s}. The solid line marks identity. The sources above and below the dashed lines are catastrophic outliers. The accuracy of the photometric redshifts (δ_{z}) and the fraction of catastrophic outliers (η) are labelled in the panel. A colour version of this plot is available in the electronic edition. 

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The odds quality parameter, as noted , is a proxy for the photometric redshift accuracy of the sources and is also provided by BPZ2.0. The odds is defined as the redshift probability enclosed on a ± K(1 + z) region around the main peak in the probability distribution function (PDF) of the source, where the constant K is specific for each photometric survey. Molino et al. (2013) find that K = 0.0125 is the optimal value for the ALHAMBRA survey. The parameter is related with the confidence of the z_{p}, making it possible to derive high quality samples with better accuracy and lower rate of catastrophic outliers. For example, a selection for i ≤ 22.5 galaxies yields δ_{z} = 0.0094 and η = 1%, while δ_{z} = 0.0061 and η = 0.8% for (see Molino et al. 2013, for further details). We explore the optimal odds selection in ALHAMBRA for close pair studies in Sect. 4.3.
Reliable photometric redshift errors (σ_{zp}) are needed to compute the merger fraction in photometric samples (Sect. 3). In addition to the z_{p}, we have the and the of each source, which are defined as the redshifts that enclose 68% of the PDF of the source. We estimated the photometric redshift error of each individual source as . The constant C is estimated from the distribution of the variable (2)The variable Δ_{z} should be normally distributed with a zero mean and unit variance if the values of σ_{zp} from ALHAMBRA are a good descriptor for the accuracy of the photometric redshifts (e.g., Ilbert et al. 2009; Carrasco Kind & Brunner 2013). We find that Δ_{z} is described well by a normal function when C = 0.49 (Fig. 3, see also Molino et al. 2013). With the definition of and , note that C = 0.5 was expected. This result also implies that the Gaussian approximation of the PDF assumed in the estimation of the merger fraction (Sect. 3) is statistically valid, even if the actual PDF of the individual sources could be multimodal and/or asymmetric at faint magnitudes. We estimated C for different iband magnitudes and odds selections, finding that the C values are consistent with the global one within ± 0.1. Thus, we conclude that σ_{zp} provides a reliable photometric redshift error for every ALHAMBRA source.
Fig. 3 Distribution of the variable Δ_{z} for the 3813 galaxies in the ALHAMBRA area with i ≤ 22.5 and a measured spectroscopic redshift. The red line is the best leastsquares fit of a Gaussian function to the data. The median, dispersion, and the factor C derived from the fit are labelled in the panel. A colour version of this plot is available in the electronic edition. 

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2.2. Sample selection
Throughout this paper, we focus our analysis on the galaxies in the ALHAMBRA first data release^{2}. This catalogue comprises ~500k sources and is complete (5σ, 3″ aperture) for i ≤ 24.5 galaxies (Molino et al. 2013). We explored different apparent luminosity subsamples from i ≤ 23 to i ≤ 20. This ensures excellent photometric redshifts and provides reliable merger fraction measurements (Sect. 4.3) because the PDFs of i ≤ 23 sources are defined well by a single Gaussian peak (Molino et al. 2013). In Sect. 4.7, we also study the cosmic variance in luminosity and stellar massselected samples. The Bband luminosities and the stellar masses of the ALHAMBRA sources were also provided by BPZ2.0 and are included in the ALHAMBRA catalogue (see Molino et al. 2013, for further details). The masstolight ratios from Taylor et al. (2011) and a Chabrier (2003) initial mass function were assumed in the estimation of the stellar masses.
3. Measuring of the merger fraction in photometric samples
The linear distance between two sources can be obtained from their projected separation, r_{p} = φd_{A}(z_{1}), and their restframe relative velocity along the line of sight, Δv = c  z_{2} − z_{1}  / (1 + z_{1}), where z_{1} and z_{2} are the redshift of the principal (more luminous or massive galaxy in the pair) and the companion galaxy, respectively; φ is the angular separation in arcsec of the two galaxies on the sky plane; and d_{A}(z) is the angular diameter distance in kpc arcsec^{1} at redshift z. Two galaxies are defined as a close pair if and Δv ≤ Δv^{max}. The PSF of the ALHAMBRA groundbased images is ≲1.4″ (median seeing of ~1″), which corresponds to 7.6 h^{1} kpc in our cosmology at z = 0.9. To ensure well deblended sources and to minimise colour contamination, we fixed to 10 h^{1} kpc (φ> 1.8″ at z< 0.9). We left kpc as a free parameter and estimate its optimal value in Sect. 4.3. Finally, we set Δv^{max} = 500 km s^{1} by following spectroscopic studies (e.g., Patton et al. 2000; Lin et al. 2008). With the previous constraints, 50%−70% of the selected close pairs finally merge (Patton & Atfield 2008; Bell et al. 2006; Jian et al. 2012).
To compute close pairs, we defined a principal and a companion sample. The principal sample comprises the more luminous or massive galaxy of the pair, and we looked for those galaxies in the companion sample that fulfil the close pair criterion for each galaxy of the principal sample. If one principal galaxy has more than one close companion, we took each possible pair separately (i.e., if the companion galaxies B and C are close to the principal galaxy A, we study the pairs A–B and A–C as independent). In addition, through this paper, we do not impose any luminosity or mass difference between the galaxies in the close pair unless noted otherwise.
With the previous definitions, the merger fraction is (3)where N_{1} is the number of sources in the principal sample and N_{p} the number of close pairs. This definition applies to spectroscopic volumelimited samples, but we rely on photometric redshifts to compute f_{m} in ALHAMBRA. In previous work, LópezSanjuan et al. (2010a) develop a statistical method to obtain reliable merger fractions from photometric redshift catalogues like those from the ALHAMBRA survey. This methodology has been tested with the MGC (LópezSanjuan et al. 2010a) and the VVDS (LópezSanjuan et al. 2012) spectroscopic surveys, and has been successfully applied in the GOODSSouth (LópezSanjuan et al. 2010a) and the COSMOS fields (LópezSanjuan et al. 2012). We recall the main points of this methodology below, and we explore how to apply it optimally over the ALHAMBRA data in Sect. 4.3.
We used the following procedure to define a close pair system in our photometric catalogue (see LópezSanjuan et al. 2010a, for details): first, we search for close spatial companions of a principal galaxy with redshift z_{1} and uncertainty σ_{z1}, assuming that the galaxy is located at z_{1} − 2σ_{z1}. This defines the maximum φ possible for a given in the first instance. If we find a companion galaxy with redshift z_{2} and uncertainty σ_{z2} at , we study both galaxies in redshift space. For convenience, we assume below that every principal galaxy has, at most, one close companion. In this case, our two galaxies could be a close pair in the redshift range (4)Because of the variation in the range [ z^{−},z^{+} ] of the function d_{A}(z), a sky pair at z_{1} − 2σ_{z1} might not be a pair at z_{1} + 2σ_{z1}. We thus impose the condition at all z ∈ [ z^{−},z^{+} ] and redefine this redshift interval if the sky pair condition is not satisfied at every redshift. After this, our two galaxies define the close pair system k in the redshift interval , where the index k covers all the close pair systems in the sample.
The next step is to define the number of pairs associated to each close pair system k. For this, and because all our sources have a photometric redshift, we suppose in the following that a galaxy i in whatever sample is described in redshift space by a Gaussian probability distribution, (5)With the previous distribution, we are able to statistically treat all the available information in redshift space and define the number of pairs at redshift z_{1} in system k as (6)where , the integration limits are the subindex 1 [2] refers to the principal [companion] galaxy in the system k, and the constant C_{k} normalises the function to the total number of pairs in the interest range, (9)Note that ν_{k} = 0 if or . The function ν_{k} tells us how the number of pairs in the system k, as noted by , are distributed in redshift space. The integral in Eq. (6) spans those redshifts in which the companion galaxy has Δv ≤ Δv^{max} for a given redshift of the principal galaxy. This translates to in our redshift range of interest.
With the previous definitions, the merger fraction in the interval z_{r} = [ z_{min},z_{max}) is (10)If we integrate over the whole redshift space, z_{r} = [ 0,∞), Eq. (10) becomes (11)where is analogous to N_{p} in Eq. (3). To estimate the observational error of f_{m}, as noted by σ_{f}, we used the jackknife technique (Efron 1982). We computed partial standard deviations, δ_{k}, for each system k by taking the difference between the measured f_{m} and the same quantity with the kth pair removed for the sample, , such that . For a redshift range with N_{p} systems, the variance is given by .
3.1. Border effects in redshift and in the sky plane
When we search for a primary source’s companion, we define a volume in the sky planeredshift space. If the primary source is near the boundaries of the survey, a fraction of the search volume lies outside of the effective volume of the survey. LópezSanjuan et al. (2010a) find that border effects in the sky plane are representative (i.e., 1σ discrepancy) only at kpc. Thus, we restricted the search radius in our study to kpc.
We avoid the incompleteness in redshift space by including the sources in the samples inside the redshift range [ z_{min},z_{max}) under study and those sources with either z_{p,i} + 2σ_{zp,i} ≥ z_{min} or z_{p,i} − 2σ_{zp,i}<z_{max}.
3.2. The merger rate
The final goal of merger studies is the estimation of the merger rate R_{m}, defined as the number of mergers per galaxy and Gyr^{1}. The merger rate is computed from the merger fraction by close pairs as (12)where C_{m} is the fraction of the observed close pairs that finally merge after a merger time scale T_{m}. The merger time scale and the merger probability C_{m} should be estimated from simulations (e.g., Kitzbichler & White 2008; Lotz et al. 2010a,b; Lin et al. 2010; Jian et al. 2012; Moreno et al. 2013). On the one hand, T_{m} depends mainly on the search radius , the stellar mass of the principal galaxy, and the mass ratio between the galaxies in the pair with a mild dependence on redshift and environment (Jian et al. 2012). On the other hand, C_{m} depends mainly on and environment with a mild dependence on both redshift and the mass ratio between the galaxies in the pair (Jian et al. 2012). Despite the efforts in the literature to estimate both T_{m} and C_{m}, different cosmological and galaxy formation models provide different values within a factor of twothree (e.g., Hopkins et al. 2010). To avoid modeldependent results in the present paper, we focus in the cosmic variance of the observational merger fraction f_{m}.
4. Estimation of the cosmic variance for merger fraction studies
4.1. Theoretical background
In this section, we recall the theoretical background and define the basic variables involved in the cosmic variance definition and characterisation. The relative cosmic variance (σ_{v}) arises from the underlying largescale density fluctuations and leads to variances larger than those expected from simple Poisson statistics. Following Somerville et al. (2004) and Moster et al. (2011), the mean ⟨ N ⟩ and the variance ⟨ N^{2} ⟩ − ⟨ N ⟩ ^{2} in the distribution of galaxies are given by the first and second moments of the probability distribution P_{N}(V_{c}), which describes the probability of counting N objects within a volume V_{c}. The relative cosmic variance is defined as (13)The second term represents the correction for the Poisson shot noise. The second moment of the object counts is (14)where ξ is the twopoint correlation function of the sample under study (Peebles 1980). Combining this with Eq. (13), the relative cosmic variance can be written as (15)Thus, the cosmic variance of a given sample depends on the correlation function of that population. We can approximate the galaxy correlation function in Eq. (15) by the linear theory correlation function for dark matter ξ_{dm}, ξ = b^{2}ξ_{dm}, where b is the galaxy bias. The bias at a fixed scale depends mainly on both redshift and the selection of the sample under study. With this definition of the correlation function, we find that (16)where the power law index α takes into account the extra volume dependence from the integral of the correlation function ξ_{dm} in Eq. (15).
The bias of a particular population is usually measured from the analysis of the correlation function and is well established in that the bias increases with luminosity and stellar mass (see Zehavi et al. 2011; Coupon et al. 2012; Marulli et al. 2013; ArnalteMur et al. 2013, and references therein). The estimation of the bias is a laborious task, so we decided to use the redshift and the number density n of the population under study instead of the bias to characterise the cosmic variance. The number density is an observational quantity that decreases with the increase of the luminosity and the mass selection, so a b ∝ n^{− β} relation is expected. This inverse dependence is indeed suggested by Nuza et al. (2013) results.
In summary, we expect (17)This equation shows that the number density of galaxies, the redshift, and the cosmic volume can be assumed as independent variables in the cosmic variance parametrisation. Equation (17) and the deduction above apply to the cosmic variance in the number of galaxies. We are interested on the cosmic variance of the merger fraction by close pairs, instead, so a dependence on V_{c}, redshift, and the number density of the two populations under study, as noted by n_{1} for principal galaxies and by n_{2} for the companion galaxies, is expected. We therefore used these four variables (n_{1}, n_{2}, z, and V_{c}) to characterise the cosmic variance in close pair studies (Sect. 4.4).
The powerlaw indices in Eq. (17) could be different for luminosity and massselected samples, as well as for fluxlimited samples. In the present paper, we use fluxlimited samples selected in the i band to characterise the cosmic variance. This choice has several benefits, since we have a wellcontrolled selection function, a better understanding of the photometric redshifts and their errors, and we have access to larger samples at lower redshift that in the luminosity and the stellar mass cases. That improves the statistics and increases the useful redshift range. At the end, future studies will be interested on the cosmic variance in physically selected samples (i.e., luminosity or stellar mass). Thus, we compare the results from the fluxlimited iband samples with the actual cosmic variance measured in physically selected samples in Sect. 4.7.
Finally, we set the definition of the number density n. In the present paper, the number density of a given population is the cosmic average number density of that population. For example, if we are studying the merger fraction in a volume dominated by a cluster, we should not use the number density in that volume, but the number density derived from a general luminosity or mass function work instead. Thanks to the 48 subfields in ALHAMBRA we have direct access to the average number densities of the populations under study (Sect. 4.4.1).
Fig. 4 Distribution of the merger fraction f_{m} for i ≤ 22 (top panel) and i ≤ 21 (bottom panel) galaxies in the 48 ALHAMBRA subfields, as measured from close pairs with 10 h^{1} kpc ≤ r_{p} ≤ 30 h^{1} kpc at 0.3 ≤ z< 0.9. In each panel, the red solid line is the best leastsquares fit of a lognormal function to the data. The star and the red bar mark the median and the 68% confidence interval of the fit, respectively. The black bar marks the confidence interval from the maximum likelihood analysis of the data and is our measurement of the cosmic variance σ_{v}. A colour version of this plot is available in the electronic edition. 

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4.2. Distribution of the merger fraction and σ_{v} estimation
In this section, we explore which statistical distribution reproduces the observed merger fractions better and how to reliably measure the cosmic variance σ_{v}. As representative examples, we show the distributions of the merger fraction f_{m} in the 48 ALHAMBRA subfields for i ≤ 22 and i ≤ 21 galaxies in Fig. 4. The merger fraction was measured from close pairs with 10 h^{1} kpc ≤ r_{p} ≤ 30 h^{1} kpc. Unless noted otherwise, the principal and the companion samples in the following comprise the same galaxies. We find that the observed distributions are not Gaussian but follow a lognormal distribution instead, (18)where μ and σ are the median and the dispersion of a Gaussian function in logspace . This is, (19)The 68% confidence interval of the lognormal distribution is [ e^{μ}e^{− σ},e^{μ}e^{σ} ]. This functional distribution was expected for two reasons. First, the merger fraction cannot be negative, implying an asymmetric distribution (Cameron 2011). Second, the distribution of overdense structures in the universe is lognormal (e.g., Coles & Jones 1991; de la Torre et al. 2010; Kovač et al. 2010) and the merger fraction increases with density (Lin et al. 2010; de Ravel et al. 2011; Kampczyk et al. 2013). We checked that the merger fraction follows a lognormal distribution in all the samples explored in the present paper.
The variable σ encodes the relevant information about the dispersion in the merger fraction distribution, including the dispersion due to the cosmic variance. The study of the median value of the merger fraction in ALHAMBRA, as estimated as e^{μ}, and its dependence on z, stellar mass, or colour, is beyond the scope of the present paper, and we will address this issue in a future work.
A best leastsquares fit with a lognormal function to the distributions in Fig. 4 shows that σ increases with the apparent brightness from σ = 0.33 for i ≤ 22 galaxies to σ = 0.62 for i ≤ 21 galaxies. However, the origin of the observed σ is twofold: (i) the intrinsic dispersion due to the cosmic variance σ_{v} (i.e., the fieldtofield variation in the merger fraction because of the clustering of the galaxies) and (ii) the dispersion due to the observational errors σ_{o} (i.e., the uncertainty in the measurement of the merger fraction in a given field, including the Poisson shot noise term). Thus, the dispersion σ reported in Fig. 4 is an upper limit for the actual cosmic variance σ_{v}. We deal with this limitation by applying a maximum likelihood estimator (MLE) to the observed distributions. In Appendix A, we develop a MLE that estimates the more probable values of μ and σ_{v}, assuming that the merger fraction follows a Gaussian distribution in logspace (Eq. (19)) that is affected by known observational errors σ_{o}. We prove that the MLE provides an unbiased estimation of μ and σ_{v} and reliable uncertainties of these parameters. Applying the MLE to the distributions in Fig. 4, we find that σ_{v} is lower than σ, as anticipated, and that the cosmic variance increases with the apparent brightness from σ_{v} = 0.25 ± 0.04 for i ≤ 22 galaxies to σ_{v} = 0.44 ± 0.08 for i ≤ 21 galaxies.
We constrain the dependence of σ_{v} on the number density of the populations under study in Sects. 4.4.1 and 4.4.4 on the probed cosmic volume in Sect. 4.4.2 and on redshift in Sect. 4.4.3. That provides a complete description of the cosmic variance for merger fraction studies. We stress that our definition of σ_{v} differs from the classical definition of the relative cosmic variance presented in Sect. 4.1, which is equivalent to e^{σv}. However, σ_{v} encodes the relevant information needed to estimate the intrinsic dispersion in the measurement of the merger fraction due to the clustering of galaxies.
4.3. Optimal estimation of σ_{v} in the ALHAMBRA survey
In the previous section, we have defined the methodology to compute the cosmic variance from the observed distribution of the merger fraction. However, as shown by LópezSanjuan et al. (2010a), we need a galaxy sample with either small photometric redshift errors or a large fraction of spectroscopic redshifts to avoid projection effects. In the present study, we did not use information from spectroscopic redshifts, so we should check that the photometric redshifts in ALHAMBRA are good enough for our purposes. A natural way to select excellent photometric redshifts in ALHAMBRA is by a selection in the odds parameter. On the one hand, this selection increases the accuracy of the photometric redshifts of the sample and minimises the fraction of catastrophic outliers (Molino et al. 2013), improving the merger fraction estimation. On the other hand, our sample becomes incomplete and could be biased toward a population of either bright galaxies or galaxies with marked features in the SED (i.e., emission line galaxies or old populations with a strong 4000 Å break). In this section, we study how the merger fraction in ALHAMBRA depends on the selection and derive the optimal one to estimate the cosmic variance.
Cosmic variance σ_{v} as a function of the search radius for galaxies at 0.3 ≤ z< 0.9.
Following the methodology from spectroscopic surveys (e.g., Lin et al. 2004; de Ravel et al. 2009; LópezSanjuan et al. 2011, 2013), if we have a population with a total number of galaxies N_{tot} in a given volume and we observe a random fraction f_{obs} of these galaxies, the merger fraction of the total population is (20)where f_{m,obs} is the merger fraction of the observed sample. In ALHAMBRA, we applied a selection in the parameter , so Eq. (20) becomes (21)where is the number of galaxies with odds higher than (i.e., galaxies with ), N_{tot} is the total number of galaxies (i.e., galaxies with ), and is the merger faction of those galaxies with . Because f_{m} must be independent of the selection, the study of f_{m} as a function of provides the clues about the optimal odds selection for merger fraction studies in ALHAMBRA. We show f_{m} as a function of for galaxies with i ≤ 22.5 at 0.3 ≤ z< 0.9 in Fig. 5. We find that

the merger fraction is roughly constant for . This is the expectedresult if the merger fraction is reliable and measured in anonbiased sample. In this particular case, the (0.6) samplecomprises 98% (66%) of the total number of galaxies with i ≤ 22.5;
Fig. 5 Merger fraction f_{m} as a function of the odds selection for i ≤ 22.5 galaxies at 0.3 ≤ z< 0.9. The filled triangles, circles, and squares are for , and 50 h^{1} kpc close pairs, respectively. The open triangles are the observed merger fractions for kpc to illustrate the selection correction from Eq. (21). In several cases, the error bars are smaller than the points. The dotted, dashed, and solid lines mark the average f_{m} at for , and 50 h^{1} kpc close pairs, respectively. A colour version of this plot is available in the electronic edition.
Open with DEXTER Fig. 6 Cosmic variance σ_{v} as a function of for i ≤ 22.5,21.5, and 21 galaxies at 0.3 ≤ z< 0.9 (circles, stars, and triangles, respectively). The horizontal lines mark the errorweighted average of the cosmic variance in each case, and the coloured areas mark their 68% confidence intervals. A colour version of this plot is available in the electronic edition.
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the merger fraction is overestimated for . Even if only a small fraction of galaxies with poor constrains in the photometric redshits are included in the sample, the projection effects become important;

the merger fraction is overestimated for . This behaviour at high odds (i.e., in samples with high quality photometric redshifts) suggests that the retained galaxies are a biased subsample of the general population under study.
In the analysis above, we only accounted for close companions of i ≤ 22.5 galaxies with 10 h^{1} kpc ≤ r_{p} ≤ 30 h^{1} kpc, but we can use other values of or searching over different samples. On the one hand, we repeated the study for and 50 h^{1} kpc, finding the same behaviour than for kpc (Fig. 5). The only differences are that the merger fraction increases with the search radius and that the point starts to deviate from the expected value (the search area increases with and more accurate photometric redshifts are needed to avoid projection effects). On the other hand, we explored a wide range of iband magnitude selections from i ≤ 23 to 20 in the three previous cases. We find again the same behaviour. That reinforces our arguments above and suggests as acceptable odds limits to select samples for merger fraction studies in ALHAMBRA.
The merger fraction increases with the search radius (Fig. 5). However, the merger rate R_{m} (Sect. 3.2) is a physical property of any population, and it cannot depend on . Thus, the increase in the merger fraction with the search radius is compensated with the increase in the merger time scale (e.g., de Ravel et al. 2009; LópezSanjuan et al. 2011). This is, . For the same reason, the cosmic variance of the merger rate cannot depend on . In other words, the 68% confidence interval of the merger rate, [ R_{m}e^{− σv},R_{m}e^{σv} ], should be independent of the search radius. Expanding the previous confidence interval, we find that (22)Note that the dependence on is encoded in the median merger fraction and in the merger time scale. Thus, the cosmic variance σ_{v} of the merger fraction should not depend on the search radius. We checked this prediction by studying the cosmic variance as a function of the search radius for i ≤ 22.5,21.5, and 21 galaxies with at 0.3 ≤ z< 0.9. We find that σ_{v} is consistent with a constant value irrespective of in the three populations probed, as desired (Table 2 and Fig. 6). This supports σ_{v} as a good descriptor of the cosmic variance and our methodology to measure it. In the previous analysis, we have omitted the merger probability C_{m}, which mainly depends on and environment (Sect. 3.2). The merger fraction correlates with environment, so the merger probability could modify the factor e^{σv} in Eq. (22). Because a constant σ_{v} with is observed, the impact of C_{m} in the f_{m} to R_{m} translation should be similar in the range of explored. Detailed cosmological simulations are needed to clarify this issue.
Fig. 7 Cosmic variance σ_{v} as a function of the odds selection for i ≤ 22.5 galaxies at 0.3 ≤ z< 0.9. Triangles, circles, and squares are for , and 50 h^{1} kpc close pairs, respectively. The dotted, dashed, and solid lines mark the average σ_{v} at for , and 50 h^{1} kpc close pairs, respectively. A colour version of this plot is available in the electronic edition. 

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Finally, we studied the dependence of σ_{v} on the odds selection for i ≤ 22.5 galaxies at 0.3 ≤ z< 0.9. Following the same arguments than before, the cosmic variance should not depend on the odds selection. We find that (i) σ_{v} is consistent with a constant value as a function of for any , reinforcing our results above; and (ii) σ_{v} is independent of the odds selection at (Fig.7). As for the merger fraction, we checked that different populations follow the same behaviour. We therefore set as the optimal odds selection to measure the cosmic variance in ALHAMBRA. This selection provides excellent photometric redshifts and ensures representative samples.
In summary, we estimate the cosmic variance σ_{v} from the merger fractions measured in the 48 ALHAMBRA subfields with 10 h^{1} kpc ≤ r_{p} ≤ 50 h^{1} kpc close pairs (the σ_{v} uncertainty is lower for larger search radii) and in samples with in the following. That ensures reliable results in representative (i.e., nonbiased) samples.
4.4. Characterisation of σ_{v}
At this stage, we have set both the methodology to compute a robust cosmic variance from the observed merger fraction distribution (Sect. 4.2) and the optimal search radius and odds selection to estimate σ_{v} in ALHAMBRA (Sect. 4.3). Now, we can characterise the cosmic variance as a function of the populations under study (Sects. 4.4.1 and 4.4.4), the probed cosmic volume (Sect. 4.4.2), and the redshift (Sect. 4.4.3).
4.4.1. Dependence on the number density of the principal sample
Fig. 8 Cosmic variance σ_{v} as a function of the number density n_{1} of the principal population under study. Increasing the number density, the principal sample comprises i ≤ 20, 20.5, 21, 21.5, 22, 22.5, and 23 galaxies, respectively. The probed cosmic volume is the same in all the cases, V_{c} ~ 1.4 × 10^{5} Mpc^{3} (0.3 ≤ z< 0.9). The dashed line is the errorweighted leastsquares fit of a powerlaw to the data, . A colour version of this plot is available in the electronic edition. 

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In this section, we explore how the cosmic variance depends on the number density n_{1} of the principal population under study. For that, we took the same population as principal and companion sample. We study the dependence on the companion sample in Sect. 4.4.4. To avoid any dependence of σ_{v} on either the probed cosmic volume and z and to minimise the observational errors, we focus on the redshift range 0.3 ≤ z< 0.9 in this section. This range probes a cosmic volume of V_{c} ~ 1.4 × 10^{5} Mpc^{3} in each ALHAMBRA subfield. To explore different number densities, we measured the cosmic variance for different iband selected samples from i ≤ 20 to i ≤ 23 in 0.5 magnitude steps. We estimated the average number density n_{1} in the redshift range z_{r} as the median number density in the 48 ALHAMBRA subfields with (23)being the number density in the subfield j and the cosmic volume probed by it at z_{r}. In the measurement of the number density, all the galaxies were taking into account; i.e., any odds selection was applied (). We stress that our measured number densities are unaffected by cosmic variance, and they can be used therefore to characterise σ_{v}. We report our measurements in Table 3.
We find that the cosmic variance increases as the number density decreases (Fig. 8), as expected by Eq. (17). The errorweighted leastsquares fit of a powerlaw to the data is (24)In this section and in the following ones, we used iband selected samples to characterise σ_{v}. We show that the results obtained with these iband samples can be applied to luminosity and stellar massselected samples in Sect. 4.7.
Cosmic variance σ_{v} as a function of the principal sample’s number density n_{1}.
4.4.2. Dependence on the cosmological volume
In this section, we explore the dependence of the cosmic variance on the cosmic volume probed by the survey. We defined as . This erased the dependence on the number density of the population and only volume effects were measured. We explored smaller cosmic volumes than in the previous section by studying (i) different redshift ranges over the full ALHAMBRA area (avoiding redshift ranges smaller than 0.1) and (ii) smaller areas, centred in the ALHAMBRA subfields at 0.3 ≤ z< 0.9. All the cases, as summarised in Table 4, are for i ≤ 23 galaxies. At the end, we explored volumes from V_{c} ~ 0.1 × 10^{5} Mpc^{3} to V_{c} ~ 1.4 × 10^{5} Mpc^{3}. The powerlaw function that better describes the observations (Fig. 9) is (25)We tested the robustness of our result by fitting the two sets of data (variation in redshift and area) separately. We find for the redshift data, while for the area data.
Cosmic variance σ_{v} as a function of the probed cosmic volume V_{c}.
4.4.3. Dependence on redshift
The redshift is an expected parameter in the parametrisation the cosmic variance. However, Fig. 9 shows that the results at different redshifts are consistent with those from the wide redshift range 0.3 ≤ z< 0.9. As a consequence, the redshift dependence of the cosmic variance should be smaller than the typical error in our measurements. We tested this hypothesis by measuring σ_{v} in different, nonoverlapping redshift bins. We summarise our measurements, as performed for i ≤ 23 galaxies, in Table 5. We defined to isolate the redshift dependence of the cosmic variance. We find that is compatible with unity, , and that no redshift dependence remains after accounting for the variation in n_{1} and V_{c} (Fig. 10). This confirms our initial hypothesis and we assume, therefore, γ = 0 in the following.
Fig. 9 Normalised cosmic variance as a function of the probed cosmic volume V_{c} for galaxies with i ≤ 23. The circle corresponds to same data as in Fig. 8. The stars probe different redshift intervals, while triangles probe sky areas smaller than the fiducial ALHAMBRA subfield. The dashed line is the errorweighted leastsquares fit of a powerlaw to the data, . A colour version of this plot is available in the electronic edition. 

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Fig. 10 Normalised cosmic variance as a function of redshift for galaxies with i ≤ 23 (circles). The dashed line marks the errorweighted average of , , and the coloured area shows its 68% confidence interval. A colour version of this plot is available in the electronic edition. 

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4.4.4. Dependence on the number density of the companion sample
As we show in Sect. 3, two different populations are involved in the measurement of the merger fraction: the principal sample and the sample of companions around principal galaxies. In the previous sections, the principal and the companion sample were the same, and here we explore how the number density n_{2} of the companion sample impacts the cosmic variance. We set i ≤ 20.5 galaxies at 0.3 ≤ z< 0.9 as principals and varied the iband selection of the companion galaxies from i ≤ 20.5 to i ≤ 23 in 0.5 steps. As in Sect. 4.4.2, the variable was used.
We find that the cosmic variance decreases as the number density of the companion sample increases (Table 6 and Fig. 11). We fit the dependence with a powerlaw, forcing it to pass for the point . We find that (26)We checked that it is consistent with unity if we leave free the intercept, as we assume . In addition, the powerlaw index changes slightly, .
Cosmic variance σ_{v} as a function of redshift.
Fig. 11 Normalised cosmic variance as a function of the relative number density of the companion and the principal samples under study, n_{2}/n_{1}. Increasing the relative density, the companion sample comprises i ≤ 20.5, 21, 21.5, 22, 22.5, and 23 galaxies, respectively. The red dashed line is the errorweighted leastsquares fit of a powerlaw to the data, . A colour version of this plot is available in the electronic edition. 

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4.4.5. The cosmic variance in merger fraction studies based on close pairs
In the previous sections, we have characterised the dependence of the cosmic variance σ_{v} on the basic parameters involved in close pair studies (Sect. 4.1): the number density of the principal (n_{1}, Sect. 4.4.1) and the companion sample (n_{2}, Sect. 4.4.4), the cosmic volume under study (V_{c}, Sect. 4.4.2), and the redshift (Sect. 4.4.3). We find that (27)This is the main result of the present paper. We estimated through Monte Carlo sampling that the typical uncertainty in σ_{v} from this relation is ~15%. The dependence of σ_{v} on redshift should be lower than this uncertainty. In addition, σ_{v} is independent of the search radius used to compute the merger fraction as we demonstrated in Sect. 4.3.
Cosmic variance σ_{v} as a function of the companion sample’s number density n_{2}.
4.5. Cosmic variance in spatially random samples
In this section, we further test the significance of our results by measuring both the merger fraction and the cosmic variance in samples randomly distributed in the sky plane. For this, we created a set of 100 random samples with each random sample comprising 48 random subsamples (one per ALHAMBRA subfield). We generated each random subsample by assigning a random RA and Dec to each source in the original catalogue but retained the original redshift of the sources. This erases the clustering signal inside each ALHAMBRA subfield (i.e., at ≲15′ scales), but the number density fluctuations between subfields because of the clustering at scales larger than ~15′ remains. We estimated the merger fraction and the cosmic variance for each random sample at 0.3 ≤ z< 0.9 as in Sect. 4.4.1, computed the median merger fraction, ⟨ f_{m} ⟩, and determined the median cosmic variance, ⟨ σ_{v} ⟩, in the set of 100 random samples to compare them with the values measured in the real samples. To facilitate this comparison, we defined the variables F_{m} = f_{m}/ ⟨ f_{m} ⟩ and Σ_{v} = σ_{v}/ ⟨ σ_{v} ⟩. We estimated F_{m} and Σ_{v} for different selections in n_{1} following Sect. 4.4.1, and we have show our findings in Fig. 12.
On the one hand, the merger fraction in the real samples is higher than in the random samples by a factor of three–four, F_{m} = 4.25 − 0.27 × n_{1} (Fig. 12, top panel). This reflects the clustering present in the real samples that we erased when we randomised the positions of the sources in the sky, as well as the higher clustering of more luminous galaxies. This result is consistent with previous close pair studies that compare real and random samples (e.g., Kartaltepe et al. 2007). On the other hand, the cosmic variance measured in the random samples is higher than the cosmic variance in the real ones, ⟨ Σ_{v} ⟩ = 0.81 ± 0.04 (Fig. 12, bottom panel). This implies that most of the variance between subfields is unrelated with the clustering inside these subfields and that the σ_{v} measured in the present paper is a real signature of the relative fieldtofield variation of the merger fraction.
Fig. 12 Top panel: Merger fraction in real samples over the average merger fraction in random samples, F_{m}, as a function of the number density n_{1}. The dotted line marks identity. The dashed line marks the best leastsquares linear fit to the data, F_{m} = 4.25 − 0.27n_{1}. Bottom panel: Cosmic variance in real samples over the average cosmic variance in random samples, Σ_{v}, as a function of the number density n_{1}. The dotted line marks identity. The dashed line is the errorweighted average of the data, Σ_{v} = 0.81 ± 0.04, and the coloured area its 68% confidence interval. A colour version of this plot is available in the electronic edition. 

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Fig. 13 Normalised cosmic variance as a function of n_{1} at 0.3 ≤ z< 0.9 for the first (circles) and the second (triangles) group of seven independent pointings in the ALHAMBRA survey (see text for details). The dashed line marks the errorweighted average of , , and the coloured area shows its 68% confidence interval. A colour version of this plot is available in the electronic edition. 

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Fig. 14 Normalised cosmic variance as a function of n_{1} for samples selected in Bband luminosity. The inverted triangles are those samples without a luminosity ratio imposed, and the triangles are those with a luminosity ratio applied (Table 8). Points at the same number density are offset when needed to avoid overlap. The dashed line marks the errorweighted average of , . The coloured area shows its 68% confidence interval. The grey area marks the 15% uncertainty expected from our parametrisation of the cosmic variance. A colour version of this plot is available in the electronic edition. 

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Fig. 15 Normalised cosmic variance as a function of n_{1} for samples selected in stellar mass. The dots are those samples without a mass ratio imposed, and the squares are those with a mass ratio applied (Table 9). Points at the same number density are offset when needed to avoid overlap. The dashed line marks the errorweighted average of , . The coloured area shows its 68% confidence interval. The grey area marks the 15% uncertainty expected from our parametrisation of the cosmic variance. A colour version of this plot is available in the electronic edition. 

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4.6. Testing the independence of the 48 ALHAMBRA subfields
Hitherto, we have assumed that the 48 ALHAMBRA subfields are independent. However, only the seven ALHAMBRA fields are really independent and correlations between adjacent subfields should exists. This correlations could impact our σ_{v} measurements, and in this section, we test the independence assumption.
We defined two groups of seven independent pointings with one per ALHAMBRA field. The first group comprises of the pointings f02p01, f03p02, f04p01, f05p01, f06p01, f07p03, and f08p02, where f0? refers to the ALHAMBRA field and p0? to the pointing in the field. The second group comprises the pointings f02p02, f03p01, f04p01, f05p01, f06p02, f07p04, and f08p01. Note that fields f04 and f05 have only one pointing in the current ALHAMBRA release. Each of the previous pointings probe a cosmic volume four times higher than our fiducial subfields with a median V_{c} = (54.49 ± 0.59) × 10^{4} Mpc^{3} for the first group and V_{c} = (55.24 ± 0.50) × 10^{4} Mpc^{3} for the second one at 0.3 ≤ z< 0.9. Then, we measured the merger fraction in the seven independent pointings of each group, and we obtained σ_{v} applying the MLE. We repeated this procedure for different selections from i ≤ 23 to i ≤ 20.5 in 0.5 magnitude steps. Finally, we defined , so the values of would be dispersed around unity if the cosmic variance measured from the seven independent areas is described well by the cosmic variance measured from the 48 subfields. We summarise our results in Table 7 and in Fig. 13.
We find that the cosmic variance from the seven independent fields nicely agree with our expectations from Eq. (27) with an errorweighted average of . Thus, assuming the 48 ALHAMBRA subfields as independent is an acceptable approximation to study σ_{v}. In addition, the uncertainties in σ_{v} are lower by a factor of two when we use the 48 subfields, improving the statistical significance of our results.
Cosmic variance σ_{v} measured from seven independent pointings in the ALHAMBRA survey.
Cosmic variance σ_{v} of luminosityselected samples.
4.7. Expectations for luminosity and massselected samples
Throughout this paper, we have focused our analysis in (apparent) bright galaxies with i ≤ 23. This ensures excellent photometric redshifts and provides reliable merger fraction measurements (Sect. 4.3). However, one should be interested on the merger fraction of galaxies selected by their luminosity, stellar mass, colour, etc. Because the bias of the galaxies with respect to the underlying darkmatter distribution depends on the selection of the sample, our prescription to estimate σ_{v} could not be valid for physically selected samples (Sect. 4.1). In this section, we compare the expected cosmic variance from Eq. (27) with the actual cosmic variance of several luminosity and stellar massselected samples to set the limits and the reliability of our suggested parametrisation.
Cosmic variance σ_{v} of stellar massselected samples.
We defined the variable , so the values of would be dispersed around unity if no extra dependence on the luminosity or the stellar mass exists. Throughout the present paper, we imposed neither luminosity nor mass ratio constraints between the galaxies in the close pairs. However, merger fraction studies impose such constraints to study major or minor mergers. This ratio is defined as ℛ = M_{⋆,2}/M_{⋆,1}, where M_{⋆,1} and M_{⋆,2} are the stellar masses of the principal and the companion galaxy in the pair, respectively. The definition of ℛ in the Bband luminosity L_{B} case is similar. Major mergers are usually defined with 1 / 4 ≤ ℛ ≤ 1, while minor mergers with ℛ ≤ 1 / 4. We explored different ℛ cases and estimated n_{2} as the number density of the L_{B} ≥ ℛL_{B,1} or the M_{⋆} ≥ ℛM_{⋆,1} population. The properties of all the studied samples are summarised in Tables 8 and 9. The redshift range probed in each case was chosen to ensure volumelimited companion samples. We stress that the samples in Tables 8 and 9 mimic typical observational selections and ℛ values from the literature.
On the one hand, we find that the errorweighted average of all the luminosityselected samples is , which is compatible with unity as we expected, if no (or limited) dependence on the selection exists (Fig. 14). We obtained from samples with the luminosity ratio ℛ applied, while from samples without it. On the other hand, we find for the stellar massselected samples (Fig. 15). As noted previously, the value is compatible with unity. We obtained from samples with the mass ratio ℛ applied, while from samples without it.
We conclude that our results based on iband selected samples provide a good description of the cosmic variance for physically selected samples with a limited dependence (≲ 15%) on both the luminosity and the stellar mass selection. Thus, only n_{1}, n_{2}, and V_{c} are needed to estimate a reliable σ_{v} for merger fractions studies based on close pairs.
5. Summary and conclusions
We use the 48 subfields of ~180 arcmin^{2} in the ALHAMBRA survey (total effective area of 2.38 deg^{2}) to empirically estimate the cosmic variance that affects merger fraction studies based on close pairs for the first time in the literature. We find that the distribution of the merger fraction is lognormal, and we use a maximum likelihood estimator to measure the cosmic variance σ_{v} unaffected by observational errors (including the Poisson shot noise term).
We find that the better parametrisation of the cosmic variance for merger fraction studies based on close pairs is (Eq. (27)), where n_{1} and n_{2} are the cosmic average number density of the principal and the companion populations under study, respectively, and V_{c} is the cosmological volume probed by our survey in the redshift range of interest. We stress that n_{1} and n_{2} should be estimated from general luminosity or mass function studies and that measurements from volumes dominated by structures (e.g., clusters or voids) should be avoided. In addition, σ_{v} is independent of the search radius used to compute the merger fraction. The typical uncertainty in σ_{v} from our relation is ~15%. The dependence of the cosmic variance on redshift should be lower than this uncertainty. Finally, we checked that our formula provides a good estimation of σ_{v} for luminosity and massselected samples and for close pairs with a given luminosity or mass ratio ℛ between the galaxies in the pair. In the latter case, n_{2} is the average number density of those galaxies brighter or more massive than ℛL_{1} or ℛM_{⋆,1}, respectively.
Equation (27) provides the expected cosmic variance of an individual merger fraction measurement f_{m} at a given field and redshift range. The 68% confidence interval of this merger fraction is [ f_{m}e^{− σv},f_{m}e^{σv} ]. This interval is independent of the error in the measurement of f_{m}, so both sources of uncertainty should be added to obtain an accurate description of the merger fraction error in pencilbeam surveys. If we have access to several independent fields j for our study, we should combine the cosmic variance of each single field with the following formula (see Moster et al. 2011, for details): (28)where is the cosmic volume probed by each single field in the redshift range of interest.
Thanks to the Eqs. (27) and (28), we can estimate the impact of cosmic variance in close pair studies from the literature. For example, Bundy et al. (2009) measure the major merger fraction in the two GOODS fields. We expect σ_{v} ~ 0.42 for massive (M_{⋆} ≥ 10^{11}M_{⊙}) galaxies, while σ_{v} ~ 0.16 for M_{⋆} ≥ 10^{10}M_{⊙} galaxies. The studies of de Ravel et al. (2009) and LópezSanjuan et al. (2011) explore the merger fraction in the VVDSDeep. We expect σ_{v} ≲ 0.09 for major mergers and σ_{v} ≲ 0.07 for minor mergers in this survey. Lin et al. (2008) explore the merger properties of M_{B} ≤ − 19 galaxies in three DEEP2 fields. We estimate σ_{v} ~ 0.03 for their results. Several major close pair studies have been conducted in the COSMOS field (e.g., de Ravel et al. 2011; Xu et al. 2012). Focusing in massselected samples, we expect σ_{v} ~ 0.17 for massive galaxies, while σ_{v} ~ 0.07 for M_{⋆} ≥ 10^{10}M_{⊙} galaxies. In addition, we estimate σ_{v} ~ 0.13 for the minor merger fractions reported by LópezSanjuan et al. (2012) in the COSMOS field. Regarding local merger fractions (z ≲ 0.1), the expected cosmic variance in the study of De Propris et al. (2005) in the MGC is σ_{v} ~ 0.03, while it is σ_{v}< 0.03 in the study of Patton et al. (2000). Finally, studies based in the full SDSS area are barely affected by cosmic variance with σ_{v} ≲ 0.005 (e.g., Patton & Atfield 2008).
Extended samples over larger sky areas are needed to constrain the subtle redshift evolution of the comic variance and its dependence on the selection of the samples. Future large photometric surveys such as JPAS^{3} (Javalambre – Physics of the accelerating universe Astrophysical Survey, Benítez et al. 2014), which will provide excellent photometric redshifts with δ_{z} ~ 0.003 over 8500 deg^{2} in the northern sky, are fundamental to progress on this topic.
In the present paper, we have studied the intrinsic dispersion of the merger fraction measured in the 48 ALHAMBRA subfields in detail. In future papers, we will explore the dependence of the median merger fraction, as estimated as e^{μ}, on stellar mass, colour, or morphology (see Pović et al. 2013, for details about the morphological classification in ALHAMBRA), and we will compare the ALHAMBRA measurements (both the median and the dispersion) to the expectations from cosmological simulations.
Acknowledgments
We dedicate this paper to the memory of our six IAC colleagues and friends who met with a fatal accident in Piedra de los Cochinos, Tenerife, in February 2007, with a special thanks to Maurizio Panniello, whose teachings of python were so important for this paper. We thank the comments and suggestions of the anonymous referee, that improved the clarity of the manuscript. This work has mainly been funding by the FITE (Fondo de Inversiones de Teruel) and the projects AYA200614056 and CSD200700060. We also acknowledge the financial support from the Spanish grants AYA201015169, AYA201022111C0301 and AYA201022111C0302, from the Junta de Andalucia through TIC114 and the Excellence Project P08TIC03531, and from the Generalitat Valenciana through the project Prometeo/2009/064. A.J.C. (RyC201108529) and C.H. (RyC201108262) are Ramón y Cajal fellows of the Spanish government.
References
 Abazajian, K. N., AdelmanMcCarthy, J. K., Agüeros, M. A., et al. 2009, ApJS, 182, 543 [NASA ADS] [CrossRef] [Google Scholar]
 AparicioVillegas, T., Alfaro, E. J., CabreraCaño, J., et al. 2010, AJ, 139, 1242 [NASA ADS] [CrossRef] [Google Scholar]
 ArnalteMur, P., Martínez, V. J., Norberg, P., et al. 2013, MNRAS, in press [arXiv:1311.3280] [Google Scholar]
 Arzner, K., Güdel, M., Briggs, K., et al. 2007, A&A, 468, 501 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Bell, E. F., Phleps, S., Somerville, R. S., et al. 2006, ApJ, 652, 270 [NASA ADS] [CrossRef] [Google Scholar]
 Benítez, N. 2000, ApJ, 536, 571 [NASA ADS] [CrossRef] [Google Scholar]
 Benítez, N., Moles, M., Aguerri, J. A. L., et al. 2009, ApJ, 692, L5 [NASA ADS] [CrossRef] [Google Scholar]
 Benítez, N., Dupke, R., Moles, M., et al. 2014 [arXiv:1403.5237] [Google Scholar]
 Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503 [NASA ADS] [CrossRef] [Google Scholar]
 Bundy, K., Fukugita, M., Ellis, R. S., et al. 2009, ApJ, 697, 1369 [NASA ADS] [CrossRef] [Google Scholar]
 Cameron, E. 2011, PASA, 28, 128 [NASA ADS] [CrossRef] [Google Scholar]
 Carrasco Kind, M., & Brunner, R. J. 2013, MNRAS, 432, 1483 [NASA ADS] [CrossRef] [Google Scholar]
 Chabrier, G. 2003, PASP, 115, 763 [NASA ADS] [CrossRef] [Google Scholar]
 Coles, P., & Jones, B. 1991, MNRAS, 248, 1 [NASA ADS] [CrossRef] [Google Scholar]
 Conselice, C. J., Bluck, A. F. L., Buitrago, F., et al. 2011, MNRAS, 413, 80 [NASA ADS] [CrossRef] [Google Scholar]
 Coupon, J., Kilbinger, M., McCracken, H. J., et al. 2012, A&A, 542, A5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 CristóbalHornillos, D., Aguerri, J. A. L., Moles, M., et al. 2009, ApJ, 696, 1554 [NASA ADS] [CrossRef] [Google Scholar]
 Davis, M., Guhathakurta, P., Konidaris, N. P., et al. 2007, ApJ, 660, L1 [NASA ADS] [CrossRef] [Google Scholar]
 de la Torre, S., Guzzo, L., Kovač, K., et al. 2010, MNRAS, 409, 867 [NASA ADS] [CrossRef] [Google Scholar]
 De Propris, R., Liske, J., Driver, S. P., Allen, P. D., & Cross, N. J. G. 2005, AJ, 130, 1516 [NASA ADS] [CrossRef] [Google Scholar]
 de Ravel, L., Le Fèvre, O., Tresse, L., et al. 2009, A&A, 498, 379 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 de Ravel, L., Kampczyk, P., Le Fèvre, O., et al. 2011, A&A, submitted [arXiv:1104.5470] [Google Scholar]
 Driver, S. P., & Robotham, A. S. G. 2010, MNRAS, 407, 2131 [NASA ADS] [CrossRef] [Google Scholar]
 Efron, B. 1982, The Jackknife, the Bootstrap and Other Resampling Plans, Society for Industrial & Applied Mathematics [Google Scholar]
 Furusawa, H., Kosugi, G., Akiyama, M., et al. 2008, ApJS, 176, 1 [NASA ADS] [CrossRef] [Google Scholar]
 Giavalisco, M., Ferguson, H. C., Koekemoer, A. M., et al. 2004, ApJ, 600, L93 [NASA ADS] [CrossRef] [Google Scholar]
 Grogin, N. A., Kocevski, D. D., Faber, S. M., et al. 2011, ApJS, 197, 35 [NASA ADS] [CrossRef] [Google Scholar]
 Hopkins, P. F., Croton, D., Bundy, K., et al. 2010, ApJ, 724, 915 [NASA ADS] [CrossRef] [Google Scholar]
 Ilbert, O., Lauger, S., Tresse, L., et al. 2006, A&A, 453, 809 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Ilbert, O., Capak, P., Salvato, M., et al. 2009, ApJ, 690, 1236 [NASA ADS] [CrossRef] [Google Scholar]
 Jian, H.Y., Lin, L., & Chiueh, T. 2012, ApJ, 754, 26 [NASA ADS] [CrossRef] [Google Scholar]
 Kampczyk, P., Lilly, S. J., de Ravel, L., et al. 2013, ApJ, 762, 43 [NASA ADS] [CrossRef] [Google Scholar]
 Kartaltepe, J. S., Sanders, D. B., Scoville, N. Z., et al. 2007, ApJS, 172, 320 [NASA ADS] [CrossRef] [Google Scholar]
 Kitzbichler, M. G., & White, S. D. M. 2008, MNRAS, 1300 [Google Scholar]
 Koekemoer, A. M., Faber, S. M., Ferguson, H. C., et al. 2011, ApJS, 197, 36 [NASA ADS] [CrossRef] [Google Scholar]
 Kovač, K., Lilly, S. J., Cucciati, O., et al. 2010, ApJ, 708, 505 [NASA ADS] [CrossRef] [Google Scholar]
 Le Fèvre, O., Vettolani, G., Garilli, B., et al. 2005, A&A, 439, 845 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Le Fèvre, O., Cassata, P., Cucciati, O., et al. 2013, A&A, 559, A14 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Lilly, S. J., Le Brun, V., Maier, C., et al. 2009, ApJS, 184, 218 [NASA ADS] [CrossRef] [Google Scholar]
 Lin, L., Koo, D. C., Willmer, C. N. A., et al. 2004, ApJ, 617, L9 [NASA ADS] [CrossRef] [Google Scholar]
 Lin, L., Patton, D. R., Koo, D. C., et al. 2008, ApJ, 681, 232 [NASA ADS] [CrossRef] [Google Scholar]
 Lin, L., Cooper, M. C., Jian, H., et al. 2010, ApJ, 718, 1158 [NASA ADS] [CrossRef] [Google Scholar]
 Liske, J., Lemon, D. J., Driver, S. P., Cross, N. J. G., & Couch, W. J. 2003, MNRAS, 344, 307 [NASA ADS] [CrossRef] [Google Scholar]
 LópezSanjuan, C., GarcíaDabó, C. E., & Balcells, M. 2008, PASP, 120, 571 [NASA ADS] [CrossRef] [Google Scholar]
 LópezSanjuan, C., Balcells, M., GarcíaDabó, C. E., et al. 2009a, ApJ, 694, 643 [NASA ADS] [CrossRef] [Google Scholar]
 LópezSanjuan, C., Balcells, M., PérezGonzález, P. G., et al. 2009b, A&A, 501, 505 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 LópezSanjuan, C., Balcells, M., PérezGonzález, P. G., et al. 2010a, A&A, 518, A20 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 LópezSanjuan, C., Balcells, M., PérezGonzález, P. G., et al. 2010b, ApJ, 710, 1170 [NASA ADS] [CrossRef] [Google Scholar]
 LópezSanjuan, C., Le Fèvre, O., de Ravel, L., et al. 2011, A&A, 530, A20 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 LópezSanjuan, C., Le Fèvre, O., Ilbert, O., et al. 2012, A&A, 548, A7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 LópezSanjuan, C., Le Fèvre, O., Tasca, L. A. M., et al. 2013, A&A, 553, A78 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Lotz, J. M., Jonsson, P., Cox, T. J., & Primack, J. R. 2010a, MNRAS, 404, 590 [NASA ADS] [CrossRef] [Google Scholar]
 Lotz, J. M., Jonsson, P., Cox, T. J., & Primack, J. R. 2010b, MNRAS, 404, 575 [NASA ADS] [CrossRef] [Google Scholar]
 Makarov, D., Makarova, L., Rizzi, L., et al. 2006, AJ, 132, 2729 [NASA ADS] [CrossRef] [Google Scholar]
 Marulli, F., Bolzonella, M., Branchini, E., et al. 2013, A&A, 557, A17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
 Moles, M., Benítez, N., Aguerri, J. A. L., et al. 2008, AJ, 136, 1325 [NASA ADS] [CrossRef] [Google Scholar]
 Molino, A., Benítez, N., Moles, M., et al. 2013, MNRAS, in press [arXiv:1306.4968] [Google Scholar]
 Moreno, J., Bluck, A. F. L., Ellison, S. L., et al. 2013, MNRAS [Google Scholar]
 Moster, B. P., Somerville, R. S., Newman, J. A., & Rix, H.W. 2011, ApJ, 731, 113 [NASA ADS] [CrossRef] [Google Scholar]
 Naylor, T., & Jeffries, R. D. 2006, MNRAS, 373, 1251 [NASA ADS] [CrossRef] [Google Scholar]
 Newman, J. A., Cooper, M. C., Davis, M., et al. 2013, ApJS, 208, 5 [NASA ADS] [CrossRef] [Google Scholar]
 Nuza, S. E., Sánchez, A. G., Prada, F., et al. 2013, MNRAS, 432, 743 [NASA ADS] [CrossRef] [Google Scholar]
 Patton, D. R., & Atfield, J. E. 2008, ApJ, 685, 235 [NASA ADS] [CrossRef] [Google Scholar]
 Patton, D. R., Carlberg, R. G., Marzke, R. O., et al. 2000, ApJ, 536, 153 [NASA ADS] [CrossRef] [Google Scholar]
 Peebles, P. J. E. 1980, The largescale structure of the universe (Princeton University Press) [Google Scholar]
 Pović, M., HuertasCompany, M., Aguerri, J. A. L., et al. 2013, MNRAS, 435, 3444 [NASA ADS] [CrossRef] [Google Scholar]
 RowanRobinson, M., Lari, C., PerezFournon, I., et al. 2004, MNRAS, 351, 1290 [NASA ADS] [CrossRef] [Google Scholar]
 Scoville, N., Aussel, H., Brusa, M., et al. 2007, ApJS, 172, 1 [NASA ADS] [CrossRef] [Google Scholar]
 Somerville, R. S., Lee, K., Ferguson, H. C., et al. 2004, ApJ, 600, L171 [NASA ADS] [CrossRef] [Google Scholar]
 Stringer, M. J., Benson, A. J., Bundy, K., Ellis, R. S., & Quetin, E. L. 2009, MNRAS, 393, 1127 [NASA ADS] [CrossRef] [Google Scholar]
 Taylor, E. N., Hopkins, A. M., Baldry, I. K., et al. 2011, MNRAS, 418, 1587 [NASA ADS] [CrossRef] [Google Scholar]
 Trenti, M., & Stiavelli, M. 2008, ApJ, 676, 767 [NASA ADS] [CrossRef] [Google Scholar]
 Xu, C. K., Zhao, Y., Scoville, N., et al. 2012, ApJ, 747, 85 [NASA ADS] [CrossRef] [Google Scholar]
 Zehavi, I., Zheng, Z., Weinberg, D. H., et al. 2011, ApJ, 736, 59 [NASA ADS] [CrossRef] [Google Scholar]
Online material
Appendix A: Maximum likelihood estimation of the cosmic variance σ_{v}
Maximum likelihood estimators (MLEs) have been used in a wide range of topics in astrophysics. For example, Naylor & Jeffries (2006) used a MLE to fit colourmagnitude diagrams, Arzner et al. (2007) to improve the determination of faint Xray spectra, Makarov et al. (2006) to improve distance estimates using red giant branch stars, and LópezSanjuan et al. (2008, 2009a,b, 2010b) to estimate reliable merger fractions from morphological criteria. The MLEs are based on the estimation of the most probable values of a set of parameters, which define the probability distribution that describes an observational sample.
The general MLE operates as follows. Throughout this Appendix, we denote the probability to obtain the values a given the parameters b as P (a  b). Being x_{j} the measured values in the ALHAMBRA field j, and θ the parameters that we want to estimate, we may express the joined likelihood function as (A.1)If we are able to express P (x_{j}  θ) analytically, we can minimise Eq. (A.1) to obtain the best estimation of the parameters θ, as denoted as θ_{ML}. In our case, x_{j} is the observed value of the merger fraction in logspace for the ALHAMBRA subfield j, where . We decided to work in logspace because that makes the problem analytic and simplifies the implementation of the method without losing mathematical rigour.
The ALHAMBRA subfields are assumed to have a real merger fraction (not affected by observational errors) that define a Gaussian distribution in logspace, (A.2)Observational errors cause the observed to differ from their respective real values . The observed are assumed to be extracted for a Gaussian distribution with mean and standard deviation σ_{o,j} (the observational errors), (A.3)We assumed that the observational errors are Gaussian in logspace, or, that they are lognormal in observational space. This is a good approximation of the reality because we are dealing with fractions that cannot be negative and that have asymmetric confidence intervals, as shown by Cameron (2011). In our case, we estimated the observational errors in logspace as σ_{o} = σ_{f}/f_{m}. We checked that the values of σ_{o} derived from our jackknife errors are similar to those estimated from the Bayesian approach in Cameron (2011) with a difference between them ≲15%.
We obtained the probability P (x_{j}  θ) of each ALHAMBRA subfield by the total probability theorem: (A.4)where and (μ,σ_{v},σ_{o,j}) = θ in Eq. (A.1). Note that the values of σ_{o,j} are the measured uncertainties for each ALHAMBRA subfield, so the only unknowns are the variables μ and σ_{v}, which we want to estimate. Note also that we integrate over the variable , so we are not be able to estimate the real merger fractions individually, but only the underlying Gaussian distribution that describes the sample.
The final joined likelihood function, Eq. (A.1) after integrating Eq. (A.4), is (A.5)With the minimisation of this function, we obtain the best estimation of both μ and the cosmic variance σ_{v}, which are unaffected by observational errors.
In addition, we can analytically estimate the errors in the parameters above. We can obtain those via an expansion of the function in a Taylor’s series of its variables θ = (μ,σ_{v},σ_{o,j}) around the minimisation point θ_{ML}. The previous minimisation process made the first L derivative null, and we obtain (A.6)where H_{xy} is the Hessian matrix, and T denotes the transpose matrix. The inverse of the Hessian matrix provides an estimate of the 68% confidence intervals of μ_{ML} and σ_{ML}, as well as the covariance between them. The Hessian matrix of the joined likelihood function L is defined as (A.7)with and (A.10)Then, we computed the inverse of the minus Hessian, h_{xy} = ( − H_{xy})^{1}. Finally, we estimated the variances of our inferred parameters as and because maximum likelihood theory states that .
Fig. A.1 Recovered cosmic variance over input cosmic variance (top panel) and median σ_{σv} over the dispersion of the recovered cosmic variance (bottom panel) as a function of Δσ. In both panels, triangles, circles, and squares are the results from synthetic catalogues with n = 50,250, and 1000, respectively. White symbols show the results from the BLS fit to the data (σ_{v,BLS}), while those coloured show the ones from the MLE (σ_{v,ML}). The n = 50 and 1000 points are shifted to avoid overlap. The dashed lines mark identity, and the solid line in the top panel shows the expectation from a convolution of two Gaussians in logspace, . 

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We tested the performance and the limitations of our MLE through synthetic catalogues of merger fractions. We created several sets of 1000 synthetic catalogues with each of them composed by a number n of merger fractions randomly drawn from a lognormal distribution with μ_{in} = log 0.05 and σ_{v,in} = 0.2 and affected by observational errors σ_{o}. We explored the n = 50,250 and 1000 cases for the number of merger fractions and varied the observational errors from σ_{o} = 0.1 to 0.5 in 0.1 steps. That is, we explored observational errors in the measurement of the merger fraction from Δσ ≡ σ_{o}/σ_{v} = 0.5 to 2.5 times the cosmic variance that we want to measure. We checked that the results below are similar for any value of σ_{v,in}. We find that

1.
The median value of the recovered μ, as noted , in each set of synthetic catalogues is similar to μ_{in}, with deviations lower than 0.5% in all cases under study. However, we find that for n = 50 catalogues overestimates σ_{v,in} more than 5% at Δσ ≳ 2.0, while we recover σ_{v,in} well even with Δσ = 2.5 (Fig. A.1, top panel) for n = 1000. This means that larger data sets are needed to recover the underlying distribution as the observational errors increase.

2.
We also study the values recovered by a best leastsquares (BLS) fit of Eq. (18) to the synthetic catalogues. We find that (i) the BLS fit recovers the right values of μ_{in}. This was expected, since the applied observational errors preserve the median of the initial distribution. (ii) The BLS fit overestimates σ_{v,in} in all cases. The recovered values depart from the initial one as expected from a convolution of two Gaussians with a variance σ_{v,in} and σ_{o}, where . The MLE performs a deconvolution of the observational errors, recovering accurately the initial cosmic variance (Fig. A.1, top panel).

3.
The estimated variances of μ and σ_{v} are reliable. That is, the median variances and estimated by the MLE are similar to the dispersion of the recovered values, as noted s_{μ} and s_{σv}, in each set of synthetic catalogues. The difference between both variances for μ is lower than 5% in all the probed cases. However, we find that for n = 50 catalogues overestimates s_{σv}, which is more than 5% at Δσ ≳ 1.5: this is the limit of the MLE to estimate reliable uncertainties with this number of data (Fig. A.1, bottom panel). Because the estimated variance tends asymptotically to s_{σv} for a large number of data, for n = 1000 catalogues deviates less from the expected value than for n = 50 synthetic catalogues. Note that the value of σ_{v} is still unbiased as such large observational errors (Fig. A.1, top panel), when the estimated variance σ_{σv} deviates from the expectations at large Δσ, and we can roughly estimate σ_{σv} through realistic synthetic catalogues as those in this Appendix.

4.
The variances of the recovered parameters decreases with n and increases with σ_{o}. That reflects the loss of information due to the observational errors. Remark that the MLE takes these observational errors into account to estimate the parameters and their variance.
We conclude that the MLE developed in this Appendix is not biased, providing accurate variances, and we can recover reliable uncertainties of the cosmic variance σ_{v} in ALHAMBRA (n = 48) for Δσ ≲ 1.5. Note that reliable values of σ_{v} in ALHAMBRA are recovered at Δσ ≲ 2.0. We checked that the average Δσ in our study is 0.60 (the average observational error is ), and the maximum value is Δσ = 0.85. Thus, the results in the present paper are robust against the effect of observational errors.
All Tables
Cosmic variance σ_{v} as a function of the search radius for galaxies at 0.3 ≤ z< 0.9.
Cosmic variance σ_{v} as a function of the principal sample’s number density n_{1}.
Cosmic variance σ_{v} as a function of the companion sample’s number density n_{2}.
Cosmic variance σ_{v} measured from seven independent pointings in the ALHAMBRA survey.
All Figures
Fig. 1 Schematic view of the ALHAMBRA field’s geometry in the sky plane. We show the eight subfields (one per LAICA chip) of the field ALHAMBRA6. The black and red squares mark the two LAICA pointings in this particular field. The geometry of the other seven fields is similar. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 2 Photometric redshift (z_{p}) versus spectroscopic redshift (z_{s}) for the 3813 galaxies in the ALHAMBRA area with i ≤ 22.5 and a measured z_{s}. The solid line marks identity. The sources above and below the dashed lines are catastrophic outliers. The accuracy of the photometric redshifts (δ_{z}) and the fraction of catastrophic outliers (η) are labelled in the panel. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 3 Distribution of the variable Δ_{z} for the 3813 galaxies in the ALHAMBRA area with i ≤ 22.5 and a measured spectroscopic redshift. The red line is the best leastsquares fit of a Gaussian function to the data. The median, dispersion, and the factor C derived from the fit are labelled in the panel. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 4 Distribution of the merger fraction f_{m} for i ≤ 22 (top panel) and i ≤ 21 (bottom panel) galaxies in the 48 ALHAMBRA subfields, as measured from close pairs with 10 h^{1} kpc ≤ r_{p} ≤ 30 h^{1} kpc at 0.3 ≤ z< 0.9. In each panel, the red solid line is the best leastsquares fit of a lognormal function to the data. The star and the red bar mark the median and the 68% confidence interval of the fit, respectively. The black bar marks the confidence interval from the maximum likelihood analysis of the data and is our measurement of the cosmic variance σ_{v}. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 5 Merger fraction f_{m} as a function of the odds selection for i ≤ 22.5 galaxies at 0.3 ≤ z< 0.9. The filled triangles, circles, and squares are for , and 50 h^{1} kpc close pairs, respectively. The open triangles are the observed merger fractions for kpc to illustrate the selection correction from Eq. (21). In several cases, the error bars are smaller than the points. The dotted, dashed, and solid lines mark the average f_{m} at for , and 50 h^{1} kpc close pairs, respectively. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 6 Cosmic variance σ_{v} as a function of for i ≤ 22.5,21.5, and 21 galaxies at 0.3 ≤ z< 0.9 (circles, stars, and triangles, respectively). The horizontal lines mark the errorweighted average of the cosmic variance in each case, and the coloured areas mark their 68% confidence intervals. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 7 Cosmic variance σ_{v} as a function of the odds selection for i ≤ 22.5 galaxies at 0.3 ≤ z< 0.9. Triangles, circles, and squares are for , and 50 h^{1} kpc close pairs, respectively. The dotted, dashed, and solid lines mark the average σ_{v} at for , and 50 h^{1} kpc close pairs, respectively. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 8 Cosmic variance σ_{v} as a function of the number density n_{1} of the principal population under study. Increasing the number density, the principal sample comprises i ≤ 20, 20.5, 21, 21.5, 22, 22.5, and 23 galaxies, respectively. The probed cosmic volume is the same in all the cases, V_{c} ~ 1.4 × 10^{5} Mpc^{3} (0.3 ≤ z< 0.9). The dashed line is the errorweighted leastsquares fit of a powerlaw to the data, . A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 9 Normalised cosmic variance as a function of the probed cosmic volume V_{c} for galaxies with i ≤ 23. The circle corresponds to same data as in Fig. 8. The stars probe different redshift intervals, while triangles probe sky areas smaller than the fiducial ALHAMBRA subfield. The dashed line is the errorweighted leastsquares fit of a powerlaw to the data, . A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 10 Normalised cosmic variance as a function of redshift for galaxies with i ≤ 23 (circles). The dashed line marks the errorweighted average of , , and the coloured area shows its 68% confidence interval. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 11 Normalised cosmic variance as a function of the relative number density of the companion and the principal samples under study, n_{2}/n_{1}. Increasing the relative density, the companion sample comprises i ≤ 20.5, 21, 21.5, 22, 22.5, and 23 galaxies, respectively. The red dashed line is the errorweighted leastsquares fit of a powerlaw to the data, . A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 12 Top panel: Merger fraction in real samples over the average merger fraction in random samples, F_{m}, as a function of the number density n_{1}. The dotted line marks identity. The dashed line marks the best leastsquares linear fit to the data, F_{m} = 4.25 − 0.27n_{1}. Bottom panel: Cosmic variance in real samples over the average cosmic variance in random samples, Σ_{v}, as a function of the number density n_{1}. The dotted line marks identity. The dashed line is the errorweighted average of the data, Σ_{v} = 0.81 ± 0.04, and the coloured area its 68% confidence interval. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 13 Normalised cosmic variance as a function of n_{1} at 0.3 ≤ z< 0.9 for the first (circles) and the second (triangles) group of seven independent pointings in the ALHAMBRA survey (see text for details). The dashed line marks the errorweighted average of , , and the coloured area shows its 68% confidence interval. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 14 Normalised cosmic variance as a function of n_{1} for samples selected in Bband luminosity. The inverted triangles are those samples without a luminosity ratio imposed, and the triangles are those with a luminosity ratio applied (Table 8). Points at the same number density are offset when needed to avoid overlap. The dashed line marks the errorweighted average of , . The coloured area shows its 68% confidence interval. The grey area marks the 15% uncertainty expected from our parametrisation of the cosmic variance. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. 15 Normalised cosmic variance as a function of n_{1} for samples selected in stellar mass. The dots are those samples without a mass ratio imposed, and the squares are those with a mass ratio applied (Table 9). Points at the same number density are offset when needed to avoid overlap. The dashed line marks the errorweighted average of , . The coloured area shows its 68% confidence interval. The grey area marks the 15% uncertainty expected from our parametrisation of the cosmic variance. A colour version of this plot is available in the electronic edition. 

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In the text 
Fig. A.1 Recovered cosmic variance over input cosmic variance (top panel) and median σ_{σv} over the dispersion of the recovered cosmic variance (bottom panel) as a function of Δσ. In both panels, triangles, circles, and squares are the results from synthetic catalogues with n = 50,250, and 1000, respectively. White symbols show the results from the BLS fit to the data (σ_{v,BLS}), while those coloured show the ones from the MLE (σ_{v,ML}). The n = 50 and 1000 points are shifted to avoid overlap. The dashed lines mark identity, and the solid line in the top panel shows the expectation from a convolution of two Gaussians in logspace, . 

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In the text 