### 4. ANALYSIS OF OBSERVED PECULIAR VELOCITIES

Given radial peculiar velocities ui sparsely sampled at positions xi over a large volume, with random errors i, the first goal is to extract the underlying three-dimensional velocity field, v(x). Under GI, this velocity field is subject to certain constraints, e.g. being associated with a mass-density fluctuation field, (x) - the other target for recovery. A field can be defined by a parametric model or by the field values at grid points. The number of independent parameters or grid points should be much smaller than the number of data points in view of the noisy data.

### 4.1Toy Models

Given a model velocity field v(k, x), the free parameters k can be determined globally by minimizing a weighted sum of residuals, e.g.

-log L i Wi [ui - xihat · v(k, xi)]2. (11)

If the errors are Gaussian and Wi i2 then L is the likelihood, and it is a useful approximation for log-normal errors as well. The model could first be a few-parameter ``toy'' model with simple geometry. Already the simplest bulk-flow model, v(x) = B corresponding to = 0, is of interest because the data clearly show a bulk flow component of several hundred km s-1 in our neighborhood (Section 7.1). Another simple model is of spherical symmetry, expected to be a reasonable fit in voids and in regions dominated by one high density peak (Bardeen et al. 1986). The velocity profile as a function of distance r from the infall center is not particularly constrained by GI, and a specific profile which proved successful is

v (r) = -vlg (r / rlg)[(rlg2 + rc2) / (r2 + rc2)](n+1)/2. (12)

The center is specified by its angular position and its distance rlg from the LG, and the profile is characterized by its value vlg at the LG, a core radius rc, and a power index n. For r << rc the velocity rises r and for r >> rc it falls off r-n. The associated density profile is given in the linear approximation by the divergence = -f-1 r-1 ðr [r v (r)]. The 7S ellipticals were modeled by a spherical infall model termed ``The Great Attractor'' (GA), with rlg = 42 h-1Mpc toward (l, b) = (309°, +18°), vlg = 535km s-1, rc = 14.3 h-1Mpc, and n = 1.7 (Faber & Burstein 1988). A similar model vaguely fits the local infall of spirals into Virgo (Aaronson et al. 1982b). The merged Mark II data is well fitted by a multi-parameter hybrid consisting of a GA infall, a Virgo-centric infall, and a ``local anomaly'' - a bulk flow shared by the ~ 10 h-1Mpc-local neighborhood of 360km s-1 perpendicular to the supergalactic plane.

The toy models provide an intuitive picturing of the large-scale motions with clues about the associated mass sources, and they can be used as simple statistics for the comparison with theory (e.g. the bulk flow, Section 7.1; and the GA model, Bertschinger & Juszkiewicz 1988). However, toy modeling imposes an oversimplified geometry associated with assumed ``sources'' on a complex velocity field which actually arises from a continuous field of asymmetric density fluctuations (Section 4.5, Section 5). Moreover, the bulk velocity statistic computed globally suffers from a bias due to the large-scale sampling gradients. The monopole, involving the radial decrease of sampling density and rise of errors, has the effect of reducing the effective volume and thus reducing the apparent conflict between the high bulk flow and the theoretical expectations (Kaiser 1988). The sampling dipole, arising for example from oversampling in the GA direction, tends to enhance the component of the bulk velocity in that direction because it is dominated by the velocity in a smaller effective volume (Regos & Szalay 1989; Szalay 1988). The sampling quadrupole arising from the Galactic zone of avoidance (ZOA) introduces larger shot-noise into the component parallel to the Galactic plane, which could also result in an artificially high bulk velocity. These biases can be partially cured by equal-volume weighting (Section 4.2).

### 4.2Potential Analysis

If the LSS evolved according to GI, then the large-scale velocity field is expected to be irrotational, i.e. x v = 0. Any vorticity mode would have decayed during the linear regime as the universe expanded, and, based on Kelvin's circulation theorem, the flow remains vorticity-free in the quasi-linear regime as long as it is laminar. Irrotationality implies that the velocity field can be derived from a scalar potential, v(x) = -(x), so the radial velocity field u(x) should contain enough information for a full reconstruction. In the POTENT procedure (Bertschinger & Dekel 1989) the potential is computed by integration along radial rays from the observer, (x) = -0r u(r',,)dr', and the two missing transverse velocity components are then recovered by differentiation. Then (x) is approximated by c (Equation 6).

The non-trivial step is the smoothing of the data into u(x). The aim in POTENT (Dekel et al. 1990) is to reproduce the u(x) that would have been obtained had the true v(x) been sampled densely and uniformly and smoothed with a spherical Gaussian window of radius Rs. With the data as available u(xc) is taken to be the value at x = xc of an appropriate local velocity model v(k, x - xc) obtained by minimizing the sum (11) in terms of the parameters k within an appropriate local window Wi = W(xi, xc) chosen as follows.

TENSOR WINDOW. Unless Rs << r, the uis cannot be averaged as scalars because the directions xihat differ from xchat, so u(xc) requires a fit of a local 3D model. The original POTENT used the simplest local model, v(x) = B, for which the solution can be expressed explicitly in terms of a tensor window function.

WINDOW BIAS. The tensorial correction to the spherical window has conical symmetry, weighting more heavily objects of large xihat · xchat. The resultant bias of a true infall transverse to the line of sight is a flow towards the LG, e.g. ~ 300km s-1 at the GA in the current reconstruction. A way to reduce this bias is by generalizing B into a linear velocity model, v(x) = B + L · (x-xc), with L a symmetric tensor, which ensures local irrotationality. The linear terms tend to ``absorb'' most of the bias, leaving v(xc) = B less biased. Unfortunately, a high-order model tends to pick undesired small-scale noise. The optimal compromise is found to be a first-order model fit out to r = 40 h-1Mpc, smoothly changing to a zeroth-order fit beyond 60 h-1Mpc (Dekel et al. 1994).

SAMPLING-GRADIENT BIAS (SG). If the true velocity field is varying within the effective window, the non-uniform sampling introduces a bias because the smoothing is galaxy-weighted whereas the aim is equal-volume weighting. One should weight each object by the local volume it ``occupies'', e.g. Vi Rn3 where Rn is the distance to the nth neighboring object (e.g. n = 4). This procedure is found via simulations to reduce the SG bias in Mark III to negligible levels typically out to 60 h-1Mpc but away from the ZOA. The Rn(x) field can serve later as a flag for poorly sampled regions, to be excluded from any quantitative analysis.

REDUCING RANDOM ERRORS. The ideal weighting for reducing the effect of Gaussian noise has weights Wi i-2 but this spoils the carefully-designed volume weighting, biasing u towards its values at smaller ri and at nearby clusters where the errors are small. A successful compromise is to weight by both, i.e.,

W(xi, xc) Vi i-2 exp[-(xi-xc)2 / 2Rs2]. (13)

Note that POTENT could alternatively vary Rs to keep the random errors at a constant level, but at the expense of producing fields not directly comparable to theoretical models of uniform smoothing. Another way to reduce noise is to eliminate badly-observed galaxies with large residuals |ui - u(xi)|, where u(xi) is obtained in a pre-POTENT smoothing stage. The whole smoothing procedure has been developed with the help of carefully-designed mock catalogs ``observed'' from simulations.

ESTIMATING THE RANDOM ERRORS. The errors in the recovered fields are assessed by Monte-Carlo simulations, where the input distances are perturbed at random using a Gaussian of standard deviation i before being fed into POTENT. The error in at a grid point is estimated by the standard deviation of the recovered over the Monte-Carlo simulations, (and similarly v). In the well-sampled regions, which extend in Mark III out to 40-60 h-1Mpc, the errors are 0.1-0.3, but they may blow up in certain regions at large distances. To exclude noisy regions, any quantitative analysis should be limited to points where v and are within certain limits.

Several variants of POTENT are worth mentioning. The potential integration is naturally done along radial paths using only u(x) because the data are radial velocities, but recall that the smoothing procedure determines a 3D velocity using the finite effective opening angle of the window ~ Rs / r. The transverse components are normally determined with larger uncertainty but the non-uniform sampling may actually cause the minimum-error path to be non-radial, especially in regions where Rs / r ~ 1. For example, it might be better to reach the far side of a void along its populated periphery rather than through its empty center. The optimal path can be determined by a max-flow algorithm (Simmons et al. 1994). It turns out in practice that only little can be gained by allowing non-radial paths because large empty regions usually occur at large distances where the transverse components are very noisy. Still, it is possible that the derived potential can be somewhat improved by averaging over many paths.

The use of the opening angle to determine the transverse velocities can be carried one step further by fitting the data to a power-series generalizing the linear model,

vi(x) = Bi + Lij tildexj + Qijk tildexj tildexk + Cijkl tildexj tildexk tildexl + . . ., (14)

where tildex = x - xc. If the matrices are all symmetric then the velocity model is automatically irrotational, and can therefore be used as the final result without appealing to the potential, and with the density being automatically approximated by c(x) = ||I - Lij|| - 1 (Equation 6). The fit must be local because the model tends to blow up at large distances. The expansion can be truncated at any order, limited by the tendency of the high-order terms to pick up small-scale noise. The smoothing here is not a separate preceding step, the SG bias is reduced, and there is no need for numerical integration or differentiation, but the effective smoothing is again not straightforwardly related to theoretical models of uniform smoothing (Blumenthal & Dekel, in preparation).

Another method of potential interest without a preliminary smoothing step is based on wavelet analysis, which enables a natural isolation of the structure on different scales (Rauzy et al. 1993). The effective smoothing involves no loss of information and no specific scale or shape for the wavelet, and the analysis is global and done in one step. How successful this method will be in dealing with noisy data and its comparison with theory and other data still remains to be seen.

### 4.3Regularized Multi-Parameter Models - Wiener Filter

A natural generalization of the toy models of Section 4.1 is a global expansion of the fields in a discrete set of basis functions, such as a Fourier series of the sort

= k [ak sin(k · x) + bk cos(k · x)],

v = k k / k2 [ak cos(k · x) - bk sin(k · x)], (15)

with a very large number (2m) of free Fourier coefficients, and where and v are related via the linear approximation (4). The maximization of the likelihood involves a 2m x 2m matrix inversion and, even if 2m is appropriately smaller than the number of data, the solution will most likely follow Murphy's law and blow up at large distances, yielding large spurious fluctuations where the data are sparse, noisy and weakly constraining, so the global fit requires some kind of regularization. Kaiser & Stebbins (1991) proposed to maximize the probability of the parameters subject to the data and an assumed prior model for the probability distribution of the Fourier coefficients; they assumed a Gaussian distribution with a power spectrum < ak2 > = < bk2 > = P0 kn.

This is in fact an application of the Wiener filter method (e.g. Rybicki & Press 1992) for finding the optimal estimator of a field (x) which is a linear functional of another field u(x), given noisy data ui of the latter and an assumed prior model:

opt(x) = < (x) ui > < ui uj >-1 uj, (16)

where the indices run over the data (Hoffman 1994; Stebbins 1994). If ui = u(xi) + i with i independent random errors of zero mean, then the cross-correlation terms are < (x) u(xi) >, and the auto-correlation matrix is < ui uj > = < u(xi) u(xj) > + i2 ij, both given by the prior model. opt is thus determined by the model where the errors dominate, and by the data where the errors are small. If the assumed prior is Gaussian, the optimal estimator is also the most probable field given the data, which is a generalization of the conditional mean given one constraint (e.g. Dekel 1981). This conditional mean field can be the basis for a general algorithm to produce constrained random realization (Hoffman and Ribak 1991). The same technique can also be applied to the inverse problem of recovering the velocity from observed density. Note (Lahav et al. 1994) that for Gaussian fields the above procedure is closely related to maximum-entropy reconstruction of noisy pictures (Gull & Daniell 1978).

The maximum-probability method has so far been applied in a preliminary way to heterogeneous data in a box of side 200 h-1Mpc (to eliminate periodic boundary effects) with 18 h-1Mpc resolution. No unique density field came out as different priors (-3 n 1) led to different fits with similar 2. This means that the data used were not of sufficient quality to determine the fields this way and it would be interesting to see how this method does when applied to better data. The method still lacks a complete error analysis, it needs to somehow deal with non-linear effects, and it needs to correct for IM bias (perhaps as in Section 3.2).

A general undesired feature of maximum probability solutions is that they tend to be oversmoothed in regions of poor data, relaxing to = 0 in the extreme case of no data. This is unfortunate because the signal of true density is modulated by the density and quality of sampling - a sampling bias which replaces the SG bias. The effectively varying smoothing length can affect any dynamical use of the reconstructed field (e.g. deriving a gravitational acceleration from a density field), as well as prevent a straightforward comparison with other data or with uniformly-smoothed theoretical fields. Yahil (1994) has recently proposed a modified filtering method which partly cures this problem by forcing the recovered field to have a constant variance.

### 4.4Malmquist-Free Analysis

The selection bias (Section 3.2) can be practically eliminated from the calibration of an inverse TF relation, (M), as long as the internal velocity parameter does not explicitly enter the selection process. An inverse analysis requires assuming a parametric model for the velocity field, v(_k,x) (Schechter 1980). Instead of (11), the sum is then

i Wi [ i (observed) - i (model)]2, (17)

with the model given by the inverse TF relation,

i (model) = ã + tilde b Mi = ã + tilde b (mi - 5 log ri),

ri = zi - xihat · v(k , xi). (18)

The parameters are the inverse TF parameters ã and tilde b and the k characterizing the velocity model.

An inverse method was first used by Aaronson et al. (1982b) to fit a Virgo-centric toy model to a local sample of spirals, and attempts to implement the inverse method to an extended sample with a general velocity model are in progress (MFPOT by Yahil et al. 1994). This is a non-trivial problem of non-linear multi-parameter minimization with several possible routes. If the velocity model is expressed in z-space, then ri is given explicitly by zi but the results suffer from oversmoothing in collapsing regions. If in r-space, then ri is implicit in the second equation of (18) requiring iterative minimization, e.g. by carrying ri from one iteration to the next one. The velocity model could be either global or local, with the former enabling a simultaneous minimization of the TF and velocity parameters and the latter requiring a sequential minimization of global TF parameters and local velocity parameters. While the results are supposed to be free of Malmquist bias, they suffer from other biases which have to be carefully diagnosed and corrected for. The inverse method is also being generalized to account for small-scale velocity noise (Willick, Burstein & Tornen 1994). The Malmquist-free results will have to be consistent with the M-corrected forward results before one can put to rest the crucial issue of M bias and its effect on our LSS results.

### 4.5Fields of Velocity and Mass Density

 Figure 3. The fluctuation fields of velocity and mass-density in the Supergalactic plane as recovered by POTENT from the Mark III velocities of ~ 3000 galaxies with 12 h-1Mpc smoothing. The vectors shown are projections of the 3D velocity field in the CMB frame. Distances and velocities are in 1000km s-1. Contour spacing is 0.2 in , with the heavy contour marking = 0 and dashed contours < 0. The LG is at the center. The GA is on the left, PP on the right, and Coma is at the top. The grey-scale in the contour map and the height of the surface in the landscape map are proportional to (Dekel et al. 1994).

Figure 3 shows supergalactic-plane maps of the velocity field in the CMB frame and the associated c field (for = 1) as recovered by POTENT from the preliminary Mark III data. The data are reliable out to ~ 60h-1Mpc in most directions outside the Galactic plane (Y = 0), and out to ~ 70 h-1Mpc in the direction of the GA (left-top) and Perseus-Pisces (PP, right-bottom). Both large-scale (~ 100 h-1Mpc) and small-scale (~ 10 h-1Mpc ) features are important; e.g. the bulk velocity reflects properties of the initial fluctuations and of the DM (Section 7.1), while the small-scale variations indicate the value of (Section 8).

The velocity map shows a clear tendency for motion from right to left, in the general direction of the LG-CMB motion (L, B = 139°, -31° in supergalactic coordinates). The bulk velocity within 60 h-1Mpc is 300-350 km s-1 towards (L, B 166°, -20°) (Section 7.1) but the flow is not coherent over the whole volume sampled, e.g. there are regions in front of PP and at the back of the GA where the XY velocity components vanish, i.e., the streaming relative to the LG is opposite to the bulk flow direction. The velocity field shows local convergences and divergences which indicate strong density variations on scales about twice as large as the smoothing scale.

The GA at 12 h-1Mpc smoothing and = 1 is a broad density peak of maximum height = 1.4 ± 0.3 located near the Galactic plane Y = 0 at X -40 h-1Mpc. The GA extends towards Virgo near Y 10 (the ``Local Supercluster''), towards Pavo-Indus-Telescopium (PIT) across the Galactic plane to the south (Y < 0), and towards the Shapley concentration behind the GA. The structure at the top roughly coincides with the ``Great Wall'' of Coma, with 0.5. The PP peak which dominates the right-bottom is centered near Perseus with = 1.0 ± 0.4. PP extends towards Aquarius in the southern hemisphere, and connects to Cetus near the south Galactic pole, where the ``Southern Wall'' is seen in redshift surveys. Underdense regions separate the GA and PP, extending from bottom-left to top-right. The deepest region in the Supergalactic plane, with = -0.7 ± 0.2, roughly coincides with the galaxy-void of Sculptor (Kauffman et al. 1991).

One can still find in the literature statements questioning the very existence of the GA (e.g. Rowan-Robinson 1993), which simply reflect ambiguous definitions for this phenomenon. A GA clearly exists in the sense that the dominant feature in the local inferred velocity field is a coherent convergence, centered near X -40. It is another question whether the associated density peak has a counterpart in the galaxy distribution or is a separate, unseen entity. The GA is ambiguous only in the sense that the good correlation observed between the mass density inferred from the velocities and the galaxy density in redshift surveys is perhaps not perfect (Section 6.2).

Other cosmographic issues of debate are whether there exists a back-flow behind the GA in the CMB frame, and whether PP and the LG are approaching each other. These effects are detected by the current POTENT analysis only at the 1.5 level in terms of the random uncertainty. Furthermore, the freedom in the zero-point of the distance indicators permits adding a Hubble-like peculiar velocity which can balance the GA back flow and make PP move away from the LG. Thus, these issues remain debatable.

To what extent should one believe the recovery in the ZOA which is empty of tracers? The velocities observed on the two sides of the ZOA are used as probes of the mass in the ZOA. The interpolation is based on the assumed irrotationality, where the recovered transverse components enable a reconstruction of the mass-density. However, while the SG bias can be corrected where the width of the ZOA is smaller than the smoothing length, the result could be severely biased where the unsampled region is larger. With 12 h-1Mpc smoothing in Mark III the interpolation is suspected of being severely biased in ~ 50% of the ZOA at r = 40 h-1Mpc (where R4 > Rs), but the interpolation is pretty safe in the highly populated GA region, for example. Indeed, a deep survey of optical galaxies at small Galactic latitudes recently discovered that the ACO cluster A3627, centered at (l, b, z) = (325°, -7°, 43 h-1Mpc), is an extremely rich cluster (Kraan-Korteweg & Woudt 1994), very near the central peak of the GA as predicted by POTENT, ~ (320°, 0°, 40 h-1Mpc) (Kolatt et al. 1994).