a r X i v :a s t r o -p h /9901400v 1 28 J a n 1999
Mon.Not.R.Astron.Soc.000,000–000(0000)Printed 1February 2008
(MN L A T E X style ?le v1.4)
Statistical characteristics of formation and evolution
of structure in the universe.
M.Demia′n ski 1,2& A.G.Doroshkevich 3,4
1Institute of Theoretical Physics,University of Warsaw,00-681Warsaw,Poland 2Department of Astronomy,Williams College,Williamstown,MA 01267,USA
3Theoretical Astrophysics Center,Juliane Maries Vej 30,DK-2100Copenhagen ?,Denmark
4Keldysh Institute of Applied Mathematics,Russian Academy of Sciences,125047Moscow,Russia
Accepted ...,Received 1998October ...;in original form 1998October 13
ABSTRACT
An approximate statistical description of the formation and evolution of structure of the universe based on the Zel’dovich theory of gravitational instability is proposed.It is found that the evolution of DM structure shows features of self-similarity and the main structure characteristics can be expressed through the parameters of initial power spectrum and cosmological model.For the CDM-like power spectrum and suitable parameters of the cosmological model the e?ective matter compression reaches the observed scales R wall ～20–25h ?1Mpc with the typical mean separation of wall-like elements D SLSS ～50–70h ?1Mpc.This description can be directly applied to the deep pencil beam galactic surveys and absorption spectra of quasars.For larger 3D catalogs and simulations it can be applied to results obtained with the core-sampling analysis.
It is shown that the interaction of large and small scale perturbations modulates the creation rate of early Zel’dovich pancakes and generates bias on the SLSS scale.For suitable parameters of the cosmological model and reheating process this bias can essentially improve the characteristics of simulated structure of the universe.
The models with 0.3≤?m ≤0.5give the best description of the observed struc-ture parameters.The in?uence of low mass ”warm”dark matter particles,such as a massive neutrino,will extend the acceptable range of ?m and h .
Key words:cosmology:large-scale structure of the Universe —galaxies:clusters:general –theory.
1INTRODUCTION
Over the past decade the large maps of the spatial galaxy distribution have been prepared and the unexpectedly com-plicated character of this distribution was established.The structure predicted by the Zel’dovich theory of gravitational instability (Zel’dovich 1970,1978)was found already in the ?rst wedge diagrams (Gregory &Thompson 1978)and now the Large Scale Structure (LSS)is seen in many obser-vational catalogs,such as the CfA (de Lapparent,Geller &Huchra 1987;Ramella,Geller &Huchra 1992),the SRSS (da Costa et al.1988)and in the Las Campanas Redshift Sur-vey (Shectman et al.1996,hereafter LCRS).The observed high concentration of galaxies within the wall-like structure elements such as the Great Attractor (Dressler et al.1987),and the Great Wall (de Lapparent,Geller &Huchra 1988)and the existence of extended under dense regions similar to the Great Void (Kirshner et al.1983)put in the forefront the investigation of the Super Large Scale Structure (SLSS).
Now the SLSS is also found in many deep pencil beam red-shift surveys (Broadhurst et al.1990;Willmer et al.1994;
Buryak et al.1994;Bellanger &de Lapparent 1995;Cohen et al.1996)as a rich galaxy clumps with the typical sepa-rations in the range of (60–120)h ?1Mpc.Here h=H 0/100km/s/Mpc is the dimensionless Hubble constant.
Further progress in the statistical description of the LSS &SLSS has been reached with the core-sampling method (Buryak et al.1994)and the Minimal Spanning Tree tech-nique (Barrow,Bhavsar &Sonoda 1985).Recent analy-sis of the LCRS performed by Doroshkevich et al.(1996&1997b,hereafter LCRS1&LCRS2,Doroshkevich et al.1998)revealed some statistical parameters of the wall-like SLSS component such as their typical separation,D SLSS ≈50?60h ?1Mpc,and the fraction of galaxies accumulated by the SLSS,which can reach ～50%.The same analysis indi-cates that formation of richer walls can be roughly described as an asymmetric 2D collapse of regions with a typical size R wall ～20–25h ?1Mpc that is about half of their typical
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2Demia′n ski&Doroshkevich
separation.The analysis of Durham/UKST redshift survey con?rms these results(Doroshkevich et al.,1999).Earlier similar scales,in the range of(50–100)h?1Mpc,were found only for spatial distribution of clusters of galaxies(see,e.g., Bahcall1988;Einasto et al.1994)and for a few superclusters of galaxies(see,e.g.,Oort1983a,b).
Evolution of structure was discussed and simulated many times(see,e.g.,Sahni et al.1994;Doroshkevich et al.1997a,hereafter DFGMM;for references,Sahni&Coles 1995).However,SLSS in the dark matter(DM)distribution similar to that seen in the LCRS was found only recently in a few simulations with the CDM-like power spectrum and?m h=0.2–0.3,(Cole,Weinberg,Frenk&Ratra1997; Doroshkevich,M¨u ller,Retzalf&Turchaninov1999,here-after DMRT).Hence,for suitable cosmological models the evolution of small initial perturbations results in the SLSS formation.
In this paper we present an approximate statistical de-scription of the process of DM structure formation based on the nonlinear Zel’dovich theory.The potential of this ap-proach is limited as the successive consideration of mutual interactions of the small and large scale perturbations be-comes more and more cumbersome.In spite of this it allows us to obtain some interesting results.Thus,it is shown that formation of both LSS and SLSS is a joint process possess-ing some features of self-similarity.The main observed characteristics of LSS and SLSS are expressed through the structure functions of power spectrum and through the typ-ical scales,set by the power spectrum,the time scale,set by the amplitude of perturbation,and the main parameters of cosmological model.One of the most interesting such charac-teristics is the dynamical scale of the nonlinearity de?ned as the scale of essential DM concentration within high density walls.We show that for the CDM transfer function(Bardeen et al.1986,hereafter BBKS)and Harrison–Zel’dovich pri-mordial power spectrum and for cosmological models with lower matter density this scale of nonlinearity reaches20–30h?1Mpc that is comparable with typical scales of the observed SLSS elements.
Simulations(DMRT)show that even in cosmological models with a low matter density the simulated velocity dispersion within the SLSS elements reaches400–700km/s along each principal axis;this exceeds the observed value by a factor of～1.5–2.Such a large and isotropic velocity dispersion is caused by the disruption of the walls into high density clouds.For smaller matter density of the universe this dispersion decreases but together with the fraction of matter accumulated by the walls.This means that other factors as,for example,the large scale bias in the spatial galaxy distribution relative to the more homogeneous dis-tribution of DM and baryons could be essential for the suc-cessful reproduction of the observed SLSS.Such large scale bias caused by the interaction of small and large scale per-turbations was discussed by Dekel&Silk(1986),and Dekel &Rees(1987),and estimated by Demia′n ski&Doroshke-vich(1999,hereafter Paper I).
The interaction of small and large scale perturbations is important during all evolutionary stages.Thus,even dur-ing early evolutionary periods the large scale perturbations modulate the rate of pancake formation.This modulation is seen as an acceleration of the pancake formation within deeper potential wells which later are transformed into the wall-like SLSS elements(Buryak et al.1992;Demia′n ski& Doroshkevich1997;Paper I).Suppression of pancake forma-tion near the peaks of gravitational potential noted by Sahni et al.(1994)is another manifestation of such interactions. During all evolutionary stages these interactions result in the successive merging of individual pancakes.The acceler-ation of pancake disruption,caused by compression of mat-ter within walls,can also be attributed to this interaction. Now it is observed as a high velocity dispersion in simulated SLSS and as di?erences between the expected and measured mass functions.It was found to be essential even for pan-cakes formed at high redshifts(Miralda-Escude et al.1996). The possible correlation of galaxy morphology with large scale perturbations was discussed by Evrard et al.(1990). All these manifestations of small and large scale interaction are important for the correct comparison and interpretation of simulated and observed matter distribution.
Now the modulation of spatial distribution of pancakes formed at high redshifts z≥4can be seen as the large scale bias in the galaxy and DM spatial distribution.This bias can be generated by the combined action of large scale per-turbations and reheating of baryonic component of the uni-verse(see,e.g.,Dekel&Silk1986;Dekel&Rees1987).The reheating was discussed many times during the last thirty years in various aspects(see,e.g.,Sunyaev&Zel’dovich 1972;White&Rees1978;Shapiro,Giroux&Babul1994). E?ects of reheating on the process of galaxy formation were discussed as well(see,e.g.,Babul&White1991;Efstathiou 1992;Quinn,Katz&Efstathiou1996).It is also known that under reasonable assumptions about the possible en-ergy sources reheating can occur for relatively small range of redshifts z≈5?10(see,e.g.,Tegmark et al.1997;Baltz et al.1998).If essential concentration of baryons in high density clouds is reached at the same redshifts,the reheat-ing can help to generate bias(Demia′n ski&Doroshkevich 1997;Paper I).In this case further formation of high den-sity baryonic clouds will be signi?cantly depressed,due to reheating,within extended regions observed today as under dense regions between richer walls.Our estimates show that this spatial modulation of the luminous matter distribution may be essential for the interpretation of observations.
Numerical simulations are now the best way to repro-duce and to study the joint action of all the pertinent fac-tors together and to obtain more representative descrip-tion of the process of structure formation.Essential progress achieved recently both in the simulations and study of DM and’galaxy’distributions(Governato et al.1998;Jenkins et al.1998;Doroshkevich,Fong&Makarova1998;DMRT; Cole,Hatton,Weinberg&Frenk1998)allows us to follow the structure evolution in a wide range of redshifts and to re-veal di?erences between DM and galaxy https://www.doczj.com/doc/151077856.html,-parison of these results with observations and an approxi-mate theoretical description stimulates further progress in our understanding of evolution of the universe.
This paper is organized as follows:In Sect.2main nota-tions are introduced.In Sec.3and4the distribution func-tions of DM pancakes are derived and the interaction of small and large scale perturbations is described that allows us to obtain in Secs.5and6the statistical characteristics of DM structure.In Sec.7the large scale bias is discussed and in Sec.8the dynamical characteristics of walls are found.In Sec.9the theoretical estimates are compared with the avail-
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Formation and evolution of structure3 able observational and simulated data.We conclude with
Sec.10where a short discussion of the main results is pre-
sented.Some technical details are given in Appendixes I–
IV.
2STATISTICAL PARAMETERS OF
PERTURBATIONS:V ARIANCES AND
TYPICAL SCALES
The simplest characteristics of perturbations are the vari-
ances of density and velocity perturbations.For a more de-
tailed statistical description of the structure evolution it is
necessary to use also the structure functions.They were in-
troduced in Paper I and are brie?y described in this Section
and Appendix I.Here we consider only the SCDM-like power
spectrum but the same approach can be applied for other
spectra as well.
Our analysis is based on the Zel’dovich theory which
links the Eulerian,r i,and the Lagrangian,q i,coordinates
of?uid elements(particles)by the expression
r i=(1+z)?1[q i?B(z)S i(q)],(2.1)
where z denotes the redshift,B(z)describes growth of per-
turbations in the linear theory,and the potential vector
S i(q)=?φ/?q i characterizes the spatial distribution of per-
turbations.The Lagrangian coordinates of a particle,q i,are
its unperturbed comoving coordinates.
For the?at universe with?m+?Λ=1,?m≥0.1,the
function B(z)can be approximated with a precision better
then10%by the expression(Paper I)
B?3(z)≈
1??m+2.2?m(1+z)3
1+1.5?m
z(2.3)
(Zel’dovich&Novikov1983).For?m=1,?Λ=0both
expressions give B?1(z)=1+z.
The main characteristics of the perturbations are the
variances of densityσ2ρ,displacementσ2s,and components
of the deformation tensorσ2D
σ2s=1
2π2 ∞0p(k)k2dk,(2.4)
where p(k)is the power spectrum,and k is the comoving wave number.
The power spectrum determines also two amplitude in-dependent typical scales,which allow us to describe the pro-cess of structure formation and can be,possibly,estimated from the observed galaxy distribution.For the Harrison–Zel’dovich primordial power spectrum these scales,l0and l c,are de?ned as
l?20= ∞0kT2(k/k0)dk,l2c=5σ2ρ,(2.5)
where T2(x)is the transfer function and k0=?m h2Mpc?1. For the CDM transfer function(BBKS)the scale l0and the typical masses of DM and baryonic components associated with the scale l0are
l0≈6.6(?m h)?1 m?2h?1Mpc,(2.6)
M0=
π
?2m h4
,M(0)
b
=
?b
√
l0 3M0≈0.6·1010M⊙
m?2
20μK a(?m,?Λ)Mpc,(2.8)
a(?m,?Λ=1??m)=?0.215?0.05ln?m
m
,
a(?m,?Λ=0)=?0.65?0.19ln?m
m
.
where T Q is the amplitude of quadrupole component of anisotropy of the relic radiation.The time scale of the struc-ture evolution is de?ned by the function
τ(z)=τ0B(z),(2.9)τ0=
σs
3l0
≈2.73h2?m m?220μK a(?m,?Λ),τ0≈2.73h2?1.21m m?220μK ,?Λ=1??m,
τ0≈2.73h2?1.65?0.19ln?m
m m?220μK
,?Λ=0,?m≤1.
Further on,as a rule,the dimensionless variables will be used.We will use l0as the unit of length,and<ρ>l0as the unit of surface density.This means also that such dimen-sionless characteristics of a pancake as the size of collapsed slab and resulting surface density of a pancake are identical. Below we will use both terms as well as the term’mass’,m,
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4Demia′n ski&Doroshkevich
to characterize the surface density reached during formation of a pancake.The gravitational potential and displacement are measured in units ofσs l0/
√3,respectively.
3STATISTICAL CHARACTERISTICS OF PANCAKES
In this section the mass function of Zel’dovich pancakes and its time evolution is given.This can be done using the main equation of Zel’dovich theory(2.1).The mass of compressed matter is measured by the Lagrangian size of compressed slab,q,or by the surface density of pancake,<ρ>q.As it was noted above,both measures are identical in dimen-sionless notation.So,we will use both terms’size’,q,and ’mass’,m,of a pancake to characterize its surface density.
Here we do not consider the transversal characteristics of structure elements and cannot discriminate,for example, the central part of a poorer pancake and periphery of richer pancake,if they have the same surface density or mass,m. In this sense our approach gives characteristics similar to that obtained with the core-sampling analysis,pencil beam observations or the distribution of absorption lines in spectra of QSOs.
3.1The pancake formation
According to the relation(2.1)when two particles with dif-ferent Lagrangian coordinates q1and q2meet at the same Eulerian point r a pancake with the surface mass density <ρ>|q1?q2|forms.Here we assume that all particles situ-ated between these two boundary particles are also incorpo-rated into the same pancake.This assumption is also made in the adhesion approach(see,e.g.,Shandarin&Zel’dovich 1989).Formally,this condition can be written as
q12=q1?q2=τ·[S(q1)?S(q2)].(3.1) This means that the pancake formation process can be char-acterized by the scalar random function
Q(q12)=
q12
8 1+erf μ(q)2τ 3,(3.3)
μ(q)=
q
2[1?G12(q)]
.
where(see Appendix I&II)
μ(q)≈
q/2,q0?q<1,μ(q)≈q/√
ln M DM
,M q=q30M0≈3.·108M⊙
8
of matter with
Q(q12)≥0is compressed at least in one direction and for
～1
2π,σ2λ=(13/6?4.5/π)σ2D.As in the
Zel’dovich theory pancake formation is described by the re-
lation B(z)λ1=1(which follows directly from(2.1)),so for
the fraction of compressed matter withλ1≥1/B we have
f DM≈1√l0τ?32π (3.5)
and forτ?l c/l0,f DM→1(in the Zel’dovich theory
0.92≤f DM≤1).This shows that already during the early
period of nonlinear evolution,atτ≈l c/l0?1,large frac-
tion of matter f DM≥0.9is compressed into low mass pan-
cakes.But for the CDM-like power spectrum the description
of matter compression through the deformation tensor is ap-
propriate only at small scales q≤q0whereas for q?q0,
the correlations between matter?ow in orthogonal direc-
tions rapidly decrease what can be seen directly from the
expressions for the structure functions given in Appendix I.
At larger scales we have to use the more cumbersome
description discussed above and the estimate(3.3)shows
that at such scales the e?ciency of matter integration into
structure elements is only～0.875(more accurate estimates
taking into account the correlation of displacements lowers
this value to0.79).This limit is reached already at smallτ,
for q?1,that means strong matter concentration within
small structure elements.Further evolution does not change
this limit and only redistributes–due to sequential merging
–the compressed matter to more and more massive struc-
ture elements.Thus,the approximate estimates show that～
21%of matter is subjected to3D compression,～21%to3D
expansion,～29%of matter is subjected to2D compression
and can be accumulated by?laments and～29%is subjected
to1D compression and remains in pancakes.The di?erence
between estimates(3.3)and(3.5)shows that～15–20%
of matter incorporated in small clouds,with M≤M q,is
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Formation and evolution of structure5 not accumulated by larger pancakes,with M≥M q,and re-
mains distributed between those pancakes.These estimates
can be changed because the Zel’dovich approximation be-
comes invalid when strong matter compression is reached
during pancakes formation.
3.2The characteristics of pancake formation
The probability distribution function(PDF)for pancakes
formed at the momentτcan be found from(3.3)as
N cr(q,τ)=?8
dq
=
6
2πτ
dμ
√
7 ∞0W cr dq,(3.7)
N(m)
cr =
6
2πτ
q
dq
Φ μ2τ .(3.8)
The rate of formation of pancakes with mass q is
Nτ=8
dτ
(q,τ)=
6
2π
μ
√
4
√qΦ μ2τ .
When l c?l0it is described by the following(approximate)
expression:
n(>q)≈
3<μr>
q0
[
√3+√
q
Φ μ2τ ,
l c n(>q)≈
1.35
6π
μ(q)
√
32π2
?q i
,Q ij=
?2Q
τ2?
1+2q
τ2?1
,(3.12)
n0=
33
qq0 3/2Φ
μ2τ .
The function n31is similar to the standard expression for
an isotropic Gaussian?eld(BBKS).
The?rst zero of Euler characteristic describes ap-
proximately the percolation when separate higher peaks
are incorporated into a larger(in the limiting case-in-
?nite)structure element(Tomita1990;Mecke and Wag-
ner1991).The expressions(3.12)show that in the direc-
tions orthogonal to q12the percolation takes place at the
’moment’τ=μ(q)whereas along q12it occurs later,at
τ=μ(q)
6Demia′n ski&Doroshkevich
density of such peaks can be obtained from(3.12),forμ/τ> 1,as
l30n pk(>q,τ)=n0
μ2
τ1
,D sep =0.5·erfc(g2/√
√
τ;
D12
dq
e?0.5g22.(3.15)
These relations characterize the parameters of pancakes formed at the momentτunder the condition of a pancake formation at the momentτ1with the size D1and the sepa-ration D sep.
The basic relation(3.1)implies that two pancakes with sizes D1and D2and a separation|D sep|≤0.5(D1+D2) merge together and form a single pancake.For larger separa-tions merging of pancakes can also be considered in the same manner as before,but using the Euler position of formed pancake
r pan= q1q2r(q)1+z q cent?B(z)?φ12
2
erfc χ(D1,D2,D sep)2 (3.17)
The functionχ(D1,D2,D sep)is given by(III.6).We can ex-tend applicability of the formula(3.17)for small separations by requiring that
W merg(D1,D2,D sep)=1,|D sep|≤0.5(D1+D2).(3.18)
4PANCAKE EVOLUTION AND FORMATION OF FILAMENTS
Similar technique can also be used to estimate the transver-sal size,the pancakes compression and/or expansion in transversal directions,and other properties of pancakes.As we are interested in the formation of structure elements with typical sizes q?q0the local description through the de-formation tensor cannot be used and the imposed condi-tions make even approximate description of pancake evolu-tion quite cumbersome(see,e.g.,Kofman et al.1994).The general tendencies and rough characteristics of this evolu-tion can only be outlined.Thus,for example,we can esti-mate the matter fraction compressed within?lamentary-like elements and high density clumps as of about50%whereas only～29%of matter is subjected to1D compression.This estimate implies that larger pancakes could also incorporate an essential fraction of?laments and clumps.
The formation of?laments as well as pancakes disrup-tion are stimulated by the growth of density in the course of pancakes compression,and therefore,probably,during early evolutionary stages,?laments represent the most conspicu-ous elements of the structure.As was discussed in Sec.3.3,?laments merge to form a joint https://www.doczj.com/doc/151077856.html,ter,when larger pancakes are formed,the evolution of pancakes becomes slower and disruption of pancakes dominates.These expec-tations are consistent with the observed and simulated mat-ter distribution.Thus,the conspicuous?laments are seen even at z=3(see,e.g.,Governato et al.1998;Jenkins et al. 1998)whereas disrupted walls dominate at small redshifts (LCRS1,LCRS2,DMRT).
4.1The characteristics of?lament formation Some approximate characteristics of?lament distribution can be obtained by considering the formation of?laments as a sequential matter compression along two principal direc-tions.Such two step compression results in formation of high
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Formation and evolution of structure7 density”ridge”surrounded by a lower density anisotropic
halo.In a coordinate system with the?rst and second axes
oriented along the directions of maximal and intermediate
compressions this process can be approximately described
by two equations similar to(3.2):
Q(q12)=q12/τ,Q(y12)=y12/τf.(4.1)
Here vectors q12and y12and functions Q(q12)&Q(y12)
describe the deformation along the?rst and second coordi-
nate axes respectively and additional conditions introduced
in(3.2)are assumed to be ful?lled.As was discussed above
the matter compression along the second axis is accelerated
by the pancake formation and the functionτf(z)di?ers from
that given by(2.9).
Bearing in mind these restrictions we will approxi-
mately characterize the probability of?lament formation,
W f cr,for given q&y prior to the’time’τ&τf or for
givenτ&τf with sizes larger then q&y,respectively,as
the probability to have Q(q12)/q12≥Q(y12)/y12≥1/τf,
Q(q12)/q12≥1/τ:
W f cr(>q,τ;>y,τf)=1?1
√
8
erfc μ(q)2τ 1+erf μ(y)2τf 2.(4.2) The PDF for the?laments,N f cr,can be found from(4.2)as
N(f)
cr (q,τ;y,τf)=
8
dq
dμ(y)
2τ2?
μ(y)√
q/2,y?1,
μ(y)≈√
2π
√
2
(x+1/x)
(4.4)
ξ=
m f
8erfc μ(q)2τ 1+erf μ(y)2τf 2.
instead of expressions(3.2)and(3.3).The PDF for such
pancakes is
N(p)
cr
(q,τ)= πdμ(q)2τ2 ,0≤q≤∞.(4.5)
This PDF di?ers from(3.6)by the form of the functionΦ(x)
what decreases the PDF for largerμ/τ.
As before,expression(4.5)characterizes pancakes by
the surface mass density of collapsed matter,q.However the
pancake surface mass density varies with time after pancake
formation due to transversal compression or expansion that
results,in particular,in formation of?laments.Even if these
transversal motions do not lead to such dramatic results
they can change drastically the observed surface density of
pancakes.So,the current surface mass density m p=q/s p,
where s p describes the variation of pancake surface caused
by transversal motions,is a more adequate characteristic of
pancakes.
As was discussed above this period of pancake evolution
is not adequately described by the Zel’dovich theory and our
results become unreliable.To obtain qualitative characteris-
tics of in?uence of these factors we can,for example,describe
the variation of pancake’s surface as
s p∝(1?τ?Q(y12)/y12)(1?τ?Q(z12)/z12).
Here vectors y12and z12,and functions Q(y12)&Q(z12),de-
scribe the deformation along the second and third coordinate
axes respectively.The functionτ?di?ers from that given
by(2.9)and it can depend on transversal motions.Even
so rough consideration shows that the exponential term in
(4.5)is eroded,and the resulting PDF becomes power-like:
N(m p?1)∝m?1/2
p
,N(m p?1)∝m?2p.(4.6)
The pancake disruption accelerates this erosion as well and
makes the PDF more complicated.
This discussion shows that slowly evolving pancakes
with slower transversal motions can be separated into a
special subpopulation for which the surface density changes
slowly,m p≈q,and the PDF(4.5)correctly describes the
pancake distribution during the essential period of evolution.
This subpopulation is singled out by conditions
|Q(z12)/z12|≤|Q(y12)/y12|≤?/τ,?<1
and the probability of existence of such pancakes is pro-
portional toτ?2.This factor describes the disappearance of
such pancakes in the course of evolution.This subpopulation
can be however quite rich(see.,discussion in Sec.9).
5STATISTICAL CHARACTERISTICS OF
DARK MATTER STRUCTURE ELEMENTS
Results obtained above allow us to?nd the approximate
PDF and other characteristics of structure elements.The
structure element with a size(mass)m is de?ned as a pan-
cake with the size m formed at a momentτthat not merged
with any other pancake.
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8Demia′n ski&Doroshkevich
5.1Merging of dark matter structure elements
As before the characteristics of structure element at a mo-
mentτare expressed through characteristics of initial per-turbations.The approximate expression for the PDF of structure element can be written in a form similar to the known equation of coagulation:
N str(m,τ)=(5.1) ∞
dy m0dxN cr(x,τ)N c(m?x,x,y,τ)dW merg(x,m?x,y)
dy
.
The functions N cr,N c&W merg are given by(3.6),(3.15), (3.17)&(3.18).Here the?rst term describes the formation of two pancakes with sizes x&m?x and a separation y and their merging to a pancake with the size m while the second term describes merging of the pancake of size m with another pancake.If the mass exchanged during merging is incorporated into the forming pancake then the?rst term in (5.1)has to be appropriately changed.
Here,as the?rst step of investigation,we will use the simpler approximate approach based on the survival proba-bility of a pancake with size m to avoid merging with larger pancakes with sizes x≥m.For the more interesting case of smaller pancakes with q0?m≤x<1,the most prob-able process is the formation of two pancakes with sizes m&x≥m at a small separation|D sep|≤0.5(x+m)that means,as follows from(3.1),formation of one structure ele-ment with a size x.This process is described by the second term in(5.1).Because in this case the probability of merging quickly decreases for larger separations|D sep|≥0.5(m+x) and the function W c(m,x,D sep)weakly depends on D sep, we will use the approximate expression for the probability of merging,P mrg,
P mrg(m,τ)≈2W c m,τ;mτ ,(5.2)
P mrg(m,τ)≈erfc μ(m)2 ,m?1,(5.3) and for the survival probability,P surv,
P surv(m,τ)≈1?P mrg(m,τ),(5.4)
P surv(m,τ)≈erf μ(m)2 ,m?1.
In spite of the approximate character of this approach,it allows us to obtain reasonable estimates of the expected e?-ciency of merging and of the large scale bias.As it is directly seen from(5.4),forμ(m)≤τ,
P surv(m,τ)∝μ(m)τ?1,
what characterizes the impact of pancake
merging.Figure1.The PDF of structure elements,N str(m,τ),the mass distribution function,mN str(m,τ),and the fraction of com-pressed matter,f(>m),vs.the masses(sizes)of structure el-ements,m/m m,are plotted for?ve moments of time:τ=0.1 (solid line),τ=0.3(dashed line),τ=0.5(dot-dashed line),τ= 0.7(dot-dot-dot-dashed line),τ=2(long dashed line).
5.2Statistical characteristics of structure
elements
With this survival probability the approximate PDF for the structure elements,N str(q,τ),can be written as follows:
N str(m,τ)∝P surv(m,τ)N cr(m,τ).(5.5) These relations allow us to obtain also the approximate mass distribution function,N(m)
str
(m,τ),characterizing the distri-bution of compressed matter over the structure elements. For the more interesting case m?1we have
N str(m,τ)≈
24
2πτ
dμ
√√
dm
P surv(m,τ)(5.7) =n(>m,τ)P surv(m,τ)+ ∞m dmn(>m,τ)dP surv(m,τ)
Formation and evolution of structure
9
Figure 2.Top panel:the mean comoving linear density of a pancake,l 0n str (>m,τ),vs.masses m/m m are plotted for ?ve moments of time:τ=0.1(solid line),τ=0.3(dashed line),τ=0.5(dot-dashed line line),τ=0.7(dot-dot-dot-dashed line),τ=2(long dashed line).Bottom panel:the mean separations of pancakes,D str (>m,τ)/l 0,vs.the fraction of compressed matter f (>m ),for the same time moments.
distribution function is rapidly changing due to the merging of smaller https://www.doczj.com/doc/151077856.html,ter the evolution is slower and it is sustained by growth of median mass due to the progressive matter concentration within richer structure elements.
The same e?ect is clearly seen in Fig.2where the mean linear number density of structure elements,l 0n str (>m,τ),is plotted for the same time moments.Rapid merging of low mass pancakes and formation of more massive pancakes changes the shape of this function at m ?m m and m ?m m while at m ～m m evolution is very slow.The function D str (f )plotted in Fig.2(bottom panel)is similar to that found in simulations (DFGMM).
Time dependence of the mean,
τ≤1.5,(5.8a )m m (τ)≈0.03+2.2τ,
τ≤1.5.
(5.8b )
Faster growth of
D str (m m ,τ)≈1.5·l 0
τ+0.07
(5.8c
)
Figure 3.Top panel:Time dependence of the mean mass,
Bottom panel in Fig.3shows evolution of the lin-ear number density of structure elements,l 0n ,caused by their exponentially fast formation at smaller τ,and later,by successive merging which is well ?tted by the expres-sion D str (>q,τ)∝τ?0.4what is slower then that found in DFGMM.
5.3
Parameters of structure in observed catalogues
In previous Sections the formation and evolution of struc-ture was described in a comoving space.But in observed
catalogues the redshift position of galaxies along the line of sight is used.This di?erence distorts the parameters of observed structure with respect to the theoretical expecta-tions (see,e.g.,Melott et al.1998;Hui,Kofman &Shandarin 1999).To take into account this distortions the theoretical relations given above need to be modi?ed.
In observational catalogues the distance to a galaxy is de?ned by its observed velocity which can be found from
c
0000RAS,MNRAS 000,000–000
10Demia′n ski&Doroshkevich
(2.1)as
v i=
H(z)
B
dB(z)
β2+1,and for?=π/2τv=τ.This means that the observed structure parameters will be randomly increased with respect to those found in the comoving space.
The value of this distortion depends on the cosmological parameters and redshift and is superposed with distortions generated by the structure disruption,selection e?ects,and other random factors.The well known example of such dis-tortion is the e?ect of so called“?nger of God”.The direct comparison of simulated structure parameters in comoving and redshift spaces performed in DMRT demonstrates a moderate dependence of the main structure properties on these factors.But some properties of the structure such as characteristics of galaxy distribution within walls are di?er-ent in comoving and redshift spaces.More details can be found in Sec.9and DMRT.
6FORMATION OF VOIDS
The same method can be used to solve the complementary but more complicated problem of void formation.Accord-ing to the Zel’dovich theory the probability to?nd a’void’with a size r>r v is identical to the probability that the inequality
(1+z)r=q1?q2?τ?S12≥(1+z)r v(6.1) holds under the conditions that pancakes have been formed near the boundary points q1&q2,and in the absence of any pancakes between points q1&q2.This is equivalent to the probability that the inequality
?S12≤[q12?(1+z)r v]/τ(6.2) holds under the same conditions.
More accurately we can consider the under dense re-gions bounded by pancakes with masses m1&m2(or with masses exceeding the threshold mass,m1,m2≥m thr)in the absence of any pancakes with masses m≥m thr be-tween points q1&q2.The methods discussed above and sup-plemented by the void probability function technique could be used,in principle,to obtain such probability but the derivation becomes very cumbersome due to many addi-tional conditions.Numerical methods similar to that used by Sahni et al.(1994)could be more suitable for such an investigation.
Let us remind however that the parameters of under dense regions are closely linked to the spatial distribution of initial gravitational potential what allows to specify con-veniently the large scale perturbations.This link is clearly demonstrated by the adhesion approach(see,e.g.,Shandarin &Zel’dovich1989)and was discussed in details in Paper I.It can also be described with the technique considered above. Thus,for example,the large scale modulation of the pancake distribution by the spatial distribution of this potential can be described as a modulation of the e?ective’time’moment τef f.
To illustrate this statement we can consider the pancake formation for a given potential di?erence,?φ,between two points with comoving coordinates x1&x1+D cell.In this case the distribution function of the pancake with a size m?D cell at the point with a comoving coordinate x1≤y≤x1+D cell is given by(3.6)&(5.5)with
1
τ 1?κφ(D cell,y)?φ
σφ
,(6.4)
G r(y,D)=yG12(y)+(D?y)G12(D?y),
and for the mean Eulerian linear number density of pancakes n e(y)= ?r pan1+τG n(y,D cell)?φ
Formation and evolution of structure11
within richer wall-like elements and is seen in simulations as a strong concentration of high density peaks within?laments
and richer walls.It results in an excess of compressed cold
gas inside”proto”walls before reheating.But after reheat-ing further compression and cooling of gas are inhibited due
to the growth of entropy of uncompressed gas.This excess of
cold,low entropy baryonic matter,reached at the reheating redshift,z=z h,can be considered as the excess of galax-
ies within richer walls that is the large scale bias in spatial
galaxy distribution.
In Paper I the modulation of the rate of pancake forma-
tion by the large scale perturbations of initial gravitational potential was discussed.Here we consider the direct inter-
action of earlier and later pancakes,that allows us to obtain
more reliable estimates of the large scale bias.With the tech-nique developed above we can consider only the direct inter-
action of two population of pancakes,namely,those created
at z=z h,and at z=0,and we have to neglect the mutual in?uence of intermediate population of pancakes accumu-
lated by the walls.Such an in?uence can in principle be
considered but the description becomes very cumbersome. So,our estimate presented below is actually the lower limit
of the possible large scale bias.Perhaps,this problem can
be studied in simulations which take into account all impor-tant factors together(see,e.g.,Sahni et al.1994;Cole et al.
1998).
For the warm dark matter model discussed in Paper I
the mass of DM particles restricts the minimal size of created
pancakes to the correlation scale of initial density pertur-bations,r c.For the CDM power spectrum considered here
there are no natural limits and,in principle,pancakes with
arbitrarily low mass can form.Formally,it is due to the di-
vergence of higher moments of power spectrum and,because of this,the typical scale r c is zero.In this case,however,
the typical scale q0introduced by(3.4)plays similar role
and discriminates the process of earlier low mass pancake formation,described through the deformation tensor,and
later formation of structure elements discussed above.The
typical mass of such low mass pancakes given by the expres-sion(3.4)exceeds the estimates of minimal baryonic mass
formed before the reheating,M min～106M⊙(Tegmark et al.1997),and the value q0can be accepted as a natural limit of pancake size.Further evolutionary history of low mass
pancakes is uncertain,and this problem should be consid-
ered separately.
Thus,for the CDM power spectrum we assume that: (i)All baryons accumulated by structure elements with a size m≥q0,at the redshift z=z h,τ=τh,are incorporated into observed’galaxies’.
(ii)The formation of such clouds is interrupted at the
redshift z=z h,τ=τh by the instantaneous reheating of
the uncompressed gas.
Both assumptions are very restrictive but they can be used
for rough estimates of the e?ciency of this mechanism of generation of the large scale bias.The moment of reheating is,probably,restricted to7≤z h≤15.The lower limit is imposed by the observational constrains.The upper limit is imposed by the very fast growth of the inverse Compton cooling for larger redshifts.
This means that for the models with?m～0.3–1the reheating probably occurred atτh=τ(z h)≈(0.15–0.3)τ0.7.2Statistical characteristics of biased cold
matter distribution
The biasing factor on the LSS and SLSS scales can be es-timated in the same manner as the probability of merging (5.2).Now we are interested in the mass of cold matter col-lapsed atτ=τh which was accumulated at the momentτby a pancake with a size D?m cld.The approximate PDF for this mass can be written as
N cld(m cld,τh)≈2N c m cld,τh;D,τ,m cld+D
τ1
=
1
τ
,κ1(D)≈
1
N cld(m cld,τh)
τ1
exp m cld
12
Demia′n ski &Doroshkevich
8
DYNAMICAL CHARACTERISTICS OF THE PANCAKES
In this section we will ?nd two dynamical characteristics of pancakes,namely,the velocity of pancakes as a whole and the dispersion of matter velocity within pancakes caused by compression of pancakes.
The velocity of an infalling particle,v i ,can be found from (2.1)as
v i (q ,z )=dr i /dt =
H (z )
B
dB (z )1+1.2?m
,?m +?Λ=1,(8.2)
β≈2?B (z )
1??m
q 12
q 1
q 2
v (q )dq =
H (z )
q 12
,
?φ12=φ(q 1)?φ(q 2),q cent =(q 1+q 2)/2.(8.4)
The pancake position,r pan ,was given by (3.16).The pecu-liar velocity of pancake is
u =v pan ?H (z )r pan =
H (z )
q 12
.
(8.5)
The mean peculiar velocity,=0,and its dispersion,σu ,is
σ2u (z )
=
H 2(z )
q 2
12
.(8.6)
The distribution function of peculiar velocity is Gaussian as before.In dimensional variables,at z =0,and for q 12?1,we have:
σu (0)=H 0[β(0)?1]σs /
√
1+z
q 3?q cent ?β(z )B (z )
S (q 3)?
?φ12
H 2(z )
σ2v =
q 2
2
√
Formation and evolution of structure13
9COMPARISON WITH SIMULATED AND OBSER VED STRUCTURE PARAMETERS Some characteristics of large scale galaxy distribution can be extracted from available catalogues of galaxies and clusters of galaxies and catalogues of absorbers in spectra of quasars. Some characteristics of large scale DM distribution can be derived from available simulations.All these characteristics are distorted by selection e?ects,bias between spatial distri-bution of DM and galaxies,and other factors what compli-cates the direct comparison of characteristics obtained with di?erent methods and data bases.
Nonetheless,the comparison of even so distorted char-acteristics with the expected characteristics discussed above can be interesting as it allows one to compare various esti-mates and illustrates their sensitivity to basic cosmological parameters and other factors.More detailed discussion can be found in DMRT&LCRS2.
9.1Observed and simulated parameters of the
structure
Analysis of galaxy distribution in the Las Campanas Red-shift Survey(Shectman et al.1996)shows that the richer and poorer structure elements can be assigned to wall-like and?lamentary populations,but the accurate demarcation of these subpopulations is problematic.Both subpopulations accumulate≈40–60%of galaxies.Filaments?ll the gaps between the walls and form a random network with the mean cell size D obs
f～10–12h?1Mpc at z=0.The richer walls can be formed due to an anisotropic compression of matter within slices with a typical size of R obs
wall≈20–25h?1Mpc and with a typical separation of D obs
wall～50–60h?1Mpc (LCRS1&LCRS2).The walls are signi?cantly disrupted by the small scale clustering of galaxies(see,e.g.,Fig.5in Ramella et al.1992).The velocity dispersion within such
wall-like elements was estimated asσ(obs)
v～350–400 km/s(Oort1983a).The bulk velocities of galaxies are now
estimated asσobs
u～400km/s(see,e.g.,Dekel1997).The observed surface density of structure elements is heavily dis-torted by the selection e?ects and small scale clustering and is equally well?tted to a power law,exponential distribu-tion,and functions discussed in Secs.4&5.
The structure in the DM distribution was simulated and analyzed by DMRT for the SCDM model with?m=1,h= 0.5,ΛCDM model with?m=0.35,?Λ=0.65,h=0.7and OCDM model with?m=0.5,h=0.6.For all these models the normalization to the two year COBE data were used. These simulations reproduce the structure with the main pa-rameters similar to those found in observations.The analysis shows that walls accumulates～40%of DM with a mean size
of”proto walls”R sim
wall～15–20h?1Mpc and with a mean separation of walls and?laments D sim
wall～50–70h?1Mpc, D sim
f～9–14h?1Mpc,respectively.
In all the models the simulated velocity dispersion
within walls,σsim
v
,is the same along all three principal di-rections of walls and it is generated by the wall curvature and disruption into a system of high density clumps.The walls are seen usually as ordered sets of irregular high den-sity clumps connected by lower density bridges.The degree of wall disruption depends on the code used and reached resolution,and it is more conspicuous in the models with larger?m.In the redshift space this inner structure is partly eroded and characteristics of DM walls are similar to that found for the observed galaxy distribution.
The isotropy of simulated velocity dispersionσv con-?rms the essential in?uence of small scale clustering on the properties of both observed and simulated structure ele-ments.The instability of a thin compressed layer was an-alyzed in the linear approximation by Doroshkevich(1980), and Vishniac(1983),and was recently simulated by Valinia et al.(1997).The clustering rate and parameters of formed clusters depend on the density of compressed matter and properties of transversal velocity?eld.
9.2Comparison of simulated and expected
structure properties
Theoretical parameters of DM wall-like structure elements are listed in Table I for the median mass m=m m and for l c/l0=0.056.The variations of R wall listed in Table1are moderate and,in the range of precision of our approximate consideration,these values are similar to those observed and simulated.The di?erences become more signi?cant only for the OCDM models.The value of D str is sensitive to the as-sumed mass threshold(see Figs.2&6)and can be adjusted.
The simulated structure elements are also distorted by the small scale clustering but owing to the rich statistics of such elements a more detailed quantitative comparison of simulated and expected properties of the structure can be performed.To do this we will consider the PDFs(4.4)and (4.5)for the surface density of?laments and pancakes,the PDFs for velocity u and velocity dispersionσv and direct estimates of important parameters
τu=
√
H0l0(β?1)
,m v/l0=
2
√
H0l0(β?1)
,(9.1) following from relations(8.7)and(8.11).
To characterize the?lamentary and wall-like subpopu-lations of structure separately the analysis was performed both in the comoving and redshift spaces for three subsam-ples of structure elements.The?rst contained all particles, the second and third incorporated the richer and poorer structure elements with N mem>200and N mem<200, respectively.Here N mem is the number of DM particles within a structure element bounded by the threshold den-sity n thr/
To obtain the required PDF a set of rectangular sam-pling cores was prepared and the number of DM particles in the intersection of separate structure elements with these cores was taken as a characteristic of surface density of struc-ture elements.For each of the cluster its velocity u and veloc-ity dispersionσv along the core were also found.To depress the impact of small scale clustering the analysis was per-formed for the core size L core=10h?1Mpc,for the second subsample,and for L core=4h?1Mpc,for the?rst and third subsamples when the core size is restricted by the separation of?laments.The random intersection of cores and structure elements generates signi?cant excess of poorer clusters.To depress this e?ect poorer clusters were rejected and the trun-cated PDFs were considered.Even so,the number of clusters used was～3500,for the second,and～10000,for the?rst
c 0000RAS,MNRAS000,000–000
14Demia′n ski &Doroshkevich
Table 1.Main expected parameters of the SLSS.?m ?Λh l 0τ0R wall D str τu m v /l 0τv m p /l 0τp h ?1Mpc h ?1Mpc h ?1Mpc 100.513.20.7～20.7～19.80.52 1.20.42 1.00.410.50.50.718.90.6～25.5～26.80.500.622.00.3～14.5～26.90.280.50.270.80.350.350.650.726.90.4～23.5～37.10.40
0.9
0.36
1.3
0.44
0.35
0.7
26.9
0.2
～12.0
～
34.5
Figure 5.The simulated PDFs of surface density of structure ele-ments,N str (m ),vs.m/
and third subsamples.The random orientation of structure elements and cores can increase the measured surface den-sity (up to two times for homogeneous matter distribution,Kendall &Moran 1963),and reduces the measured velocity of structure elements along the core,u c ,by a factor of
√3τuc .
(9.2)
To compare the simulated and theoretical PDFs the two parameters ?ts were used.For the second subsample the distribution of surface density was ?tted to the expression
N w =a w x w e ?x w
erf (
√
(4.5)with the correction to the pancake merging discussed in Sec.5.For the ?rst and third subsamples the expression N f =
a f
x f )],
x f =
,(9.
4)
Figure 6.The simulated PDFs of velocity dispersion of struc-ture elements,N str (σv ),vs.σv /<σv >for the full sample (top panel)and subsamples of richer (middle panel)and poorer (bot-tom panel)structure elements for the ΛCDM model.The ?ts (9.3)are plotted by solid lines.
reproduces approximately the theoretical relations (4.4)and gives better ?ts.Here parameters b w &b f describe the trun-cated character of ?tted PDF whereas parameters a w &a f provide its normalization.For velocity of clusters,u ,the PDF is found to be well ?tted to the expected Gaussian dis-tribution,the expression (9.3)with parameters a v &b v ?ts the velocity dispersion σv .
For ΛCDM model these PDFs are plotted in Figs.5and 6together with the best ?ts.It turns out that di?erent PDFs verify the accepted discrimination of two populations of structure elements.For the second subsample the theo-retical estimate of mean wall size
0,
(9.5)
links the parameters τ0and
OCDM models the parameters τu (for all samples)and
c
0000RAS,MNRAS 000,000–000
Formation and evolution of structure15
and third subsamples the distributions plotted in Fig.6and
corresponding parameter
The formal precision of these estimates can be taken
as～7–10%(precision of?ts)but a real precision can be estimated by the comparison with the value of the input pa-rameterτ0also listed in Table I.Di?erences between results
obtained in comoving and redshift spaces do not exceed the reached precision.For the SCDM model the expected and reconstructed parameters di?er by a factor of～1.5what
can be partly caused by the strong wall disruption.More-over,this simple description is correct for m/l0≤1whereas more cumbersome general relations describe the properties
of richer walls which dominate the SCDM model.
In all these cases the simulated mass distribution can be equally well?tted to a power law with the exponentκ～1.5–2.Such distribution is similar to that described analyt-ically for the matter concentration within a set of clusters with a surface densityσcls∝r?γ(see also discussion in Miralda-Escude et al.1996).In this case the measured PDF and mass function are also expressed by the power law
dW cls∝σcls rdr∝σ?2/γ
cls
dσcls.(9.6) Of course,pro?les of separate irregular clusters can vary over a wide range and such description and interpretation illustrate only the important role of small scale clustering.
9.3Evolution of DM structure
Results obtained in Secs.3,4&5show that during the most interesting periodτ?l c/l0evolution of DM structure shows features of self-similarity and important characteris-tics of structure can be expressed as functions of the pa-rameterμ(q)/τ.This approximate self-similarity is caused by the Zel’dovich approximation and occurs for any dis-tribution function of initial perturbations and any power spectrum.It becomes more transparent for simple structure functions,at q0?q≤1(Appendix I),typical for the CDM like initial power spectrum,but becomes more cumbersome forτ～l c/l0?1,andτ≥1,when the in?uence of the scales q0&L0,introduced by(I.6),(I.7)becomes important.This self-similarity allows us to characterize the structure evolu-tion by the time dependence of several typical parameters such as the mean and median masses,
This approximate self-similarity is partly violated due to the evolution of pancakes,discussed in Sec.4,and small scale pancake disruption as they are not described by the Zel’dovich theory.Nonetheless,the observations and simu-lations show that in a universe dominated by cold DM parti-cles general properties of matter distribution are quite simi-lar at high and small redshifts(see more detailed discussion in Governato et al.1998;Jenkins et al.1998).Self-similarity of structure evolution was previously discussed for the scale-free power spectra(see,e.g.,Efstathiou et al.1988).
The evolution of observed structure formed by galaxies can however be far from the self-similar evolution of DM structure due to the small and large scale bias.Nonetheless, as it was discussed above,the main parameters of DM struc-ture can be compared with observed galaxy distribution. Thus,in particular,the observed separation of?laments,at z=0,is about4–6times smaller then the typical separa-tion of walls(see,e.g.,Efstathiou et al.1988).Such result
can be attributed to pancakes formed by compression of a
slice with thickness m f～(0.1–0.05)m m≈1–2h?1Mpc at redshift z～3–5.In smaller pancakes the formation of
galaxies can be suppressed and they can be associated with
weak Ly-αabsorbers observed far from galaxies(Morris et al.1993;Stocke et al.1995;Shull et al.1996).
10SUMMARY AND DISCUSSION
In this paper we continue investigations and statistical de-scription of the process of structure formation and evolu-tion initiated in our previous papers(Buryak et al.1992; Demia′n ski&Doroshkevich1992,1997;DFGMM;Paper I) and based on the nonlinear theory of gravitational insta-bility(Zel’dovich1970).The new elements discussed above are the approximate statistical description of the nonlinear structure evolution manifesting itself as a successive mat-ter concentration into more and more massive structure ele-ments and the estimates of the large scale modulation of the spatial distribution of luminous matter relative to DM and baryonic components.We show that,as has been discussed in Sec.9.4,the evolution of DM structure demonstrates some features of self-similarity and the main characteristics of the DM structure can be expressed through the structure func-tions of initial perturbations and through parameters of the initial power spectrum and cosmological models.It is shown –in accordance with simulations–that in a low density models the nonlinear evolution occurs on the scale～20–25h?1Mpc and results in formation of wall-like component of the structure of the universe.
Results discussed in Section3show that,for the CDM-like power spectra,the high nonlinear matter compression into low mass pancakes and the percolation take place al-ready atτ≈l c/l0?https://www.doczj.com/doc/151077856.html,ter evolution leads to a rapid growth of typical mass of DM pancakes.The theoretical description discussed above can be directly applied to the interpretation of deep pencil beam galactic surveys and ab-sorption spectra of quasars,what allows us to consider to-gether rich observational data accumulated by these meth-ods for both small and large redshifts.For larger3D catalogs and simulations this description can be applied to results obtained with the core-sampling analysis as it was demon-strated in Sec.9.2.
Comparison with simulations shows that the velocity dispersion of matter compressed within the wall-like ele-ments is more sensitive to the cosmological parameters,large scale modulation of spatial galaxy distribution and proper-ties of DM component.The small scale clustering generates the essential di?erence between the expected and simulated velocity dispersion and allows us to discriminate the cosmo-logical models with respect to the matter density,?m,and the composition of DM.This clustering is smaller for the models with?m≤0.5,but for models with?m≤0.3it is more di?cult to reproduce the sizes,separations of observed walls,and the high matter concentration within the SLSS. The observed structure parameters are best reproduced for the models with?m h≈0.2–0.3.These values are consis-tent with estimates obtained both from the simulations of cluster of galaxies(Bahcall&Fan1998;Cole et al.1997,
c 0000RAS,MNRAS000,000–000
16Demia′n ski&Doroshkevich
1998)and from the observations of high redshift supernovae (Perlemutter et al.1998).
Two factors can extend the set of acceptable cosmologi-cal parameters.The?rst is a more complicated composition
of DM component,what means that an essential fraction of
DM can be associated with relatively hot and/or low mass particles(see,e.g.,Colombi et al.1996;Brustein&Hadad
1998).The simple estimates based on the Trimain-Gunn
relation(Trimain&Gunn1979)show that if the recently formed wall-like SLSS is sensitive to the in?uence of low
mass relic particles,with M p≤3–5eV,then the structure properties at higher redshifts are sensitive to more massive particles as well.This means that for the MDM models with
more complicated DM composition the agreement between
observed and simulated properties of the LSS and SLSS can
be achieved in a broader range of?m and h and,moreover, the analysis of observed structure evolution can specify the DM composition.The models including some fraction of un-stable dark matter particles(Turner et al.1984;Doroshke-vich et al.1989)seem also to be perspective.Unstable parti-cles can produce reheating by photo decay whereas the hot products of decay will delay both formation and disruption of the walls.
The second factor is the large scale modulation of spa-tial galaxy distribution.The formation of LSS and SLSS is always accompanied by bias on the same scales,because both processes are caused by the same large scale perturba-tions and,so,are strongly correlated in space.The e?ciency of such bias depends on many factors,such as the redshift of reheating period,and the formation and evolution of cold clouds.The rough estimates of the bias presented in Sec. 7show that the bias factor could be quite high(see also Demia′n ski and Doroshkevich1997;Paper I)and together with other factors it can essentially distort the spatial dis-tribution of galaxies with respect to the DM distribution. These estimates could be enhanced by taking into account the mutual interaction of pancakes with various sizes and moments of formation.More representative estimates of the bias can,probably,be obtained from simulations such as, for example,Sahni et al.(1994)and Cole et al.(1998).
This mechanism of bias generation implies early,for
z≥5,formation of the main fraction of low entropy gaseous clouds,that can be identi?ed with’proto galaxies’.The re-heating does not prevent further formation of DM pancakes, which can be identi?ed with a population of gas clouds re-sponsible for weaker absorption lines observed at high red-shifts(Miralda-Escude et al.1996;Hernquist et al.1996)but it inhibits the formation of high density,low entropy gaseous clouds and delays the formation of galaxies in extended un-der dense regions.Further transformation of formed earlier ’proto galaxies’into observed galaxies is a slow and compli-cated process continuing up to now and earlier formation of such clouds is not in contradiction with the observed peak of galaxy formation at redshifts z≈2–3(Steidel et al.1996).
The observed strong variations of galaxy distribution
with respect to the DM and intergalactic gas distribution,
ρgal/ρgas,can be,partly,associated with this bias.Indeed,if in clusters of galaxies this ratio is found to beρgal/ρgas≈0.2 (see,e.g.,White et al.1993)then,for example,within Bo¨o ts Voidρgal/ρgas→0(Weistrop et al.1992).The existence of’invisible’structure elements,which are now seen as gas clouds responsible for weak Ly-αabsorption lines situated far from galaxies(≈5–6h?1Mpc,Morris et al.1993;Stocke et al.1995;Shull et al.1996)can also be considered as an evidence in favor of large scale bias.
Simulations show also existence of bias in the distribu-tion of DM and’galaxies’identi?ed with the highest peaks in the initial density distribution(see,e.g.,Eke et al.1996). Such bias is similar,in some important respects,to that generated by reheating.It operates on really large scales (Bower et al.1993)and can be essential for the SLSS for-mation as well.It results in structure composed of?laments and sheets in the distribution of’galaxies’(Doroshkevich, Fong&Makarova1998).Other mechanisms of bias for-mation discussed recently(Coles1993;Sahni&Coles1995; Tegmark&Peebles1998),operate on signi?cantly smaller scales.
Simulations allow us to test the joint action of vari-ous factors on the structure parameters and,therefore,they now seem to o?er more perspective way for detailed inves-tigations of the structure formation and evolution.They need,however,to be essentially improved in order to dis-criminate the spatial distribution and other parameters of ’galaxies’and dark matter.The methods used for the mea-surement and description of simulated and observed struc-ture should be also improved as now they provide us with limited information about structure properties.These re-strictions become especially important at higher redshifts, where the simulated density contrast is https://www.doczj.com/doc/151077856.html,rge disper-sions of measured values make also any comparison of sim-ulated and theoretical structure parameters more di?cult. Nonetheless,such approach seems to be perspective because further progress in these directions can be reached. Acknowledgments
We are grateful to our referee,Peter Coles,for very useful comments and criticism.This paper was supported in part by Denmark’s Grundforskningsfond through its support for an establishment of Theoretical Astrophysics Center and Polish State Committee for Scienti?c Research grant Nr. 2-P03D-022-10.AGD also wishes to acknowledge support from the Center for Cosmo-Particle Physics”Cosmion”in the framework of the project”Cosmoparticle Physics”. REFERENCES
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Appendix I
Correlation functions for initial perturbations.
To investigate mutual interaction of perturbations and to reveal their in?uence on the nonlinear processes of struc-ture formation we can use the conventional distribution functions and conventional mean values.In this appendix we introduce a few correlation and structure functions which describe the relative spatial distribution of important pa-rameters of perturbations.
We begin with the structure function of gravitational potential perturbations which characterizes correlation of the gravitational potential in two points q1and q2.As the power spectrum is a function of the absolute value of wave number|k|only this structure function depends on
c 0000RAS,MNRAS000,000–000
18Demia′n ski &
Doroshkevich
Figure 7.Structure functions G 0(top panel)and G 0,G 1,G 12&?G https://www.doczj.com/doc/151077856.html,grangian coordinate q/l 0for the SCDM power spectrum.
q 12=| q 1? q 2|and for the perturbations of gravitational potential we have 3
l 0σ2
s
=(x 1?x 3)i G 1(x 13)?(x 2?x 3)i G 1(x 23),
3
x 2
G 12(x ))|x =x 12,
3l 0
x 2
G 23(x ))|x =x 21,
where the projection operator ?ij =δij ?x i x j /x 2is used
and x =q/l 0.The scale l 0was introduced by (2.5).
In the general case these functions can be expressed through the power spectrum,p (k ),and spherical Bessel func-tions and we obtain
G 0(q )=
3
k 2
1?
sin y
(2π)3/2σ2
s
∞
p (k )y ?3/2J 3/2(y )dk =G ′0/x,
G 2(q )=?
3l 2
(2π)3/2σ2
s
∞
k 4p (k )y ?7/2J 7/2(y )dk =G ′2/x,
where y =kq,x =q/l 0.
G 12(x )=G 1(x )+x 2G 2(x )=G ′′
0=(xG 1)′
,G 23(x )=3G 2(x )+x 2G 3(x )=G ′
12/x,(I.4)
G 234(x )=3G 2(x )+6x 2G 3(x )+x 4G 4(x )=G ′′
12.G 1(0)=G 12(0)=1,
G 23(0)=G 234(0)=?
l 2
2a 0
ln(1+x +a 0x 2)
?
1
4a 0?1
tg ?1
x
√
2+x
,
G 1(x )≈[1+x +a 0x 2]?1,
G 12(x )≈G 21(x )[1?a 0x 2
],
(I.6)
G 23(x )≈?2G 31(x )(1+3a 0x 2?a 20x 3
)/x,G 234(x )≈6G 41(x )(1+1.3a 0x 2+4a 20x 3?a 30x 4),
a 0=5(l 0/L 0)2≈0.3,L 0≈4.1l 0,
where the typical scale L 0is de?ned as L 20
=3π
2
∞
p (k )
1?
sin kL 0
q 2
0+x 2+a 0x 2]?1,
(I.7)
G 12≈
G 21(1
+q 0)
?1
[1?a 0x 2
+
q 2
0/
1+12l 2c /l 20]?1
?1
can be used for all x.In this case the functions G 2(x ),G 23(x )
and G 234(x )can be found through the relations (I.4).
Appendix II
Distribution function of pancakes.
We begin with the distribution function for the di?er-ence of displacements at points with coordinates q 1and q 2.
c
0000RAS,MNRAS 000,000–000
Formation and evolution of structure19 The normalization and notation introduced in Appendix I
are used.The dimensionless’time’τis introduced by(2.9).
Let us consider the deformation of spherical cloud with
a diameter q.For the case q?q0the general deformation of
cloud can be characterized by the2D random scalar function
Θ(θ,φ)=[S(q/2)?S(?q/2)]·q/q2instead of the deforma-
tion tensor d ik.Expansion of this function into spherical
harmonics allows one to obtain an approximate description
of deformation of the cloud with a reasonable accuracy.Now
we will take into account the spherical and quadrupole defor-
mations only.These modes describe successfully the trans-
formation of the cloud into?laments and sheets while higher
order deformations relate mainly to the cloud disruption.In
this case we can consider the deformation of the cloud in
three principal directions,namely,x,y,&z.The dispersion
of displacement di?erences in these directions are
σ2x=<[S x(q/2)?S x(?q/2)]2>=2[1?G12(q)],(II.1)
σ2y=σ2z=σ2x
and their correlations are described by the coe?cient r xy:
σ2x r xy=<[S x(q/2)?S x(?q/2)][S y(q/2)?S y(?q/2)]>
r xy=?q22)2
1+q/
√
dζ=
3
2π
e?ζ2/2 1+erf ζ2 2,ζ≥0,(II.3)
dW(ζ)
4
√√
8 1+erf μ(q)2τ 3,(II.5)
μ(q)=
q
2[1?G12(q)]
.
This function characterizes the intersection of two particles
before the time momentτor for the pancake separation
≥q and,in fact,(8/7)W cr is the cumulative distribution
function for pancakes with masses>q,for a givenτ.
The standard technique(see,e.g.,BBKS)can be used in
order to?nd the mean comoving linear density of pancakes
with a given?S1=q/τalong a random straight line.It
is expressed through the characteristics of the derivations of
function Q introduced by(3.2),Q i=?Q/?q i,and for q0?1
=0,σ2d1=
σ2d2=
(q0is introduced by(I.7)).The required linear number den-
sity,n(>q),is
l0n(>q)=
3<μr>
√
Q21+Q22+Q23exp ?Q212σ2d2?Q23
2 ?G12
D1+D2
20Demia′n ski&Doroshkevich
For a given?S(D1)=D1/τ1the conditional mean val-uesand dispersionsσc(D2,D sep)are described by the standard relations
=σ2r sμ1/τ1,σc(D2,D sep)=σ2
√
τ2?r s
μ(D1)
D1?φ(D sep+q cent+D2/2)?φ(D sep+q cent?D2/2)
2σ2ψ=
G0(D1)
D22
+
G0(q31)?G0(q41)?G0(q32)+G0(q42)
σψσ1=μ(D1)rψ,
<ψ?S(D2)>
D1D2σψ
.
For the conditional mean value and dispersion ofψwe have
<ψc>
1?r2s +
2μ(D1)μ(D2)
σψ 2=1?r2ψ (μ(D1)?μ(D2))21+r s .
Finally for the required functionχ(D1,D2,D sep)we obtain
χ=[D sep?<ψc>]/σcψ.(III.6)
Appendix IV
The dynamical characteristics of pancakes.
A correlation of pancake velocity described by(8.4)in points with di?erent transversal coordinates is convention-ally characterized by the correlation coe?cient
r v=
<[φ(q1)?φ(q2)][φ(q3)?φ(q4)]>
<[φ(q1)?φ(q2)]2><[φ(q3)?φ(q4)]2>
=
G0(q14)?G0(q13)+G0(q23)?G0(q24)
G0(q12)G0(q34)
.(IV.1)
For small q12,&q34it transforms into(8.8).
The important characteristic of the pancake is the ve-locity pro?le around the points q1&q2under the condition ?S(q12)=q12/τ.It is described by the mean conditional pro?le of displacement
q1?q2
2[1?G12(q12)]
,(IV.2) and the conditional dispersion
σ2s(q3)=1?
[G12(q13)?G12(q23)]2
H2(z)
σ2v=
q212
q212
G0(q12)?0.5q212G1(q12)
[1?G12(q12)]2
,
f3(q12)=1?
J12
q212
,
J12=q?112 q1q2dq3[G12(q1?q3)?G12(q2?q3)]2.
c 0000RAS,MNRAS000,000–000
工作表中的表单、表单控件和ActiveX 控件概念 是的，确实如此。在Microsoft Excel 中，使用少量或者无需 使用Microsoft Visual Basic for Applications (VBA) 代码即可 创建出色的表单。使用表单以及可以向其中添加的许多控件和 对象，您可以显著地增强工作表中的数据项并改善工作表的显 示方式。 ? ? 什么是表单？ 无论是打印表单还是联机表单都是一种具有标准结构和格式的文档，这种文档可让用户更轻松地捕获、组织和编辑信息。 ?打印表单含有说明、格式、标签以及用于写入或键入数据的空格。您可以使用Excel 和Excel 模板创建打印表单。 ?联机表单包含与打印表单相同的功能。此外，联机表单还包含控件。控件是用于显示数据或者更便于用户输入或编辑数据、执行操作或进行选择的对象。通常，控件可使表单更便于使用。例如，列表框、选项按钮和命令按钮都是常用控件。通过运行(VBA) 代码，控件还可以运行指定的和响应事件，如鼠标点击。 您可以使用Excel 通过多种方式创建打印表单和联机表单。 Excel 表单的类型 您可以在Excel 中创建多种类型的表单：数据表单、含有表单和ActiveX 控件的工作表以及VBA 用户表单。可以单独使用每种类型的表单，也可以通过不同方式将它们结合在一起来创建适合您的解决方案。 数据表单
为在无需水平滚动的情况下在单元格区域或表格中输入或显示一整行信息提供了一种便捷方式。您可能会发现，当数据的列数超过可以在屏幕上查看的数据列数时，使用数据表单可以使数据输入变得更容易，而无需在列之间进行移动。如果以标签的形式将列标题列出的文本框这一简单表单足以满足您的需求，而且您不需要使用复杂的或自定义的表单功能（例如列表框或调节钮），则可以使用数据表单。 Excel 可以为您的或自动生成内 置数据表单。数据表单会在一个对 话框中将所有列标题都显示为标 签。每个标签旁边都有一个空白文 本框，您可以在其中输入每一列的 数据，最多可以输入32 列数据。 在数据表单中，您可以输入新行， 通过导航查找行，或者（基于单元 格内容）更新行及删除行。如果某 个单元格包含，则公式结果会显示 在数据表单中，但您不能使用数据 表单更改该公式。 含有表单和ActiveX 控件的工作表 工作表是一种类型的表单，可让您在网格中输入数据和查看数据，Excel 工作表中已经内置了多种类似控件的功能，如注释和数据验证。单元格类似于文本框，因为您可以在单元格中输入内容以及通过多种方式设置单元格的格式。单元格通常用作标签，通过调整单元格高度和宽度以及合并单元格，您可以将工作表用作简单的数据输入表单。其他类似控件的功能（如单元格注释、超链接、背景图像、数据验证、条件格式、嵌入图表和自动筛选）可使工作表充当高级表单。 为增加灵活性，您可以向工作表的“”添加控件和其他绘图对象，并将它们与工作表单元格相结合和配合。例如，您可以使用列表框控件方便用户从项目列表中选择项目。还可以使用调节钮控件方便用户输入数字。 因为控件和对象存储在绘图画布中，所以您可以显示或查看不受行和列边界限制的关联文本旁边的控件和对象，而无需更改工作表中数据网格或表的布局。在大多数情况下，还可以将其中许多控件链接到工作表中的单元格，而无需使用VBA 代码即可使它们正常工作。您可以设置相关属性来确定控件是自由浮动还是与单元格一起移动和改变大小。例如，在对区域进行排序时，您可能有一个希望与基础单元格一起移动的复选框。不过，如果您有一个希望一直保持在特定位置的列表框，则您可能希望它不与其基础单元格一起移动。 Excel 有两种类型的控件：表单控件和ActiveX 控件。除这两个控件集之外，您还可以通过绘图工具（如、、SmartArt 图形或文本框）添加对象。 以下部分介绍这些控件和绘图对象，此外，还更为详细地介绍如何使用这些控件和对象。
https://www.doczj.com/doc/151077856.html,4.0 用户控件的概述 用户控件是页面的一段，包含了静态HTML代码和服务器控件。其优点在于一旦创建了一个用户控件，可以在同一个应用的多个页面中重用。并且，用户可以在Web用户控件中，添加该控件的属性、事件和方法。 1．什么是用户控件 用户控件（后缀名为.ascx）文件与https://www.doczj.com/doc/151077856.html,网页窗体(后缀名为.aspx)文件相似。就像网页窗体一样，用户控件由用户接口部分和控制标记组成，而且可以使用嵌入脚本或者.cs代码后置文件。用户控件能够包含网页所能包含的任何东西，包括静态HTML内容和https://www.doczj.com/doc/151077856.html,控件，它们也作为页面对象（Page Object）接收同样的事件(如Load和PreRender)，也能够通过属性（如Application,Session,Request 和Response）来展示https://www.doczj.com/doc/151077856.html,内建对象。 用户控件使程序员能够很容易地跨Web应用程序划分和重复使用公共UI功能。与窗体页相同，用户可以使用任何文本编辑器创作用户控件，或者使用代码隐藏类开发用户控件。 此外，用户控件可以在第一次请求时被编译并存储在服务器内存中，从而缩短以后请求的响应时间。与服务器端包含文件（SSI）相比，用户控件通过访问由https://www.doczj.com/doc/151077856.html,提供的对象模型支持，使程序员具有更大的灵活性。程序员可以对在控件中声明的任何属性进行编程，而不只是包含其他文件提供的功能，这与其他任何https://www.doczj.com/doc/151077856.html,服务器控件一样。 此外，可以独立于包含用户控件的窗体页中除该控件以外的部分来缓存该控件的输出。这一技术称作片段缓存，适当地使用该技术能够提高站点的性能。例如，如果用户控件包含提出数据库请求的https://www.doczj.com/doc/151077856.html,服务器控件，但该页的其余部分只包含文本和在服务器上运行的简单代码，则程序员可以对用户控件执行片段缓存，以改进应用程序的性能。 用户控件与普通网页页面的区别是： ●用户控件开始于控件指令而不是页面指令。 ●用户控件的文件后缀是.ascx，而不是.aspx。它的后置代码文件继承于 https://www.doczj.com/doc/151077856.html,erControl类.事实上，UserControl类和Page类都继承于同一个 TemplateControl类，所有它们能够共享很多相同的方法和事件。 ●没有@Page指令，而是包含@Control指令，该指令对配置及其他属性进行定义。 ●用户控件不能被客户端直接访问，不能作为独立文件运行，而必须像处理任何控件一 样，将它们添加到https://www.doczj.com/doc/151077856.html,页中。 ●用户控件没有html、body、form元素，但同样可以在用户控件上使用HTML元素和 Web控件。 用户可以将常用的内容或者控件以及控件的运行程序逻辑，设计为用户控件，
JAVA程序设计课程设计报告 课题: 学生信息管理系统 姓名： 学号： 同组姓名： 专业班级： 指导教师： 设计时间： 评阅意见： 评定成绩：
目录 一、系统描述 (2) 1、需要实现的功能 (3) 2、设计目的 (3) 二、分析与设计 (3) 1、功能模块划分 (3) 2、数据库结构描述 (4) 3、系统详细设计文档 (6) 4、各个模块的实现方法描述 (9) 5、测试数据及期望结果 (11) 三、系统测试 (16) 四、心得体会 (23) 五、参考文献 (24) 六、附录 (24)
一、系统描述 1、需求实现的功能 、录入学生基本信息的功能 学生基本信息主要包括：学号、姓名、年龄、出生地、专业、班级总学分，在插入时，如果数据库已经存在该学号，则不能再插入该学号。 、修改学生基本信息的功能 在管理员模式下，只要在表格中选中某个学生，就可以对该学生信息进行修改。 、查询学生基本信息的功能 可使用“姓名”对已存有的学生资料进行查询。 、删除学生基本信息的功能 在管理员模式下，只要选择表格中的某个学生，就可以删除该学生。 、用户登陆 用不同的登录权限可以进入不同的后台界面，从而实现权限操作。 、用户登陆信息设置 可以修改用户登陆密码 2、设计目的 学生信息管理系统是一个教育单位不可缺少的部分。一个功能齐全、简单易用的信息管理系统不但能有效地减轻学校相关工作人员的工作负担，它的内容对于学校的决策者和管理者来说都至关重要。所以学生信息管理系统应该能够为用户提供充足的信息和快捷的查询手段。但一直以来人们使用传统人工的方式管理文件档案、统计和查询数据，这种管理方式存在着许多缺点，如：效率低、保密性差、人工的大量浪费；另外时间一长，将产生大量的文件和数据，这对于查找、更新和维护都带来了不少困难。随着科学技术的不断提高，计算机科学日渐成熟，
学习目标： ?掌握CommonDialog。 ?掌握文件操作相关的对话框。 ?理解打印对话框。 6.1对话框： 对话框是一种用户界面接口，用于同用户进行交互，完成一些特定的任务，简单的对话框有对用户操作进行提示的对话框，对重要操作要求用户进行决定的交互对话框等。 这类任务能被独立出来，作为通用的交互处理过程。这些能被独立出来作为通用交互过程的任务常见如下一些： （1）文件选取。 （2）保存设置。 （3）路径选取。 （4）字体选取。 （5）颜色选取。 （6）打印设置。 （7）打印预览框。 在.NET中这些组件是在https://www.doczj.com/doc/151077856.html,monDialog的基础上发展而来。
6.1.1Common pialog： CommonDialog是.NET中对话框组件的基础，它是System.Windows.Forms命名空间下的一个抽象类，在程序中不能直接使用。 CommonDialog公开了2个方法和一个属性，即：ShowDialog()/ShowDialog(IWin32Window)方法和Reset（）方法以及Tag属性。 ShowDialog是用于显示对话框。ShowDialog()有一个重载形式：ShowDialog（IWin32Window），IWin32Window在这里指一个窗口句柄，在调用中，这个参数应该被赋值成要显示的对话框的父窗体。 注意：句柄是Window中的一个常用词语，可以把它理解为一个标识符号，只是这个标识符号是一个数字。相应的窗口句柄就是窗口的标标识符。 Reset方法： 使用过程中可能改变初始值，当需要让所有的初值回到原来的状态时，调用Reset能达到目的。 Tag属性： Tag没有具体含义，它可以让用户在对话框控件中存储、维护自己的数据。这个数据由用户自己的代码解释。 对话框的返回值（ShowDialog的返回值）： 对话框通过调用ShowDialog()调用后，返回一个类型为DialogResult 值，其中DialogResult.OK指出用户成功完成了操作，成功选取了文
一、标签 标签能够显示多个字符构成的文本，用于设计表单上所需的文字性提示信息。标签和大多数控件的不同点在于运行表单时不能用《tab》键来选择标签。 常用的标签属性及其作用如下。 1、Caption：确定标签处显示的文本。 2、Visible：设置标签可见还是隐藏。 3、AutoSize：确定是否根据标签上显示文本的长度，自动调整标签大小。 4、BackStyle：确定标签是否透明。 5、WordWrap：确定标签上显示的文本能否换行。 6、FontSize：确定标签上显示文本所采用的字号。 7、FontName：确定标签上显示文本所采用的字体。 8、ForeColor：确定标签上显示的文本颜色。 二、命令按钮和命令按钮组 在各种窗口或对话框中几乎都要使用一个或多个命令按钮。一旦用户单击一个命令按钮，就可实现某种规定的操作。例如，各种对话框中的“确定”按钮，当用户单击时将结束对话框的操作。 VisualForPro中的命令按钮控件同样用于完成特定的操作。操作的代码通常放在命令按钮的“单击”事件（即Click Event）代码中。这样，运行表单时，当用户单击命令按钮时便会执行Click事件代码。如果在表单运行中，某个命令按钮获得了焦点（这时，这个命令按钮上会比其他命令按钮多一个线框），则当用户按下《Enter》键或空格键时，也会执行这个命令按钮的Click时间代码。 常用的命令按钮属性及其作用如下： 1、Caption：设置在按钮上显示的文本。 2、Default：在表单运行中，当命令按钮以外的某些控件（如文本框）获得焦点时，若 用户按下《Enter》键，将执行Default属性值为.T.的那个命令按钮的click事件代码。 3、Cancel：如果设置该属性值为.T.，则当用户按下
学生学籍管理详细设计 学号：____________ 姓名：____________ 班级：____________ 一、设计题目： 学生学籍管理 二：设计内容： 设计GUI学生学籍管理界面，用户可以加入学生信息，并对基本信息进行修改，添加，查询，删除。 三：设计要求： 进行简单的学生信息管理。 四：总体设计 (1)登陆界面的设计 （2）主窗体的设计 （3）添加学生信息窗体 （4）查询学生信息窗体 （5）修改学生信息窗体 （6）删除学生信息窗体 （7)事件响应的处理 五：具体设计 （1）程序结构的说明： A.入口程序：; B.登陆界面程序：; C.主窗体程序：; D.添加信息窗口程序：; E.修改信息窗口程序：;
F.查询信息窗口程序：; G.删除信息窗口程序：; H.程序数据连接：; (2)程序代码及分析说明 A.程序源代码（已提交） 是程序的入口。使登录窗口位于窗口中间，并且不可改变窗口大小。 是程序的登陆窗体。输入用户名和密码（用户名和密码在数据库的password表中）点击“进入系统”，然后登陆界面消失；出现要操作的界面（屏幕左上角）。是添加信息界面。添加基本信息后，点击“添加信息”按钮，将信息加入xinxi 表中。 是修改信息界面。输入要修改的学号或姓名（两者数其一或全部输入），并输入所有信息，点击“修改信息”按钮（如果数据库中不存在此学号，则弹出对话框“无此学生信息”），若有则修改。 是删除信息界面。输入要删除的学生的学号，点击“删除信息”按钮，弹出确认删除对话框，即可删除该生信息。 是查询信息界面。输入要查询的学生学号，点击“信息查询”按钮，在相应的文本区里显示查询的信息。 H:源代码 ; import .*; etScreenSize(); Dimension frameSize=(); if> { =; } if> { =; } ( (true); } public static void main(String[] args) { try{ ()); } catch(Exception e) { (); } new student(); } }
MFC对话框程序中的各组件常用方法： Static Text: 将ID号改成唯一的一个，如：IDC_XX，然后进一次类向导点确定产生这个ID，之后更改Caption属性： GetDlgItem(IDC_XX)->SetWindowText(L"dsgdhfgdffd"); 设置字体： CFont *pFont = new CFont; pFont->CreatePointFont(120,_T("华文行楷")); GetDlgItem(IDC_XX)->SetFont(pFont); Edit Control: 设置文本： SetDlgItemText(IDC_XX,L"iuewurebfdjf"); 获取所有输入： 建立类向导创建一个成员变量（假设是shuru1，shuru2……）类型选value，变量类型任选。 UpdateData(true); GetDlgItem(IDC_XX)->SetWindowText(shuru1); 第一句更新所有建立了变量的对话框组件，获取输入的值。第二句将前面的IDC_XX的静态文本内容改为shuru1输入的内容。 若类型选用control: 1.设置只读属性： shuru1.SetReadOnly(true); 2.判断edit中光标状态并得到选中内容(richedit同样适用) int nStart, nEnd; CString strTemp; shuru1.GetSel(nStart, nEnd); if(nStart == nEnd) { strTemp.Format(_T(" 光标在%d" ), nStart); AfxMessageBox(strTemp); } else { //得到edit选中的内容 shuru1.GetWindowText(strTemp); strTemp = strTemp.Mid(nStart,nEnd-nStart); AfxMessageBox(strTemp); } 其中nStart和nEnd分别表示光标的起始和终止位置，从0开始。strTemp.Format 方法用于格式化字符串。AfxMessageBox(strTemp)显示一个提示对话框，其内容是字符串strTemp。 strTemp = strTemp.Mid(nStart,nEnd-nStart)返回一个被截取的字符串，从nStart开始，长度为nEnd-nStart。如果nStart == nEnd说明没有选择文本。 注：SetSel(0,-1)表示全选;SetSel(-1,i)表示删除所选。
常用服务器端控件 Windows控件与Web服务器控件的主要区别： Windows控件的属性、方法、事件都是在本机上执行的； Web服务器控件的属性、方法、事件则全部是在服务器端执行的。 Web应用程序的执行方式： 在Web应用程序中，用户通过客户端浏览器操作Web页面时，对Web服务器控件的每个请求都要发送到服务器端，服务器进行处理后，再将处理结果转换为客户端脚本发送到客户端显示。 https://www.doczj.com/doc/151077856.html,控件的基本概念 1控件分类 （1）标准控件 （2）数据控件 （3）验证控件 （4）站点导航控件 （5）WebPart控件 （6）登陆控件 基本控件 1 标签控件 2 按钮控件 3 TextBox控件 4 CheckBox控件与CheckBoxList控件 5 RadioButton控件与RadioButtonList控件 6 ListBox控件和DropDownList控件 7 Table控件 8 HiddenField控件 5.1.1 按钮控件 VS2005中有以下三种类型的按钮控件： 1.Button控件：与Windows窗体的Button控件用法相同。 2.LinkButton控件：外观与Hyperlink控件相同，但在功能上与Button控件完全相同。 3.ImageButton控件：以图片形式显示的按钮。 三种类型的按钮在鼠标单击时都可以将窗体提交给服务器，并触发服务器端对应的Click事件，然后在服务器端执行相应的事件代码。 5.1.2 TextBox控件 1. 常用的基本属性 1) AutoPostBack属性：决定控件中文本修改后，是否自动回发到服务器。该属性默认值为false， 即修改文本后并不立即回发到服务器，而是等窗体被提交后一并处理。 2) TextMode属性：用于设置文本框接受文本的行为模式。共有三种属性值：MultiLine（多行输 入模式）；Password（密码输入模式）；SingleLine（单行输入模式）。默认情况下，该属性为 SingleLine。 2. 常用的事件 TextChanged事件：文本框的内容发生更改导致窗体回发服务器时触发。注意，是否触发该事件与AutoPostBack属性相关。
优秀课堂教学设计 课题：教表单控件选项按钮组 师：教材分马冬艳析：本节课是选自中等职业学校计算机技术专业的《数据库应用技术 VISUAL FOXPRO6.0 》中第六章表单设计中的第三节的内容。节课是在同学们 已经掌握了几种基本表单控件的基础上，进一步学习选项按扭组控件。重点：选项 按钮组的基本属性和特有属性难点：选项按钮组的应用能力目 1）标：通过了解选项按钮组的特性，并予以适当的启发，让学生能够利用此 2）控件具有创造性的设计出实用表单，培养学生的创造力。 3）知识目标：熟知选项按钮组的特性并熟练应用。情感目标：通过讨论增进同学们的感情交流和知识交流。由于书上对本节的内容实例较少且实例多是在以往例 题的基础上添加上此控因此控件属性突出不明显，为此我特地 专对此控件的属性设计了一道例题，不但能突出这个控件的特有属性，而（4） 且能极大的提高学生的学习兴趣，有利有的突出了重点问题，为解决难点课程重组：（5） 做好了铺垫。在精心设置例题的基础上增加了让学生自己根据控件属性设置问题的环节，不但能增加学生学习的兴趣而且有利于学习对本节课的内容进行深层次的思考，从而达到突破难点的目的。学生在学习本节课之前已经学习了一些控件，对于控件的学习已经有了一定学习经验，知道在学习控件的学习过程中应该注意哪些地方。但是由于控件学习的比较多，而且有很多相似的地方学生容易产生厌烦情绪，为了解决这个问题，要在引入此控件时设置好问题情境，引发学生学习兴趣，且鼓励学生进行大胆的学情分析：设想，培养同学们的创造思维能力。根据学生学习能力水平的不同在请同学们上前操作时，按照要操作的内容有选择性的挑选学生上来操作，在做简单操作时挑选那些平时操作不是很熟练且胆子比较小的同学，在培养他们胆量的同时通过完成一些简单操作激发他们的信心。对于那些较有难度且需要进行一不思考的问题，找一些底子比较好但是又不会很快把这个问题解决出来的同学来做，在他做的过程
https://www.doczj.com/doc/151077856.html,的两种编码方式是什么，什么是代码内嵌，什么是代码后置？Web页面的父类是谁？ 代码内嵌和代码后置。代码内嵌把业务逻辑编码和显示逻辑编码交叉使用。代码后置式业务逻辑代码和显示逻辑代码分开使用。system.web.ui.page 2.Web控件的AutoPostBack属性的作用是什么？ 控件的值改变后是否和服务器进行交互（自动回传） 3.验证服务器控件有哪些，他们有哪些常用的属性，ControlToValidate属性的作用是什么？有哪两种服务器控件？ RequiredFieldValidator：controltovalidate（验证的控件ID，共有的属性），text,ErrorMessage||||（dropdownlist控件验证时InitialValue属性是如果用户没有改变初始值，会验证失败）CompareValidator:controltocompare（要进行对比的控件），type（比较类型设置），operator(比较运算符，默认为等于)，ValueToCompare（进行比较的值） RangeValidator:type（验证类型（5种）），MaximumValue（最大值），MinimumValue（最小值）（包括上下限） RegularExpressionValidator:ValidationExpression（设置要匹配的正则表达式）ValidationSummary:showMessageBox(是否显示弹出的提示消息)，ShowSummary（是否显示报告内容） HTML服务器控件和web服务器控件 4.什么是Session，如何进行Session的读写操作，使用什么方法可以及时释放Session？Session 是用于保持状态的对象。Session 允许通过将对象存储在Web服务器的内存中在整个用户会话过程中保持任何对象。 通过键值对的方式进行读写；clear()和abandon()方法 5.运行https://www.doczj.com/doc/151077856.html,程序需要安装和配置什么，.NET Framework是不是必须要安装？ 安装IIS和.NET Framework 必须安装 https://www.doczj.com/doc/151077856.html,配置信息分别可以存储在什么文件中？ web.config文件和machine.config文件中 7.常用服务器控件，如Label、Button、TextBox、HyperLink、DropdownList的常用属性有哪些？label:text ,forecolor,visible Button:CommandName,CauseValidation, TextBox:AutopostBack,TextMode Hyperlink:NavigateUrl(单击Hyperlink时跳转的Url)，Text，Target（设置NavigateUrl属性的目标框架），ImageUrl（设置Hyperlink中显示图片文件的Url） Dropdownlist:AutoPostBack 8.XMLHttpRequest对象的常用属性和方法有哪些？ 方法是open（）和send() 属性：ReadyState和Status,ResponseText,ResponseXML,ResponseStream https://www.doczj.com/doc/151077856.html,中的常用的指令有哪些？谈谈这些指令的常用属性的作用？
什么时候用html控件，什么时候用"标准控件"? 能不用服务器端控件尽量不用 能用html控件就不要用web控件 服务器端控件效率低 前两句同意，至于后一句， 效率上，纯 html 肯定比 runat=server 低， 对于 runat=server ，事实上 https://www.doczj.com/doc/151077856.html, 内部帮我们作了许许多多的工作，比如 在 asp/php/jsp 中需要 来维护两次post之间的状态而
但，它带来的是，【开发效率成倍的提升，完整的组件编程模式....】 你不必再一堆的 Request.Form 中绕，你可以引用服务器控件对应，统一的编程模型 如 1. string txt = Requst.Form["MyTextBoxClientName"]; VS string txt2 = MyTextBoxServerID.Text; 2. // js document.form1.action = "?action=delete" // aspx.cs if(Requst.QueryString["action"] == "Delete") { // 执行删除操作 ... } VS // aspx VFP表单/控件常用属性、事件及方法英中对照 ——属性—— Name：表单或控件名 Caption：标题文字 AutoCenter：自动居中 AutoSize：自动大小 ForeColor：前景色 BackColor：背景色 Closable：可关闭 Movable：可移动 Width：宽度 Height：高度 Icon：图标 Visible：可见 Font*：字体、字号等 Enabled：能用 ButtonCount：命令按钮组、选项组控件中控件的个数 Buttons（1）：命令按钮组、选项组控件中第一个控件；Buttons（2）命令按钮组、选项组控件中第二个控件；…… value：表示组控件中选中的是第几个控件 或文本框中的内容 或列表框中选择的内容 等 PasswordChar：文本框用于输密码时显示的符号 ControlSource：和控件绑定的内存变量或字段SelStart：编辑框中选定内容的开始位置SelLength：编辑框中选定内容的长度SelText：编辑框中选定的内容 ListCount：列表框中可供选择的内容数 List（1）表示列表框中的第一项内容，List（2）表示列表框中的第二项内容，……RowSourceType：列表框中内容的给出方式RowSource：列表框中内容来自的字段名等MultiSelect：1或.t.时允许多项选择 Selected（1）为真，第一项被选；Selected（2）为真，第二项被选；……。 Text：下拉列表框中输入的内容Recordsource：表格控件绑定的表PageCount：页框中页面的个数 Pages（1）表示页框中的第一个页面，Pages （2）表示页框中的第二个页面，……ActivePage：页框中的活动页面号Increment：微调每次的变化量SpinnerHighValue：鼠标调整时的最大值SpinnerLowValue：鼠标调整时的最小值KeyboardHighValue：键盘输入时的最大值KeyboardLowValue：键盘输入时的最小值Value：微调的当前值 Picture：图像控件对应的图像 Stretch：图像的显示方式 Interval：计时器定时的时间间隔，单位毫秒 ——事件—— Load：装入事件 Init：初始化事件 Destroy：表单关闭前发生的事件Unload：表单关闭时发生的事件Click：单击事件 DblClick：双击事件 RightClick：右键事件 GotFocus：得到焦点事件 LostFocus：失去焦点事件 Timer：计时器指定的时间间隔到时发生 Error：执行对象事件代码出错时发生——方法—— Release：关闭表单Refresh：表单刷新Show：显示表单Hide：隐藏表单SetFocus：将焦点放到控件中 AddItem（内容项）：向列表框中增加数据项RemoveItem（位置）：从列表框中删数据项 实验四 https://www.doczj.com/doc/151077856.html,程序设计基础和常用控件 一、实验目的 本实验主要了解面向对象程序设计语言https://www.doczj.com/doc/151077856.html,基本语言元素包括集成开发环境、语言基础、基本控制结构、过程、常用控件和界面设计。通过本实验，读者将学会一些主要的面向对象的设计方法并可以利用https://www.doczj.com/doc/151077856.html,完成简单的应用程序开发。 二、实验环境 Microsofe Visual Studio .NET 2008 三、实验内容 1．设计一个Visual 的应用程序，窗体上有一个多行文本框和3个命令按钮，程序界面如图1所示。要求应用程序运行时，当单击窗体上【显示文本信息】按钮，文本框中显示红色文字“我喜欢https://www.doczj.com/doc/151077856.html,，因为它简单易学，使用方便。”当单击窗体上【改变背景色】按钮，文本框的背景色变为黄色。当单击窗体上【结束】按钮，程序结束。保存该应用程序。【实验步骤】： 1）创建工程：打开Visual Studio 后，点击左上角的新建项目，选中“模板”，展开选择Visual Basic，再选中Windows桌面，再在左边的类型中选择“Windows窗体应用程序”，在下方为此项目命名为“Win dowsApplication4.1” 2）先打开“工具箱”：展开左上角的“视图”，点击工具箱。 3)修改Form1的名称：右键选中From1,点击“属性”，在新弹出的属性菜单栏中，找到“Text”这个属性，将右边的“From1”改为“第一个https://www.doczj.com/doc/151077856.html,实验”即可。 4）设置一个普通文本框：在工具栏中，选中公共空间中的TextBox，然后拖入右边的设计窗口中，然后鼠标移到TextBox后，鼠标左键按住不放可以移动此控件。 5）调整文本框的大小：鼠标移动到文本框的左右边缘，鼠标箭头会变成一个左右的箭头， . C语言上机实践报告 专业：冶金工程 班级：冶金1102 姓名： 学号： 任课教师：丽华 时间：2012年8月 一、题目 学生信息管理系统设计 ●学生信息包括：学号，姓名，年龄，性别，出生年月，地址，，E-mail等。 ●试设计一学生信息管理系统，使之能提供以下功能： a)系统以菜单方式工作 b)学生信息录入功能（学生信息用文件保存）---输入 c)学生信息浏览功能---输出 d)查询、排序功能---算法 (1) 按学号查询 (2) 按姓名查询 e)学生信息的删除与修改（可选项） 一、系统功能模块结构图 二、数据结构设计及用法说明#include"stdio.h" #include"stdlib.h" #include"string.h" /*定义结构体用作创建链表*/ typedef struct z1 { char no[11]; //学生学号 char name[15]; //学生姓名 int age; //学生年龄 char sex; //学生性别 char birthday[8]; //学生出生年月char address[20]; //学生住址 char tel[12]; //学生联系 char e_mail[20]; //学生e-mail struct z1 *next; //指向下一链表}STUDENT; /*声明用户自定义函数*/ STUDENT *init(); STUDENT *create(); STUDENT *del(STUDENT *h); STUDENT *insert(STUDENT *h); STUDENT *revise(STUDENT *h); void print(STUDENT *h); void search1(STUDENT *h); void search2(STUDENT *h); void save(STUDENT *h); int menu_select(); void inputs(char *prompt,char *s,int count); /*主函数，用于选择功能*/ void main() { STUDENT *head; head=init(); //初始化链表表头 for(;;) { switch(menu_select()) { case 0:head=init();break; //初始化 case 1:head=create();break; //创建列表 WEB服务器控件 编写一个WEB控件至少要包含三个元素:ASP:XXX指明是哪一类控件,ID指明控件的标识符,Ruant 指明是在服务器端运行的。如: 表单控件常用属性、事件及方法英中对照
实验四VBNET程序设计基础和常用控件
学生信息管理系统程序
WEB服务器控件
MultiLine多行 Pasword密码输入 Columns 以字符为单位指明文本框的显示宽度 Rows 当TextMode为MultiLine时，指明文本框的行数 MaxLength 在单行文本方式下，文本框可以输入的字符数 Wrap 当TextMode为MultiLine时,是否自动换行，默认为TRUE ReadOnly 输入框为只读，默认为FALSE DataBind 将数据源绑定到被调用的服务器控件及其所有子控件上 TextChanged 当文本框内容发生变化时，触动。 文字控件案例一(5_3):制作登录界面 控件类型ID 属性设置说明 Label Label1 Text=用户名用于显示静态文本Label Label2 Text=密码用于显示静态文本Label LblMessage Text=””用于显示提示文本或登 录信息 TexBox TxtUserName TextMode=SingleLine 用于输入用户名TexBox TxtPassWord TextMode=Password 用于输入密码 Button BtnSumit Text=提交向服务器发送登录信息Button BtnRest Text=重置清除文本框内容 { TxtUserName.Text = ""; TxtPassWord.Text = ""; LblMessage.Text = ""; } protected void BtnSumit_Click(object sender, EventArgs e) { if ((TxtUserName.Text.Trim() != "") && (TxtPassWord.Text.Trim() != "")) { LblMessage.Text = "用户名:" + TxtUserName.Text + ":" + "密码" + TxtPassWord.Text; } else if (TxtUserName.Text.Trim() == "") { LblMessage.Text = "请输入用户名"; } else { LblMessage.Text = "请输入密码"; }
OA工作流的表单设计器中最常用控件的用法 如果想要设计制作精确、合理的OA工作流程，最基本的条件是设计出最合适的工作表单，而表单的制作最关键的是熟练掌握各个控件的使用方法。 下面就以最常用的几个控件跟大家分享一下它们在工作表单的制作过程中的用法。
控件类型及其用 第一，单行输入框。 单行输入框是最简单的空间，就是为表单添加一个可以输入内容的空，一般是用来填写比较简短的内容，比如：名字、手机号等。 ?如上图所示设置了单行输入框的属性后，就会在表单中出现下图所示的样式。 ?第二，多行输入框。 性质跟单行输入框类似，这个控件的内容也是完全由填写表单的用户手填。但多行输入框一般是用在输入内容较长的地方，比如一个较长的地址。
?如下图所示就是一个设置好的多行输入框在表单中显示的样式。 ?第三，下拉菜单。 这个很好理解，下拉菜单包含所有可能的选项。然后填写表单的用户可以通过下拉菜单选择需要的选项。
?第四，单选框。 单选框的含义我们都知道，就是设置多于一个的选项，而用户填写表单的时候只能从中选择一个选项。 ?比如下图所示的一个同意或不同意，只能选择其中一个选项。
?第五，多选框。 多选框的功能其实是只在表单中画一个可以打勾的小框，多选框有多少选项，就设置多少个多选框，然后在每个多选框后面自定义选项内容。 ?如下图所示就是一个多选框的样式，其中，火车、汽车、飞机和轮船这四个选项是在表单中定义的。 ?第六，列表控件。 这个列表控件其实是不经常用到的。起作用是相同格式记录的动态输入，可以根据实际需要灵活新增行数录入相应数据。 使用这个控件，是可以设置好列表头。列表控件支持多种输入类型，包括单行输入框、多行输入框、下来菜单、单选框、复选框和日期，满足多方面的需求； 而且支持自动计算和合计，使用通用运算符+、-、*、/、%等，可以实现列表项目的自动计算输入。其中列表计算项目是不可人工输入的。 如果用户在设计表单的时候确实用到了这个控件，可以设置上一两行试一下，看完表单效果后就知道该如何设置。
Label控件 功能说明：用于显示文本，提示信息，如窗体标题，文本框的标题 命名前缀：Lbl ASPX代码：