- freely available
Galaxies 2014, 2(2), 263-274; doi:10.3390/galaxies2020263
Abstract: The Sloan Digital Sky Survey (SDSS) provides data on several hundred thousand galaxies. The precise location of these galaxies in the sky, along with information about their luminosities and line-of-sight (Doppler) velocities, allows one to construct a three-dimensional map of their location and estimate their line-of-sight velocity dispersion. This information, in principle, allows one to test dynamical gravity models, specifically models of satellite galaxy velocity dispersions near massive hosts. A key difficulty is the separation of true satellites from interlopers. We sidestep this problem by not attempting to derive satellite galaxy velocity dispersions from the data, but instead incorporate an interloper background into the mathematical models and compare the result to the actual data. We find that due to the presence of interlopers, it is not possible to exclude several gravitational theories on the basis of the SDSS data.
Recently, Klypin and Prada  presented an analysis of galaxy observations of the Sloan Digital Sky Survey (SDSS ) to test gravity and dark matter in the peripheral parts of galaxies at distances 50–400 kpc from the centers of galaxies. This field of extragalactic astronomy provides one of the main arguments for the presence of dark matter [3,4].
The analysis of Klypin and Prada  begins with identifying candidate host galaxies and candidate satellite galaxies based on their relative positions in the sky, relative velocities, and relative luminosities. After a candidate population of hosts and satellites has been identified, an ad-hoc mathematical model is used to separate the (presumed constant) background of interlopers from actual satellites. This mathematical model yields a velocity dispersion profile for the presumed satellites that is then checked against theory.
In the present work we propose an alternative approach that altogether avoids the difficult issue of identifying satellites versus interlopers. Rather than attempting to subtract the interloper population from the data in order to construct a dataset that is then hoped to represent the satellite population correctly, we endeavor to model the actual data instead, by adding an interloper population to the velocity dispersions predicted by various gravity theories. Crudely put, we extend the theory to model the data correctly, rather than massaging the data to fit within the constraints of a limited model.
In the first section of our paper, we offer a detailed description of our data analysis. In the second part, we model the data using three gravity theories. In addition to Newtonian gravity without exotic dark matter and Modified Newtonian Dynamics (MOND, ), of particular interest to us is our Modified Gravity Theory (MOG, [6,7]), which has been used successfully in the past to explain galaxy rotation curves , galaxy cluster mass profiles , cosmological observations , and gravitational lensing in the Bullet Cluster  without assuming the presence of nonbaryonic dark matter. In the third section, we combine our satellite velocity dispersion predictions with the observed interloper background, and contrast the resulting predictions as well as the cold dark matter (CDM) prediction of Klypin and Prada  with the SDSS data. Our conclusion is that the SDSS galaxy data cannot be used to exclude any of these gravitational theories, not unless an independent, nonstatistical method is found that can be used reliably to identify individual interlopers.
2. Data Analysis
The SDSS  Data Release 6 (DR6) provides imaging data over 9500 deg in five photometric bands. Galaxy spectra are determined by charge-coupled device (CCD) imaging and the SDSS 2.5 m telescope on Apache Point, New Mexico . Over half a million galaxies brighter than Petrosian r-magnitude 17.77 over 7400 deg are included in the SDSS data with a redshift accuracy better than 30 km/s.
Due to the complications of calculating modified gravity for non-spherical objects, Klypin and Prada  restricted their analysis only to red galaxies, the vast majority of which are either elliptical galaxies or are dominated by bulges. We followed a similar strategy, restricting our selection of candidate host galaxies to isolated red galaxies. We also restricted our selection to galaxies with a recession velocity between 3000 km/s and 25,000 km/s, which yielded approximately 234,000 galaxies in total.
We began our analysis by obtaining a dataset from the SDSS. We obtained sky positions, spectra, and extinction-corrected magnitudes for 687,423 galaxies. We adjusted the dataset by accounting for the motion of the solar system. We then processed the result using a C-language program that selected, as candidate hosts, isolated red galaxies with no other galaxy within a projected distance of 1 Mpc and a luminosity more than 25% that of the candidate host. We then identified as candidate satellites galaxies that were within 1 Mpc of projected distance from the candidate host, and had a line-of-sight redshift velocity of less than 1500 km/s relative to the candidate host. These candidate satellites were binned by distance. The computation yielded 3589 hosts with 8156 satellites. Of these, 121 satellites (or about 1.5% of the total) were assigned to multiple hosts; no attempt was made to eliminate these duplicates.
The radial number density of the candidate satellites (Figure 1c) suggests that many of these galaxies are not, in fact, satellites. Indeed, if dim galaxies were distributed completely randomly, with no relation to the candidate host, we would expect a number density that increases linearly with projected radius. The actual number density plot appears to be a distribution with a peak at ∼100 kpc, superimposed on just such a linear density profile. Subtracting the linear density profile yields the plot in Figure 1d, which is a power law profile with exponent , as shown in Figure 1b. (This corresponds to a parameter of in the Jeans equation, discussed below).
3. Satellite Galaxy Velocity Dispersion
Predictions for modified gravity can be made by solving the Jeans equation, which gives the radial velocity dispersion as a function of radial distance. Klypin and Prada  find that neither the Newtonian gravity without nonbaryonic dark matter nor the Modified Newtonian Dynamics (MOND) is compatible with observations. Angus et al. , however, demonstrated that a suitably chosen anisotropic model and appropriately chosen galaxy masses can be used to achieve a good fit for MOND.
Radial velocity dispersions in a spherically symmetric gravitational field can be computed using the Jeans equation :
If we assume that the velocity distribution of satellite galaxies is isotropic, . In general, β needs to be neither zero nor constant. The number density of candidate satellites favors a value of , corresponding to the observed power law radial density with exponent .
The observed velocity dispersion is along the observer’s line-of-sight, seen as a function of the projected distance from the host galaxy. Therefore, it is necessary to integrate velocities along the line-of-sight:
In recent work , we have been able to develop formulae that predict the values of the α and μ parameters from the source mass, in the form:
The MOND acceleration is given by the solution of the non-linear equation:
Following in the footsteps of Klypin and Prada , we grouped satellite galaxy velocities for host galaxies into two luminosity ranges: , and . The corresponding masses for the host galaxies, calculated by Klypin and Prada  on the basis of the work of Bell and de Jong , are:
We used these values to obtain two sets of predictions for each theory, using , .
4. The Interloper Background
Having obtained the velocity dispersion for satellite galaxies around a host galaxy, we now turn our attention to the interloper population.
The actual data consist of host galaxies, satellites, and an effectively random interloper background. When satellites and interlopers are binned by projected distance from host galaxies, the result can be modeled symbolically as:
This is not the approach taken by Klypin and Prada , however. Instead, they elected to subtract a modeled interloper background from the observed number density of satellites, and then compare that to a model representing only satellite galaxies. In effect, they used:
For this reason, in our analysis we endeavor to model the actual observation, by estimating both satellite galaxy velocity dispersions in accordance with the previous section and the velocity dispersion of the interloper background. We assume a constant (i.e., independent of distance or sky position) interloper background.
In terms of the polar coordinate R in the sky plane and the line-of-sight velocity v, we find that the likelihood of finding a satellite between R and , with line-of-sight velocity between v and , will be proportional to:
Using this likelihood function, we find that ΛCDM is the best performing model, marginally outperforming MOND and MOG, with maximum likelihood obtained at and , respectively, for the two candidate satellite populations. The ΛCDM and MOND models are effectively indistinguishable (see Figure 2). They both outperform MOG, but the difference is not statistically significant: comparison with a t-statistic yields a probability of 24.2% (for ) and 23.0% (for ) that the difference between MOG and ΛCDM is due to chance. Only the Newtonian prediction without nonbaryonic dark matter can be excluded with a significance.
For this reason, it seems futile to use this type of statistical analysis of satellite galaxies to distinguish between CDM models on the one hand, and various gravitational theories on the other, due to the presence of the interloper population.
Observational data presented by Adelman-McCarthy et al.  and studied by Klypin and Prada  are viewed as evidence of the success of the ΛCDM model. The usual approach relies on the critical step of interloper removal, before the data is compared against predictions. We argue that this approach is fundamentally flawed: rather than attempting to remove interlopers from the data, we must add the interloper background theoretical predictions, in order to predict observational values. When we carry out this approach, we find that the ΛCDM and modified gravity theory predictions cannot be distinguished and that although the SDSS dataset weakly favors ΛCDM over the alternatives, it cannot be used to falsify any of the theories we examined.
John W. Moffat thanks the John Templeton Foundation for generous financial support. The research was partially supported by National Research Council of Canada. Research at the Perimeter Institute for Theoretical Physics is supported by the Government of Canada through the Natural Sciences and Engineering Research Council (NSERC) and by the Province of Ontario through the Ministry of Research and Innovation (MRI).
Both authors contributed to the preparation of the manuscript text and the development of the underlying theoretical framework. The programming required to obtain and process SDSS galaxy data was performed by Viktor T. Toth.
In this Appendix, we provide additional details about the dataset being used and our calculations.
Our sample of galaxies was obtained from the SDSS (DR6) using the publicly available Structured Query Language (SQL) interface  . We ran the following query to list all galaxies for which spectra were obtained:
SELECT G.ra,G.dec,u,g,r,i,dered_z, petroMag_r, psfMag_r,extinction_r,petror50_r,S.z,S.zErr, S.zConf,G.flags,S.zStatus FROM Galaxy as G, SpecObj as S WHERE G.ObjID = S.BestObjID
The columns of this query are:
right ascension (α),
u-band extinction-corrected model magnitude (mu),
g-band extinction-corrected model magnitude (mg),
r-band extinction-corrected model magnitude (mr),
i-band extinction-corrected model magnitude (mi),
z-band extinction-corrected model magnitude (mz),
Petrosian r-band magnitude mP (needed for LRG selection),
PSF r-band magnitude mPSF (needed for LRG selection),
r-band extinction er,
Petrosian r-band 50% light radius r50,
redshift error (Δz),
redshift correlation coefficient,
photometric flags (as defined by the SDSS),
redshift status (as defined by the SDSS).
The query yielded 687,423 rows. We verified the dataset by specifically searching for known galaxies in the setand comparing their positions, redshifts, and magnitudes with the data records. We then processed this dataset using a C-language program that selected luminous red galaxies (LRGs) using the appropriate SDSS photometric parameters. For an object to be considered an LRG, the following conditions had to be satisfied:
The recession velocity and Hubble distance were calculated from the redshift:
Model magnitudes m were converted into absolute magnitudes M using the formula:
This formula assumes that r is measured in Mpc. It also corrects for redshift.
The C-language program then iterated through the filtered galaxy set to find candidate hosts and candidate satellites. A candidate host was an LRG with no other object within a projected distance of 1 Mpc and a brightness at least 25% that of the candidate host. For all candidate hosts, galaxies within 1 Mpc projected distance and with a line-of-sight velocity less than 1500 km/s relative to the candidate host were considered as candidate satellites. The computation yielded 3589 hosts with 8156 satellites. Of these, 121 satellites (or about 1.5% of the total) were assigned to multiple hosts; no attempt was made to eliminate these duplicates.
The line-of-sight velocity dispersion given by Equation (6) was numerically integrated using Maple. The result was fitted using a sixth order polynomial in the range :
Finally, the likelihood function given by Equation (21) was calculated for different values of κ and interpolated, as shown in Figure 2. The and deviations from the ΛCDM maximum likelihood, shown in this figure, were calculated relative to the best ΛCDM likelihood using a t-statistic. This part of the analysis was carried out using Microsoft Excel (spreadsheets available upon request.)
The dataset downloaded from the SDSS and processed using the C-language code is available upon request, as well as the C-language program itself, Maple, and Excel scripts that were used in this analysis.
Conflicts of Interest
The authors declare no conflict of interest.
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