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Properties of the Intracluster Medium in an Ensemble of Nearby Galaxy Clusters

a r X i v :a s t r o -p h /9901281v 1 20 J a n 1999

To appear in The Astrophysical Journal:submitted Oct 2,’98;accepted Dec 15,’98

Preprint typeset using L A T E X style emulateapj v.04/03/99

PROPERTIES OF THE INTRACLUSTER MEDIUM IN AN ENSEMBLE OF NEARBY GALAXY

CLUSTERS

Joseph J.Mohr 1,2,3,4Benjamin Mathiesen 2&August E.Evrard 2

1Department of Astronomy and Astrophysics,University of Chicago,Chicago,IL 60637

2Department of Physics &3Department of Astronomy,University of Michigan,Ann Arbor,MI

48109

To appear in The Astrophysical Journal:submitted Oct 2,’98;accepted Dec 15,’98

ABSTRACT

We present a systematic analysis of the intracluster medium (ICM)in an X-ray ?ux limited sample of 45galaxy https://www.wendangku.net/doc/162477479.html,ing archival ROSAT PSPC data and published ICM temperatures,we present best ?t double and single βmodel pro?les,and extract ICM central densities and radial distributions.We use the data and an ensemble of numerical cluster simulations to quantify sources of uncertainty for all reported parameters.

We examine the ensemble properties within the context of models of structure formation and feed-back from galactic winds.We present best ?t ICM mass-temperature M ICM - T X relations for M ICM

calculated within r 500and 1h ?1

50Mpc.These relations exhibit small scatter (17%),providing evidence of regularity in large,X-ray ?ux limited cluster ensembles.Interestingly,the slope of the M ICM - T X relation (at limiting radius r 500)is steeper than the self-similar expectation by 4.3σ.We show that there is a mild dependence of ICM mass fraction f ICM on T X ;the clusters with ICM temperatures below 5keV have a mean ICM mass fraction f ICM =0.160±0.008which is signi?cantly lower than that of the hotter clusters f ICM =0.212±0.006(90%con?dence intervals).In apparent contradiction with previously published analyses,our large,X-ray ?ux limited cluster sample provides no evidence for a more extended radial ICM distribution in low T X clusters down to the sample limit of 2.4keV.

By analysing simulated clusters we ?nd that density variations enhance the cluster X-ray emission and cause M ICM and f ICM to be overestimated by ～12%.Additionally,we use the simulations to estimate an f ICM depletion factor at r 500.We use the bias corrected mean f ICM within the hotter cluster subsample as a lower limit on the cluster baryon fraction.In combination with nucleosynthesis constraints this measure provides a ?rm upper limit on the cosmological density parameter for clustered

matter ?M ≤(0.36±0.01)h ?1/2

50.

Subject headings:galaxies:clusters:general —intergalactic medium —cosmology

1.INTRODUCTION

The properties of galaxy cluster virial regions provide powerful constraints on models of structure formation and evolution.For example,evidence for continuing accretion in nearby clusters constrains the cosmological density pa-rameter ?0and the slope of the power spectrum of den-sity perturbations P (k )on cluster https://www.wendangku.net/doc/162477479.html,bining the baryon fraction within cluster virial regions with nucle-osynthesis constraints on the baryon to photon ratio pro-vides an estimate of the cosmological density parameter for clustered matter ?M .An emerging theoretical con-sensus that dissipationless collapse produces haloes which conform to a “universal”density pro?le (Navarro,Frenk,&White 1997)implies that measurements of cluster virial structure provide constraints on the nature of dark mat-ter.Therefore,systematic,high precision analyses of large,well de?ned cluster samples can potentially enjoy broad and lasting impact.

There are several tools available to probe galaxy cluster virial regions.One can use gravitational lensing to study the characteristics of the clustered mass directly.At the present time,typical cluster weak lensing maps have peak signal to noise of ～10even at disappointingly low angular the NFW prediction.Studies of strong lensing can provide extremely detailed constraints on the structure of cluster cores (Tyson,Kochanski &Dell’Antonio 1998),but the required alignments of lens,source and observer occur in few clusters.Dynamical studies using galaxies can poten-tially resolve the dark matter structure and galactic orbits simultaneously,but doing so requires such a large galaxy sample that data from multiple clusters must be rescaled and stacked (e.g.Carlberg et al 1997),precluding tests of anything except average properties of cluster mass pro?les.Archival ROSAT PSPC X-ray images of nearby galaxy clusters are of su?cient quality that high precision studies of the ICM in individual clusters are possible.Moreover,it is straightforward to study a large,well de?ned clus-ter ensemble,making it possible to compare the structure of high and low mass clusters and to quantify departures from self similarity.

Here we present a precision analysis of the intraclus-ter medium in 45nearby clusters.Our goals include (i)a study of cluster regularity,(ii)a quantitative characteriza-tion of the ICM mass-temperature (M ICM - T X )relation for comparison to future structure formation and galaxy formation simulations,(iii)a de?nitive test of whether the ICM mass fraction f and radial distribution vary with

leading to an upper limit on the cosmological density pa-rameter?M.

Our technique is a synthesis of pro?le?tting methods applied to elliptical galaxies(Saglia et al.1993,Mohr& Wegner1997)and procedures applied to examine the ICM in smaller cluster samples(David et al.1993,David,Jones &Forman1995).The primary observational data required to study each cluster are(i)the X-ray surface brightness pro?le,which constrains the radial distribution of the ICM along with(ii)the X-ray luminosity within some aperture and(iii)the emission weighted mean ICM temperature T X ,which together constrain the ICM central density. We demonstrate that our ICM constraints are insensitive to temperature variations within the cluster.Departing from some other published analyses,we do not assume a particular form for the dark matter distribution,because it is not necessary to do so when studying the ICM;however, we do assume a spherically symmetric ICM distribution. We test our technique on an ensemble of hydrodynami-cal cluster simulations and quantify the e?ects of present epoch mergers and asphericity on our results.

We describe the data and numerical simulations in§2, detail the analysis in§3,and then present the results in §4.Section5contains a discussion of the results.We use H0=50km/s/Mpc and q0=1

Γ=0.24),and (4)LCDM (?0=0.3,λ0=0.7,σ8=1.0,h 50=1.6,Γ=0.24).Here σ8is the power spectrum normalization on 8h ?1Mpc scales;initial conditions are Gaussian random ?elds

consistent with a CDM transfer function with the speci?ed Γ(Davis et al.1985).Within each of these models,we use two 1283N–body only simula-tions of cubic regions with scale ～400Mpc to determine sites of cluster formation.Within these initial runs the virial regions of clusters with Coma–like masses of 1015M ⊙contain ～103particles.

Using the N–body results for each model,we choose clusters for additional study.We zoom in on these clusters,resimulating them at higher resolution with gas dynamics and gravity on a 643N–body grid.The large wavelength modes of the initial density ?eld are sampled from the ini-tial conditions of the large scale N–body simulations,and power on smaller scales is sampled from the appropriate CDM power spectrum.The simulation scheme is P3MSPH (Evrard 1988),the baryon density is a ?xed fraction of the total ?b =0.2?0,and radiative cooling is ignored.

Simulating individual clusters requires two steps:(1)an initial,323,purely N–body simulation to identify which portions of the initial density ?eld lie within the cluster virial region at the present epoch,and (2)a ?nal,ef-fectively 643,three species,hydrodynamical simulation.In the ?nal simulation the portion of the initial density ?eld which ends up within the cluster virial region by the present epoch is represented using dark matter and gas particles of equal number,while the portions of the initial density ?eld that do not end up within the cluster virial region by the present epoch are represented using a third,collisionless,high mass,species.The high mass species is 8times more massive than the dark matter particles in the central,high resolution region.This approach allows us to include the tidal e?ects of the surrounding large scale structure and the gas dynamics of the cluster virial region with simulations that take only a few days of CPU time on a low end UltraSparc.The scale of the simulated re-gion surrounding each cluster is in the range 50–100Mpc,

and varies as M 1/3

halo ,where M halo is approximately the mass enclosed within the present epoch turn around ra-dius.Thus,the 48simulated clusters in our ?nal sample have similar fractional mass resolution;the spatial reso-lution varies from 125–250kpc.The masses of the ?nal cluster sample vary by an order of magnitude.We create X–ray images and temperature maps for further analysis following procedures described in Evrard (1990).

3.ANALYSIS

In this section,we detail our data reduction procedures,placing special emphasis on the determination of statistical uncertainties and systematic errors in the derived cluster ICM gas masses.

3.1.Modeling the X-ray Surface Brightness Pro?le of the form

I (R )=I 0

(1)

where R c is the core radius and r ?3βis the asymptotic radial fall-o?of the underlying ICM density distribution.Because the PSPC photon detection rate for a parcel of gas with constant emission measure varies only moder-ately with temperature above 1.5keV (see Fig. 2.1),the distribution of X–ray photons in PSPC images directly constrains the ICM density distribution regardless of the temperature structure (see similar discussion of the Ein-stein IPC:Fabricant,Lecar &Gorenstein 1980).Fitting the βmodel provides a systematic approach to studying the ICM properties.In the case where cluster X–ray emis-sion is well described by βmodels —even ?attened β–models with axial ratios within the observed range (Mohr et al.1995)—?tting to azimuthally averaged surface brightness pro?les recovers unbiased estimators of βand R c as long as the point source response function (PSF)of the imager is properly treated.

Fig.2.—In the upper panel we plot the observed surface bright-ness pro?le in A 1795(points with error bars),the best ?t single βmodel (dashed line),and the best ?t double βmodel (solid line).The lower panel contains residuals around the single β(solid)and the double β(open)?ts.In clusters with central emission excesses,a single β?t produces an arti?cially small core radius and β;adding the second βcomponent results in an improved ?t to the emission excess and the outer pro?le.

Unfortunately,approximately half of nearby galaxy clus-ters exhibit X–ray emission which is not azimuthally symmetric (Mohr,Fabricant &Geller 1993,Mohr et al.1995).These features include large centroid variations (e.g.A 754)and bimodality (e.g.A 85)which violate the assumptions underlying Eqn.1.Nevertheless,we ?nd that the βmodel describes azimuthally averaged cluster emis-

exhibit X–ray morphologies similar to those of our PSPC cluster sample.

The presence of cooling?ows presents another potential problem to our choice of model.Signi?cant central emis-sion excesses associated with cooling instabilities are found in18of the clusters in our sample;they can greatly bias the model parameters.Clusters for which cooling?ows present a problem to?tting are easily recognized by two criteria.First,the cluster must display nonrandom be-haviour in the?t residuals consistent with a central emis-sion excess.Second,the cluster must appear“relaxed”, lacking obvious asphericity or substructure which would indicate a recent merger.Figure3.1shows an example of these criteria in Abell1795,a symmetric cluster which dis-plays a signi?cant central emission excess.The emission excess biases our best-?tβmodel(the dashed line)to-wards a small core radius and shallow pro?le,resulting in poor agreement between?t and data in the region outside the excess.

In such cases we model the emission excess with a sec-ondβmodel and simply?t the cluster surface brightness pro?le to the sum of the two:

I D(R)=

i=1I i 1+ R2.(2)

We constrain both components of this function to have the sameβ,and we determine the distribution of the un-derlying ICM numerically.This method essentially decou-ples the inner and outer regions of the cluster,allowing the?t to?nd the transition between excess and primary emission on its own.An example of a two-component?t is also shown in Figure3.1(solid line);the extra degrees of freedom are e?ective in removing nonrandom trends from the residuals.In the process of formulating our method, we also considered removing the emission excess and?t-ting a single componentβmodel to the remainder of the image.We found that?tting under these conditions pro-duces biased parameter estimates in cases where the emis-sion model is a perfectβmodel;speci?cally,the best?t core radius is correlated with the size of the excised re-gion.These systematics and associated signal to noise reductions led us to abandon this approach.The double βmodel approach is similar in spirit to this method and usually gives similar results,but is much more objective.

3.2.Calculating Radial Pro?les

Producing a background subtracted cluster surface brightness pro?le appropriate for?tting requires several steps.First,we choose an emission center using a circular aperture of radius10pixels.We adjust the position of this aperture to minimize the di?erence between the geometric center and the centroid of the X-ray photon distribution which lies within the aperture.This approach converges even in cases where the cluster emission is signi?cantly skew(Mohr,Fabricant&Geller1993).

Second,we evaluate the background level in the image by examining annuli at large radii where the cluster surface for each annulus is the mean of the clipped distribution of the～2×104pixels within the annulus.The clipping algo-rithm excludes all pixels that are more than2σi brighter than the median value,whereσi is the uncertainty asso-ciated with pixel i.The clipping algorithm is iterated10 times,with upper and lower sigma clipping during the last two iterations.Tests on simulated PSPC images indicate that the mean value is an unbiased estimator of the image background.

This method allows us to detect clusters whose emission contaminates the entire image,by seeing whether the back-ground values steadily decrease with increasing radius.It also guards against the possibility of localized excess emis-sion contaminating our result.In cases where the neigh-boring clusters contaminate the image background region in one direction(e.g.A3558),we use half rather than full annuli.In the end,we measure the background in the an-nulus whose background value lies closest to the mean of the?ve measurements;in cases where detectable cluster emission essentially?lls the PSPC FOV,we use the back-ground value in the outermost annulus.We use the scatter in the?ve background measurements as an indicator of the true background uncertainty.

Lastly,we calculate the radial pro?le.Individual pixels are treated separately within1.5pixels of the image cen-troid;outside this region the pro?le values are averages over all pixels whose centers lie within an annulus of1 pixel width.The pro?le is truncated at the radiusθmax where the signal to noise falls below a critical value;specif-ically,the pro?le is terminated when the average signal to noise of the last few pro?le points falls below～3.A few clusters are treated di?erently because of contamination by neighboring clusters;for example,Abell401is?t out to a radius halfway between it and Abell399.

3.3.FittingβModels

We?nd the best estimates of the parameters I0,β,and R c by minimizing theχ2di?erence between the PSF con-volved model and the observed surface brightness pro?le. We minimizeχ2using the downhill simplex method(Press et al.1992).In correcting for the e?ects of the PSF and ?nite pixel scale we employ the techniques used in stud-ies of the fundamental plane of elliptical galaxies(Saglia et al.1993,Mohr&Wegner1997).The PSF convolved mean surface brightness I c within an annulus of radius R and width2dR is

I c(R)=

[F(R+dR)?F(R?dR)]

5 sources created with the PROS task rosprf at1.0keV(see

Fig. 3.3);these parameters are appropriate for the cen-

tral part of the PSPC?eld,consistent with the position of

cluster cores in this

dataset.

Fig.3.—The radial pro?le using PROS)and

?t(solid line;see Eqn.5)of a1keV point source located in the

center of the ROSAT PSPC?eld.The divergence of the model and

the true PSF at radii greater than60′′does not bias estimates ofβ

model parameters.

The Fourier transform of the one componentβmodel

I(R)is

?I(k)=2πI

R2c ∞0dλλJ0(kR cλ)2(6)

where J0is the Bessel function.This function?I(k)is not

analytic for allβ,but note that the integral is only a func-

tion ofβand the combination kR c.Our approach is to

calculate?I(k)over a?nely sampled grid inβand kR c and

then interpolate on this grid when evaluating the PSF con-

volved surface brightness I c(R)(Eqn.3).We test the ac-

curacy of this grid by comparing the inverse transform of

?I(k)directly with I(R).The Fourier transform of the two

componentβmodel is simply the sum of the transforms of

the individual components.Naturally,the PSF corrected

surface brightness I c(R)approaches the uncorrected pro-

?le I(R)at radii which are several times the PSF scale,

so at large radii we use the unconvolved surface brightness

I(R)(Eqn.1)directly.

3.4.Calculating ICM Masses

We constrain the ICM central density using the cluster

X–ray luminosity measured within an annulus(e.g.David

et al.1990,Mohr et al.1996).Speci?cally,the luminosity

within an annulus de?ned by a minimum radius R?and

a maximum radius R+is

L X(R?,R+)=4π ∞dz R+n e(r)n H(r)Λ(T)RdR(7)

r2=R2+z2.The integral along z can be truncated

at the cluster“boundary”;tests demonstrate that for ob-

served values ofβthis integral is not sensitive to its upper

https://www.wendangku.net/doc/162477479.html,ing n e(r)=ρ(r)/μe m p and n H(r)=ρ(r)/μH m p

the ICM central density is

ρ0= L X(R?,R+)μeμH m2p(1?3β)2(8)

where F(R?,R+)is the dimensionless integral

F(R?,R+)= ∞0ds 1+s2+(R+/R c)2 1?3β(9)

? 1+s2+(R?/R c)2 1?3β ,

and we have assumed that the ICM is isothermal with

temperature T X .For the results presented here we mea-

sure the luminosity over the same region we?t the X-ray

surface brightness pro?le(R?=0and R+=θmax

Fig.4.—The correlated errors in?t parametersβand R c are ob-

vious in thisχ2νcontour plot.The best?t value for A2255is marked

with a star,the contours deliniate2σand3σcon?dence regions,and

the dotted and dashed lines correspond to90%con?dence intervals

determined by Monte Carlo runs.The uncertainties presented with

tabulated results are derived from Monte Carlo simulations.

In determiningρ0we useμe=1.167andμH=1.400,

appropriate for a fully ionized plasma with30%solar abun-

dances(Feldman1992).The ICM mass within some lim-

iting radius is then a straightforward integral over theβ

model density distribution using the measured ICM cen-

tral density(Eqn.8).

The density distribution consistent with a two compo-

nentβmodel is not analytic in general.We express the

density distribution in the two component case as a ra-

dially dependendent multiple f(r)of the broad,primary

component:ρD(r)=f(r)ρ1(r);we solve for f(r)using the

surface brightness pro?les of the two component?t I D(R)

Using this equation,we work our way inward solving for f (r )at equal intervals in r .The central density in this case is ρ0=f (r )ρ1(r )|r =0.In our sample the central value of the multiple f (r =0)ranges from 2.5(Ophiuchus)to 33(A 2204)and averages ～10.

3.5.Measurement Uncertainties

A uniform,systematic analysis of the ICM properties in this well de?ned cluster sample requires careful quan-ti?cation of all known sources of uncertainty.We identify multiple sources of uncertainty in the measured properties of the ICM,including Poisson noise,choice of cluster emis-sion center,PSF blurring,X–ray background subtraction,ICM temperature uncertainties,luminosity uncertainties,and T X <1.5keV gas associated with central cooling instabilities.Below we discuss each in turn,and describe how they are incorporated into the ?nal uncertainties for the βmodel parameters and ICM masses M ICM .We also quantify the impact of each error contribution on the de-rived ICM mass,to give some indication of their relative importance.

?Poisson noise :We evaluate the Poisson contribu-tion to the uncertainties using a Monte Carlo ap-proach.Speci?cally,we create and ?t 500indepen-dent realizations of each cluster image.The template for these arti?cial images is a Gaussian smoothed (σ=30′′)version of the original cluster image.Each pixel in an arti?cial image contains a value Poisson distributed around the mean of the pixel value in the template image.When ?tting βmodels to the arti?cial images,we adjust the PSF parameters to account for the degraded resolution in the smoothed template.This approach allows us to account for the deviations of many clusters from idealized βmodels.We use this ensemble of 500?ts for each cluster to quantify the Poisson contribution to measurement uncertainties.The typical Poisson contribution to the fractional uncertainty in M ICM is 1.1%(sample median),and the largest is 3.9%in A 1689.

The ?t parameters R c and βare strongly correlated.In Fig. 3.4we present 2σand 3σχ2νcontours in the space of R c and βfor the single component ?t to the cluster A 2255.Rather than present these plots for each cluster we note the upper and lower 90%con?dence intervals on the best ?t parameters.Dashed lines indicate the locations of these con?-dence boundaries in Fig.3.4.

?Emission center :We use a circular aperture with 150′′radius to de?ne the cluster emission center (see §3.2)before calculating the radial pro?le.In clus-ters with azimuthally symmetric emission,the emis-sion center is independent of the aperture radius.In clusters that exhibit centroid variations this is not the case.Calculating the pro?le around di?erent emission centers can a?ect core radii,β’s and ICM masses.We evaluate these e?ects by re-analyzing the cluster images using a centering aperture three indicates recent major mergers,and,for the most part,they can be identi?ed by the high χ2νin the ?t results.We use the di?erence in ICM parameters in the two di?erent analyses as an estimate of the scale of this uncertainty.The ratio of M ICM mea-surements with the two centering apertures has a mean value of 0.997and a standard deviation of 2.5%calculated over the whole sample;the largest out-lier is A 754,a well studied recent merger candidate (e.g.Fabricant et al.1986,Henriksen &Markevitch 1996,Roettiger,Stone &Mushotzky 1998),where M ICM changes by 16%.

?PSF correction :Without a PSF correction,core radii and β’s in small core radius clusters would be systematically overestimated.By ?tting PSF cor-rected βmodels to observed radial pro?les,we re-move this bias from our βparameters and ICM masses.We ignore any residual errors associated with the minor di?erences between the true PSPC PSF and our best ?t model (see Fig.3.3).

?X–ray background :Errors in the background sub-traction can systematically bias βparameters and X-ray luminosities.We use the variation in the mea-sured background within ?ve concentric annuli as a measure of the true background uncertainty σXB (see primary discussion in §3.1).We quantify the e?ects of the background uncertainty on measured ICM parameters by re?tting the image with the background ?xed to the preferred value perturbed by ±1σXB and ±2σXB .For each cluster we use the ?ve ?ts to measure a maximal deviation in the parameters of interest (corresponding to a ～4σXB variation in the X-ray background);we use half this maximal di?erence as an estimate of the X-ray back-ground induced uncertainty.In our ensemble,the RMS maximal deviation in M ICM is 3.6%,and the largest change is 14%in MKW 3S.

?ICM T X measurements :Typical measurement un-certainties in T X translate into negligible uncer-tainties in the ICM parameters,but make important contributions to uncertainties in binding mass esti-mates.Eqn 8shows that ρ0∝

7 1h?150Mpc aperture is una?ected by T X uncertain-

ties.

?Luminosity measurements:We measure cluster

aperture luminosities using the PSPC images.With

Eqn.8and the uncertainty in the observed cluster

count rate,we calculate the contribution to uncer-

tainties in the ICM parameters.The typical contri-

bution to M ICM uncertainties is0.4%(sample me-

dian)and the largest contribution is1%in A2244.

We do not account for any errors in the PSPC e?ec-

tive area tables;upcoming observations with AXAF

will provide an excellent test of these data.

?Core ICM with T X<1.5keV:The PSPC count rate

for a parcel of gas with constant emission measure

is relatively insensitive to T X for T X>1.5keV(see

Fig. 2.1);therefore,our assumption of isothermal-

ity when we calculate the underlying density pro?le

consistent with the X-ray surface brightness pro?le

does not a?ect our results.However,ICM tempera-

tures within the cores of clusters which exhibit strong

emission excesses or cooling?ows can be less than

1.5keV.In clusters where this is the case the ICM in

the cores is essentially overrepresented with photons

in the cluster X-ray image.Speci?cally,the PSPC

count rate for a1keV(0.75keV)gas clump is～1.4

(～2)times higher than the count rate for a5keV

gas clump.

In the absence of detailed ICM temperature pro?les,

we estimate the e?ects of these cool cores empiri-

cally.We use the clusters with two componentβ

model?ts to estimate the nature of this e?ect on

M ICM measurements.Speci?cally,we assume that

the secondary component of the?t is responding to

ICM at a temperature of0.75keV;thus,we scale the

best?t I2by1

ρ2 ?1

whereρis the local ICM density;because the ICM emis-

These density?uctuations are presumably residual structure in the ICM from recent mergers;higher resolu-tion simulations would likely yield even higher?uctuation amplitudes.We are carrying out a detailed analysis of the ICM structure in our simulated ensemble to better charac-terize the source of these density?uctuations(Mathiesen, Evrard&Mohr in prep).In real clusters this mechanism and additional physics like magnetic?elds and radiative cooling instabilities(all explicitly ignored in these simu-lations)could all contribute to ICM density?uctuations (and overestimates of M ICM).Quantifying the M ICM measurement biases in real clusters requires detailed anal-ysis of high resolution,spatially resolved,X-ray spectra of the ICM emission or comparison of M ICM measurements from X-ray data and observations of the ICM’s Sunyaev-Zeldovich e?ect on the cosmic microwave background.

In summary,our ensemble of hydrodynamical cluster simulations indicates that(i)our analysis technique is likely to overestimate M ICM,and(ii)(after accounting for this bias)high signal to noise X-ray images of a coeval cluster population allow the determination of M ICM with

a typical accuracy of～9%.

4.RESULTS

Below we present results of an analysis of our X-ray?ux limited sample of45galaxy clusters.First we describe our analysis of the cluster X-ray surface brightness pro-?les.Second,we present our constraints on ICM masses M ICM and mass fractions f ICM,and third,we constrain systematic variations in the radial distribution of the ICM. Finally,we compare our results to previously published analyses.

4.1.X-ray Surface Brightness Pro?les

Best?t parameters for the pro?les appear in Table2. Columns contain the cluster name,the central surface brightness and core radius of the primaryβmodel compo-nent,the central surface brightness and core radius of the secondary component(if there is one),the best?tβ,the reducedχ2ν,the number of constraints in the?t,and the truncation radius of the?tθm(we also measure our aper-ture luminosities withinθm).Central surface brightnesses are the unabsorbed values in ergs/s/cm2/??′for the0.5-2.0keV band.Core radii are in h?150Mpc(with q0=0.5), and the truncation radiiθm are in arcminutes.Uncertain-ties are90%con?dence intervals with the other?t param-eters unconstrained.

The X-ray surface brightness pro?le of each cluster ap-pears in Fig. 6.Each page contains a two by three ar-rangement of cluster panels.The clusters are arranged by T X ,which facilitates the?xed radial range for each group of six.Cluster names appear in the upper right cor-ner of each panel.Surface brightness(in ergs/s/cm2/??′in the0.5-2.0keV band)is plotted versus radius in h?150Mpc. We plot measurements(points with error bars),best?t pro?le(solid line),and best?t pro?le without the PSF correction(dashed line)for each cluster.The best?t pa-rameters appear below the cluster name.The?t residuals pear at factors of2.5in surface brightness,and the heavy contour always corresponds to4×10?14ergs/s/cm2/??′. We?t two componentβmodels to18clusters in our sample;these clusters exhibit trends in the residuals of their?ts to single componentβmodels.Some remain-ing clusters with single componentβ?ts exhibit trends, but these clusters also exhibit large centroid variations or other evidence of recent mergers,and so we do not at-tempt a double component?t:(A754,A1367,A2319, A3266,A3558,A3667).Two clusters with double com-ponent?ts also exhibit obvious evidence of recent mergers: A2142and A85;in the case of A85we remove the small, secondary surface brightness peak to the south before cal-culating a radial pro?le.

It is interesting to note that29clusters in our sample are not formally well?t by either a one or two compo-nentβmodel(less than1%chance of consistency between model and data).This is most likely another indicator of the prevalence of cluster X-ray substructure(Mohr et al.1995).However,in all but a few clusters the best?t βmodel reproduces the general character of the surface brightness pro?le(no systematic trends in the residuals), and,as demonstrated by our analysis of the cluster simu-lations,this is all that is required to constrain the average behavior of the density pro?le and calculate M ICM with a typical accuracy of～9%.(Note that parameter uncer-tainties are determined using Monte Carlo simulations and not?χ2.)

Note that the highχ2ν?tting problem for galaxy clusters is similar to that of elliptical galaxies.Elliptical galaxies are reasonably well described by bulge plus disk models, but very rarely are the measured galaxy pro?les and mod-els consistent in a formalχ2sense(Saglia et al.1997);in-terestingly,the photometric parameters derived from those ?ts exhibit the fascinating regularity relation termed the Fundamental Plane.

4.2.ICM Properties

We use the?t to the X-ray surface brightness pro?le of each cluster to constrain the ICM central density,radial distribution,and total mass within several limiting radii r lim.We use r lim=1h?150Mpc and r lim=r500,the radius within which the mean density is500times the critical densityρcrit=3H20/8πG.By using r500we are able to study the same portion of the virial region in each cluster;

a drawback is that one requires a model of the potential to calculate r500,and so we also present measurements within a?xed metric radius to avoid this uncertainty.We calculate the radius r500and enclosed binding mass M500 two di?erent ways:(i)using virial scaling relations and (ii)using the isothermalβmodel.The virial scaling rela-tion is motivated by the simple assumptions that clusters have recently formed and are approximately virialized and self-similar;in this case r500∝T1/2and M500∝T3/2. Our simulated cluster ensemble exhibits scaling relations consistent with these assumptions,in agreement with pre-vious work(Evrard,Metzler&Navarro1996,Schindler 1996).On the other hand,estimates of M500and r500with

Fig.6h.—Ophiucus,A 1689,Triangulum Australis;Cluster X–ray surface brightness pro?les:For each cluster the radially averaged,unabsorbed surface brightness in units of ergs/s/cm 2/??′in the 0.5:2.0keV band is plotted versus distance from the cluster emission center [in h ?1

50Mpc].The surface brightness measurements (points with error bars),the underlying model (dashed line)and the PSF corrected model (solid line)are plotted.Residuals between the ?t and the data (scaled by the data uncertainty)appear below each radial pro?le.A contour map of the portion of the cluster image used to calculate I (R )appears in the lower left corner;the images are smoothed to facilitate contouring.The emission center is marked with a star;contours appear at factors of 2.5in surface brightness,and the heavy contour corresponds to a ?ducial surface brightness of 4.0×10?14ergs/s/cm 2/??′in each cluster.The cluster name and best ?t parameters appear in the upper right hand corner of each plot.Both sets of parameters are listed for those clusters ?t with a two component βmodel.

A 1795.

r 500=2.37h ?1

50Mpc

T X

10KeV

3/2

If our βmodel mass for A 1795is in error,we will still

Table 3correspond to cluster name,ICM central den-sity ρ0,central electron number density n e ,and the ra-dial location r /r 500of a typical ICM particle located within r 500.Table 4contains the ICM masses M ICM and mass fractions f ICM .The columns correspond to cluster name;M ICM and f ICM calculated within a limiting ra-dius r lim =1h ?1

50Mpc;r 500,M ICM and f ICM calculated using the virial relation;and r 500,M ICM and f ICM cal-culated using the isothermal βmodel.All uncertainties

17 Fig.

7contains a plot of M ICM and f ICM,calculated

within a limiting radius r500,versus emission weighted

mean ICM temperature T X .The left panel contains re-

sults obtained using the isothermalβmodel to estimate

binding masses and r500,and the right panel contains re-

sults using the virial scaling relation(Eqn.11).As noted

above,measurements within r500sample the same por-

tion of each cluster’s virial region,whereas measurements

within a?xed metric radius are directly comparable to

results from previous analyses and are more nearly inde-

pendent of assumptions about the cluster potential.

Within r500the best?t relation between M ICM and

T X is

M ICM=(1.49±0.09)×1014M⊙T1.98±0.18

(12)

where T6is T X in units of6keV.The variance weighted

RMS scatter in M ICM around this relation is17%;in

calculating the variance weighted RMS,each cluster is

weighted using its M ICM measurement uncertainty.We

?nd the best?t relation by minimizingχ2,the sum of

the squared,orthogonal deviationsν2i of each point;νi is

the minimum distance between point i and the?t scaled

by one overσi,the point i uncertainty along that vector.

We?t using only those42clusters with well determined

temperature uncertainties,and we determine the90%un-

certainties on the?t parameters using500bootstrap?ts.

The17%scatter about this relation is another indica-

tor of the regularity of galaxy clusters;the scatter is con-

sistent with constraints derived from analysis of scatter

in the Size-Temperature relation in an X-ray?ux limited

cluster sample(Mohr&Evrard1997)and the luminosity-

temperature relation in a cooling-?ow free cluster sample

(Arnaud&Evrard1998).The slope of this relation is steep;it is statistically inconsistent with a slope of3

large M ICM o?sets like those observed.Alternatively,one might suggest that we are seeing the results of the ICM “?owing”into the cluster core;the scale of the mass o?-set requires an average ?ow rate of ～2000M ⊙/yr over the age of the universe,which makes this explanation un-likely.On the other hand,the higher M ICM in clusters with shorter central cooling times may be an artifact;cen-tral cooling instabilities may enable the formation of a multiphase medium and signi?cant ICM density and tem-perature variations rather than the smooth density distri-bution and isothermal ICM implicit in the βmodel anal-ysis.These density variations enhance the X-ray emissiv-ity,causing an increase in the X-ray luminosity and a bias in our estimates of

the

central

density ρ0(see Eqn.8).Quantitatively the o?sets in M ICM could be explained by a multiphase medium in short central cooling time clusters if that medium enhanced the X-ray luminosity by ～50%on average.

4.2.2.ICM Radial Distribution

We also probe for systematic di?erences in the radial dis-tribution of the ICM in high and low T X clusters.The presence of di?erences could be an indication that galac-tic winds have contributed signi?cant energy to the ICM (White 1991,Metzler &Evrard 1994,Metzler &Evrard 1998).In past studies,discussion has focused on the value ?3β,the asymptotic slope of the ICM;we take a new approach which incorporates the entire ICM distribution within the radius r 500.Speci?cally,we calculate the aver-age radial location r /r 500of an ICM particle within the same physical region of each cluster,where

r = r 5000

d 3

r rρ(r )

log(I i /I o )

;(17)

operationally,we choose an area A o ,?nd the isophote I o which encloses that area,and then ?nd the isophote I i which encloses the area A i =0.7A o .This approach yields the e?ective ICM fallo??3βeff over a particular annulus determined by A o .Fig. 3.1(upper panel)con-tains a plot of βeff measured at r 5000versus T X for the same sample;the data provide no indication that lower T X clusters have a more extended ICM.Because βeff changes with radius in a cluster (small in the core and steepening with radius)it is important that it be mea-sured in the same physical region in each cluster (rather than at a particular isophote;see Mohr &Evrard 1997).The results displayed here are calculated at a radius r =

0.75h ?1

50( T X /10keV)Mpc,corresponding to r 5000and A o =πr 25000,a radius at which βeff can be measured in each cluster image.

https://www.wendangku.net/doc/162477479.html,parison to Previous Results

We compare our M ICM and f ICM measurements to pre-viously published results.Rather than present detailed notes on each cluster,we focus on two trends apparent from the comparisons.First,our measurements in the clusters ?t with single βmodels are generally consistent with previously published results.Second,our measure-ments on clusters with central emission excesses tend to produce lower ICM masses M ICM than previously pub-lished results.In individual cases of disagreement in βparameters,we invite the reader to examine the X-ray surface brightness pro?les and contour plots (Fig.6)as a way of gauging the plausibility of our ?t (Allen et al.1996,Bardelli et al.1996,Breen et al.1994,Briel et al.1992,Briel &Henry 1996,Buote &Canizares 1996,Cir-imele,Nesci &Trevese 1997,David,Jones &Forman 1995,Dell’Antonio,Geller &Fabricant 1995,Fabricant et

Fig.9.—We plot the typical location of a gas particle within r500 (below)andβef f,a nonparametric estimate of the radial fallo?of the ICM calculated at～r5000(above).As before,symbols denote di?erent central cooling times.Neither dataset provides evidence for a more extended ICM in low T X clusters(see discussion in §4.2.2).

We have examined the issue of emission excesses in great detail;we analyze all clusters with central emission excesses using a single and double componentβmodel. In the median,the doubleβmodel analysis provides an M ICM which is7%lower than the singleβmodel analysis (maximum reduction is20%in A3526).This results be-cause the emission excess biases the parameters R c andβlow in the single component?ts.The works which re-ported higher ICM masses generally used a single-beta model approach and ignored the central emission excesses; in these cases we were able to demonstrate consistency by showing that we could match the author’s result using a single-beta model ourselves.A few authors took the ap-

above.Three of these(Durret et al1994,Myers Fukumoto&Ikeuchi1992)employ very dif-

making it di?cult to discern the source

The recent deprojection analysis of ?ows using IPC data(White,Jones&Forman

ICM masses and mass fractions for many

and seems to employ a comparable method,

poor agreement between our results.There

which we both measure out to1h?1Mpc;

set,there are9clusters for which our uncer-

in M ICM do not overlap.Strangely,there is

of a systematic di?erence in our results;when

one another the scatter is just much larger

bars.Analysis of the ICM mass fractions

more discouraging,with only two clusters in

Here,however,there is a systematic di?erence

of WJF are almost all much lower than

worst o?enders are clusters for which WJF

temperature determinations(Cygnus A,

A2319,A401and A496)or which have velocity dispersions.The source of this dis-

still unclear,but seems to be due at least in

large uncertainties in the galaxy velocity dis-

,which sets the depth of the potential well in

technique.As already mentioned in the calculating ICM masses and mass fractions

assumptions about the detailed shape of potential,and so we make none in our analysis.

we reiterate that because the ICM distri-

directly from the X-ray surface brightness

which provides a better?t to the data will

accurate estimate of M ICM(see§3.Exten-

of the double beta model approach convinces

technique produces a signi?cantly better de-

the data,allowing simultaneous?tting of the

excess and the asymptotic behavior of the

radius of the data.

5.DISCUSSION

We analyze the X-ray and ICM properties of an essen-tially X-ray?ux limited sample of45galaxy clusters.The fundamental observations are archival ROSAT PSPC im-ages and published T X measurements;we describe our techniques in§3to facilitate https://www.wendangku.net/doc/162477479.html,bining our data and an ensemble of numerical cluster simulations, we quantify the uncertainties in the measured quantities. In addition to constraining the properties of the ICM in nearby clusters(see discussion below and§4),we include a set(see Fig.6)of cluster X-ray surface brightness pro?les in physical units along with contour plots of the cluster X-ray emission to help the reader evaluate the goodness of ?t,the importance of substructure and the overall plausi-bility of the analysis in each cluster.While a few clusters in our sample are undergoing major mergers,making our analysis suspect,it should be remembered that our anal-ysis of an ensemble of simulated clusters indicates that M ICM can be estimated with a typical accuracy of～9% in coeval populations with similar morphologies to those observed(i.e.even with merging clusters included).

高二《甜美纯净的女声独唱》教案

高二《甜美纯净的女声独唱》教案一、基本说明教学内容 1）教学内容所属模块：歌唱 2）年级：高二 3）所用教材出版单位：湖南文艺出版社 4）所属的章节：第三单元第一节 5）学时数： 45 分钟二、教学设计 1、教学目标： ①、在欣赏互动中感受女声的音域及演唱风格，体验女声的音色特点。 ②、在欣赏互动中，掌握美声、民族、通俗三种唱法的特点，体验其魅力。 ③、让学生能够尝试用不同演唱风格表现同一首歌。 ④、通过学唱歌曲培养学生热爱祖国、热爱生活的激情。 2、教学重点： ①、掌握女高音、女中音的音域和演唱特点。 ②、掌握美声、民族、通俗三种方法演唱风格。 3、教学难点： ①、学生归纳不同唱法的特点与风格。

②、学生尝试用不同演唱风格表现同一首歌。 3、设计思路《普通高中音乐课程标准》指出：“音乐课的教学过程就是音乐的艺术实践过程。”《甜美纯净的女声独唱》作为《魅力四射的独唱舞台》单元的第一课，是让学生在丰富多彩的歌唱艺术形式中感受出女声独唱以其优美纯净的声音特点而散发出独特的魅力。为此，本课从身边熟悉的人物和情景入手，激发学生学习兴趣，把教学重心放在艺术实践中，让学生在欣赏、学习不同的歌唱风格中，培养自己的综合欣赏能力及歌唱水平。在教学过程中让学生体会不同风格的甜美纯净女声的内涵，感知优美纯净的声音特点而散发出的独特魅力，学会多听、多唱，掌握一定的歌唱技巧，提高自己的演唱水平。为实现以上目标，本人将新课标“过程与方法”中的“体验、比较、探究、合作”四个具体目标贯穿全课，注重学生的个人感受和独特见解，鼓励学生的自我意识与创新精神，强调探究、强调实践，将教学过程变为整合、转化间接经验为学生直接经验的过程，让学生亲身去感悟、去演唱，并力求改变现在高中学生普遍只关注流行歌曲的现状，让学生自己确定最适合自己演唱的方法，自我发现、自我欣赏，充分展示自己的的声音魅力。三、教学过程教学环节及时间教师活动学生活动设计意图

适合女生KTV唱的100首好听的歌

适合女生KTV唱的100首好听的歌别吝色你的嗓音很好学 1、偏爱----张芸京 2、阴天----莫文蔚 3、眼泪----范晓萱 4、我要我们在一起---=范晓萱 5、无底洞----蔡健雅 6、呼吸----蔡健雅 7、原点----蔡健雅&孙燕姿 8、我怀念的----孙燕姿 9、不是真的爱我----孙燕姿 10、我也很想他----孙燕姿 11、一直很安静----阿桑 12、让我爱----阿桑 13、错过----梁咏琪 14、爱得起----梁咏琪 15、蓝天----张惠妹 16、记得----张惠妹 17、简爱----张惠妹 18、趁早----张惠妹 19、一念之间----戴佩妮 20、两难----戴佩妮 21、怎样----戴佩妮 22、一颗心的距离----范玮琪 23、我们的纪念日----范玮琪 24、启程----范玮琪 25、最初的梦想----范玮琪 26、是非题----范玮琪 27、你是答案----范玮琪 28、没那么爱他----范玮琪 29、可不可以不勇敢----范玮琪 30、一个像夏天一个像秋天----范玮琪 31、听，是谁在唱歌----刘若英 32、城里的月光----许美静 33、女人何苦为难女人----辛晓琪 34、他不爱我----莫文蔚 35、你是爱我的----张惠妹 36、同类----孙燕姿 37、漩涡----孙燕姿 38、爱上你等于爱上寂寞----那英 39、梦醒了----那英 40、出卖----那英 41、梦一场----那英 42、愿赌服输----那英

43、蔷薇----萧亚轩 44、你是我心中一句惊叹----萧亚轩 45、突然想起你----萧亚轩 46、类似爱情----萧亚轩 47、Honey----萧亚轩 48、他和他的故事----萧亚轩 49、一个人的精彩----萧亚轩 50、最熟悉的陌生人----萧亚轩 51、想你零点零一分----张靓颖 52、如果爱下去----张靓颖 53、我想我是你的女人----尚雯婕 54、爱恨恢恢----周迅 55、不在乎他----张惠妹 56、雪地----张惠妹 57、喜欢两个人----彭佳慧 58、相见恨晚----彭佳慧 59、囚鸟----彭羚 60、听说爱情回来过----彭佳慧 61、我也不想这样----王菲 62、打错了----王菲 63、催眠----王菲 64、执迷不悔----王菲 65、阳宝----王菲 66、我爱你----王菲 67、闷----王菲 68、蝴蝶----王菲 69、其实很爱你----张韶涵 70、爱情旅程----张韶涵 71、舍得----郑秀文 72、值得----郑秀文 73、如果云知道----许茹芸 74、爱我的人和我爱的人----裘海正 75、谢谢你让我这么爱你----柯以敏 76、陪我看日出----蔡淳佳 77、那年夏天----许飞 78、我真的受伤了----王菀之 79、值得一辈子去爱----纪如璟 80、太委屈----陶晶莹 81、那年的情书----江美琪 82、梦醒时分----陈淑桦 83、我很快乐----刘惜君 84、留爱给最相爱的人----倪睿思 85、下一个天亮----郭静 86、心墙----郭静

2019-2020年高一音乐甜美纯净的女声独唱教案

2019-2020年高一音乐甜美纯净的女声独唱教案一、教学目标 1、认知目标：初步了解民族唱法、美声唱法、通俗唱法三种唱法的风格。 2、能力目标：通过欣赏部分女声独唱作品，学生能归纳总结出她们的演唱风格和特点，并同时用三种不同风格演唱同一首歌曲。 3、情感目标：通过欣赏比较，对独唱舞台有更多元化的审美意识。二、教学重点：学生能用三种不同风格演唱形式演唱同一首歌。三、教学难点：通过欣赏部分女声独唱作品，学生能归纳总结出她们的演唱风格和特点。四、教学过程：（一）导入 1、播放第十三界全国青年歌手大奖赛预告片（师）问：同学们对预告片中的歌手认识吗（生）答：（师）问：在预告片中提出了几种唱法？（生）答：有民族、美声、通俗以及原生态四种唱法，今天以女声独唱歌曲重点欣赏民族、美声、通俗唱法，希望通过欣赏同学们能总结出三种唱法的风格和特点。（二）、音乐欣赏

1、通俗唱法 ①(师)问：同学们平常最喜欢唱那些女歌手的歌呢？能唱唱吗？（可让学生演唱几句喜欢的歌，并鼓励） ②欣赏几首通俗音乐视频一：毛阿敏《绿叶对根的情谊》片段、谭晶《在那东山顶上》片段、韩红《天路》片段、刘若英《后来》片段视频二：超女《想唱就唱唱得响亮》 ①由学生总结出通俗音乐的特点 ②师总结并板书通俗音乐的特点：通俗唱法是在演唱通俗歌曲的基础上发展起来的，又称“流行唱法”。通俗歌曲是以通俗易懂、易唱易记、娱乐性强、便于流行而见长，它没有统一的规格和演唱技法的要求，比较强调歌唱者本人的自然嗓音和情绪的渲染，重视歌曲感情的表达。演唱上要求吐字清晰，音调流畅，表情真挚，带有口语化。 ③指出通俗音乐尚未形成系统的发声训练体系。其中用沙哑、干枯的音色“狂唱”和用娇柔、做作的姿态“嗲唱”，不属于声乐艺术的正道之物，应予以摒弃。 2、民族唱法 ①俗话说民族的才是世界的那么民族唱法的特点是什么呢？ ②欣赏彭丽媛《万里春色满人间》片段鉴赏提示：这首歌是剧种女主角田玉梅即将走上刑场时的一段难度较大的咏叹调。

女生唱的歌曲欢快甜美

女生唱的歌曲欢快甜美美妙的歌曲能令我们陶醉其中而无法自拨，最激烈的歌曲能令我们的身体不由自主的跟着手舞足蹈起来，下面是小编整理的欢快甜美的歌曲的内容，希望能够帮到您。欢快甜美的歌曲 1. Talking - 2. 羽毛- 劲歌金曲 3. 为你- 黑龙 4. 我的小时候- 罗艺达 5. 听说爱情回来过 6. 那个男人 7. 夫妻观灯_韩再芬、李迎春- 中国民歌宝典二 8. 往生- 镀飞爱在阳光空气中- 区瑞强- 音乐合辑 9. 说中国- 班- 华语群星 10. 第十八封信- Kent王健 11. 那一夜你喝了酒- 傅薇 12. 最近比较烦- 周华健/李宗盛/品冠- 滚石群星 13. 告白- 张娜拉 14. Talking VIII - 15. my love - 网友精选曲 16. 音乐人民- 音乐合辑 17. 深深深深- 徐誉滕 18. Honkytonk U - Toby Keith 19. 征服- 阿强 20. 我总会感动你- 沙宝亮欢快甜美的歌曲 1. 一千步的距离- 高桐 2. fleeing star - 音乐合辑 3. My Life - 李威杰 4. 小妹听我说- 金久哲 5. 上海滩- 梁玉嵘- 华语群星 6. 爱我多爱一些- 黎姿 7. 恋人未满- 8. 玛奇朵飘浮- 音乐听吧 9. 風- 音乐听吧 10. 七月- 小鸣 11. 滚滚红尘- 罗大佑 12. 张震岳—想要- 华语群星 13. 有梦有朋友- 14. 童年- 拜尔娜 15. 洪湖水，浪打浪- 宋祖英 16. 只爱到一半- 魏晨 17. 风雨人生路- 何静 18. 居家男人- 回音哥如果当时- 许嵩 19. 那个男人的谎言Tae In - 非主流音乐

适合女生唱的各种难度的歌

【适合女生唱的各种难度的歌】以后点歌的时候记得挑战一下自己（哈哈，今天心情高兴，在微博整理下的小东西和大家分享） 1.我不知道--唐笑（特别喜欢的一首歌） 2.那个--文筱芮（特别伤的歌，真的可以听到心里去） 3.一半--丁当（喜欢喜欢，但没能力唱） 4.指望--郁可唯（本来不喜欢她，但她唱歌挺有水平） 5.路人--江美琪（推荐，好听又挺好唱的） 6.过敏--杨丞琳（听听就知道了） 7.大女人--张亚飞（没什么名气的超级女生，这歌挺棒的） 8.一个人的星光--许静岚（绿光森林的主题曲） 9.不要说爱我--许紫涵（高潮真的挺好听，我爱单曲循环，但这歌还没腻） 10.为你我受冷风吹--林忆莲（没那么简单，都是很喜欢的老歌，偶尔听听老歌感觉特别好） 11.一秒也好--卓文萱（她的（爱我好吗）也不错，最近挺喜欢她的歌） 12.你在哪里--张婧（不被太多人知道的歌手） 13.你的背包--莫艳琳（在校内看到一个女孩唱的，觉得挺好听的） 14.原来爱情那么难--泳儿（好听好听，没什么难度，就是在ktv不太好找） 15.在你眼里--同恩（也是到副歌特别吸引人的一首）

16.很久很久以后--梁文音（爱她的歌，她的很多歌都特别好听） 17.知道我们不会有结果--金莎（听着特别有感觉，那些喜欢听悲伤歌的都是因为这种感觉吧） 18.指尖的星光--钟汶（不太好唱的，我就只有听的份了） 19.放不下--龚诗嘉（挺简单的一首，调调挺平的，她的（远远在一起）也不错） 20.灰色的彩虹--范玮琪 21.现在才明白--萧贺硕（不被太多人知道的歌手，有些歌真的很好听，只是需要慢慢挖掘） 22.终点--关心妍（这首歌大多都听过，自己感觉吧） 23.遇到--王蓝茵（旋律让人感觉特舒服的，很爱的一首） 24.一个人--蔡依（她的毅力不是一般人能做到的） 25.婴儿--陈倩倩（这首歌真的凄凉到有点甚人的感觉。“喜欢一个人的心情”--江语晨，因为这歌的词）26.那又怎么样呢--张玉华（我爱听音乐，但一定要是伤感的，虽然不会听到泪流满面，但是那种感觉真的很好） 27.还爱你--景甜（像这样好听，又不被大家熟悉的歌还有很多吧）

适合女生KTV唱的100首好听的歌

分享适合女生KTV唱的100首好听的歌别吝色你的嗓音很好学 1、偏爱----张芸京 2、阴天----莫文蔚 3、眼泪----范晓萱 4、我要我们在一起---=范晓萱 5、无底洞----蔡健雅 6、呼吸----蔡健雅 7、原点----蔡健雅&孙燕姿 8、我怀念的----孙燕姿 9、不是真的爱我----孙燕姿 10、我也很想他----孙燕姿 11、一直很安静----阿桑 12、让我爱----阿桑 13、错过----梁咏琪 14、爱得起----梁咏琪 15、蓝天----张惠妹 16、记得----张惠妹 17、简爱----张惠妹 18、趁早----张惠妹 19、一念之间----戴佩妮 20、两难----戴佩妮 21、怎样----戴佩妮 22、一颗心的距离----范玮琪 23、我们的纪念日----范玮琪 24、启程----范玮琪 25、最初的梦想----范玮琪 26、是非题----范玮琪 27、你是答案----范玮琪 28、没那么爱他----范玮琪 29、可不可以不勇敢----范玮琪 30、一个像夏天一个像秋天----范玮琪 31、听，是谁在唱歌----刘若英 32、城里的月光----许美静 33、女人何苦为难女人----辛晓琪 34、他不爱我----莫文蔚 35、你是爱我的----张惠妹 36、同类----孙燕姿 37、漩涡----孙燕姿 38、爱上你等于爱上寂寞----那英 39、梦醒了----那英 40、出卖----那英 41、梦一场----那英 42、愿赌服输----那英

100首适合女人唱的歌,不要吝惜自己的嗓子

1、偏爱----张芸京 2、阴天----莫文蔚 3、眼泪----范晓萱 4、我要我们在一起---=范晓萱 5、无底洞----蔡健雅 6、呼吸----蔡健雅 7、原点----蔡健雅&孙燕姿 8、我怀念的----孙燕姿 9、不是真的爱我----孙燕姿 10、我也很想他----孙燕姿 11、一直很安静----阿桑 12、让我爱----阿桑 13、错过----梁咏琪 14、爱得起----梁咏琪 15、蓝天----张惠妹 16、记得----张惠妹 17、简爱----张惠妹 18、趁早----张惠妹 19、一念之间----戴佩妮 20、两难----戴佩妮 21、怎样----戴佩妮 22、一颗心的距离----范玮琪 23、我们的纪念日----范玮琪 24、启程----范玮琪 25、最初的梦想----范玮琪 26、是非题----范玮琪 27、你是答案----范玮琪 28、没那么爱他----范玮琪 29、可不可以不勇敢----范玮琪 30、一个像夏天一个像秋天----范玮琪 31、听，是谁在唱歌----刘若英 32、城里的月光----许美静 33、女人何苦为难女人----辛晓琪 34、他不爱我----莫文蔚 35、你是爱我的----张惠妹 36、同类----孙燕姿 37、漩涡----孙燕姿 38、爱上你等于爱上寂寞----那英 39、梦醒了----那英 40、出卖----那英 41、梦一场----那英 42、愿赌服输----那英 43、蔷薇----萧亚轩 44、你是我心中一句惊叹----萧亚轩

45、突然想起你----萧亚轩 46、类似爱情----萧亚轩 47、Honey----萧亚轩 48、他和他的故事----萧亚轩 49、一个人的精彩----萧亚轩 50、最熟悉的陌生人----萧亚轩 51、想你零点零一分----张靓颖 52、如果爱下去----张靓颖 53、我想我是你的女人----尚雯婕 54、爱恨恢恢----周迅 55、不在乎他----张惠妹 56、雪地----张惠妹 57、喜欢两个人----彭佳慧 58、相见恨晚----彭佳慧 59、囚鸟----彭羚 60、听说爱情回来过----彭佳慧 61、我也不想这样----王菲 62、打错了----王菲 63、催眠----王菲 64、执迷不悔----王菲 65、阳宝----王菲 66、我爱你----王菲 67、闷----王菲 68、蝴蝶----王菲 69、其实很爱你----张韶涵 70、爱情旅程----张韶涵 71、舍得----郑秀文 72、值得----郑秀文 73、如果云知道----许茹芸 74、爱我的人和我爱的人----裘海正 75、谢谢你让我这么爱你----柯以敏 76、陪我看日出----蔡淳佳 77、那年夏天----许飞 78、我真的受伤了----王菀之 79、值得一辈子去爱----纪如璟 80、太委屈----陶晶莹 81、那年的情书----江美琪 82、梦醒时分----陈淑桦 83、我很快乐----刘惜君 84、留爱给最相爱的人----倪睿思 85、下一个天亮----郭静 86、心墙----郭静 87、那片海----韩红 88、美丽心情----RURU

适合女生唱的各种难度的歌

婴儿——陈倩倩这首歌真的凄凉到有点儿甚人的感觉。“喜欢一个人的心情”——江语晨，因为这歌的词。那又怎么样呢——张玉华我爱听音乐，但一定要是伤感的，虽然不会听到泪流满面，但是那种感觉真的很好还爱你——景甜你可以爱我很久吗——游艾迪夜夜夜夜——原唱齐秦爱一直存在——梁文音有人想找男生唱的，真不常听男生的歌，不过有几首觉得挺不错的。初雪的忧伤——赵子浩爱你，离开你——南拳妈妈说谎——林宥嘉三人游、爱爱爱——方大同分开以后——唐禹哲突然好想你——五月天还是男生的，寂寞的季节、暗恋——陶喆情歌两三首——郭顶掌纹——曹格需要人陪——王力宏王妃——箫敬腾（除了这首，其他几首都是比较安静抒情的。）挥之不去——殷悦别再哭了——罗忆诗前段时间特别喜欢这首歌，听的快吐了，真的挺好听。热气球——黄淑惠很特别，超级好听，强烈推荐。你是爱我的——张惠妹她的嗓音让我着迷，超级喜欢。问——粱静茹老歌了，不过很喜欢。

忽略——萧萧握不住的他，看到萧萧还是会第一个想起这首。趁早——张惠妹她有点儿沙哑的声音让我着迷。幸运草——丁当早点儿的歌了，喜欢丁当。哭了——范晓萱越听越喜欢，。氧气——范晓萱小时候喜欢听她的歌，不过随着年龄的增长，喜欢的类型也变了。温柔的慈悲——阿桑喜欢她的歌，只是她的声音不能再更新了。礼物——刘力扬罗美玲的也还好。。洋葱——丁当杨宗纬（两个不一样的感觉）挺难的唱不好，不过喜欢听。眼泪知道——温岚喜欢，唱出来特别有激情哈。

类似爱情——萧亚轩不难又有感觉。第三者——梁静茹还好，喜欢这首歌的歌词。心墙——郭静 “我不想忘记你”，“不药而愈”，“每一天都不同”，都好听喜欢她的歌。我知道你很难过——蔡依林唱起来有感觉也不难唱，推荐。夏伤——SARA 感觉很特别，喜欢。那天——蓝又时喜欢她的歌，她的调调，强烈推荐。“秘密”也不错。礼物——罗美玲好听有感觉，不过不太好唱。我比想象中爱你——JS 老歌了一直很喜欢，唱起来有感觉。

高一音乐甜美纯净的女声独唱教案

魅力四射的独唱舞台 ——甜美纯净的女声独唱一、教学目标 1、认知目标：初步了解民族唱法、美声唱法、通俗唱法三种唱法的风格。 2、能力目标：通过欣赏部分女声独唱作品，学生能归纳总结出她们的演唱风格和特点，并同时用三种不同风格演唱同一首歌曲。 3、情感目标：通过欣赏比较，对独唱舞台有更多元化的审美意识。二、教学重点：学生能用三种不同风格演唱形式演唱同一首歌。三、教学难点：通过欣赏部分女声独唱作品，学生能归纳总结出她们的演唱风格和特点。四、教学过程：（一）导入 1、播放第十三界全国青年歌手大奖赛预告片（师）问：同学们对预告片中的歌手认识吗（生）答：（师）问：在预告片中提出了几种唱法？（生）答：有民族、美声、通俗以及原生态四种唱法，今天以女声独唱歌曲重点欣赏民族、美声、通俗唱法，希望通过欣赏同学们能总结出三种唱法的风格和特点。

（二）、音乐欣赏 1、通俗唱法 ①(师)问：同学们平常最喜欢唱那些女歌手的歌呢？能唱唱吗？（可让学生演唱几句喜欢的歌，并鼓励） ②欣赏几首通俗音乐视频一：毛阿敏《绿叶对根的情谊》片段、谭晶《在那东山顶上》片段、韩红《天路》片段、刘若英《后来》片段视频二：超女《想唱就唱唱得响亮》 ①由学生总结出通俗音乐的特点 ②师总结并板书通俗音乐的特点：通俗唱法是在演唱通俗歌曲的基础上发展起来的，又称“流行唱法”。通俗歌曲是以通俗易懂、易唱易记、娱乐性强、便于流行而见长，它没有统一的规格和演唱技法的要求，比较强调歌唱者本人的自然嗓音和情绪的渲染，重视歌曲感情的表达。演唱上要求吐字清晰，音调流畅，表情真挚，带有口语化。 ③指出通俗音乐尚未形成系统的发声训练体系。其中用沙哑、干枯的音色“狂唱”和用娇柔、做作的姿态“嗲唱”，不属于声乐艺术的正道之物，应予以摒弃。 2、民族唱法 ①俗话说民族的才是世界的那么民族唱法的特点是什么呢？ ②欣赏彭丽媛《万里春色满人间》片段鉴赏提示：这首歌是剧种女主角田玉梅即将走上刑场时的一段难度较大的咏叹调。

适合女生唱的100首好听的歌

适合女生唱的100首好听的歌 1、偏爱----张芸京 2、阴天----莫文蔚味道很难把握 3、眼泪----范晓萱还好，张学友版本适合男生 4、我要我们在一起---范晓萱A段超级难唱的，拍子和口气都不好抓 5、无底洞----蔡健雅听起来简单，唱起来很难——这首是典型的例子 6、呼吸----蔡健雅 7、原点----蔡健雅&孙燕姿到哪里找两个好听的女中音呢？ 8、我怀念的----孙燕姿 9、不是真的爱我----孙燕姿 10、我也很想他----孙燕姿 11、一直很安静----阿桑 12、让我爱----阿桑 13、错过----梁咏琪 14、爱得起----梁咏琪为什么没有洗脸？剪发？胆小鬼？ 15、蓝天----张惠妹 16、记得----张惠妹 17、简爱----张惠妹是剪爱吧？ 18、趁早----张惠妹男生可以试试张宇的版本 19、一念之间----戴佩妮 20、两难----戴佩妮 21、怎样----戴佩妮 22、一颗心的距离----范玮琪 23、我们的纪念日----范玮琪 24、启程----范玮琪 25、最初的梦想----范玮琪 26、是非题----范玮琪 27、你是答案----范玮琪 28、没那么爱他----范玮琪 29、可不可以不勇敢----范玮琪 30、一个像夏天一个像秋天----范玮琪范范的歌这么多啊。。。 31、听，是谁在唱歌----刘若英 32、城里的月光----许美静 33、女人何苦为难女人----辛晓琪我一直在找这个歌曲的粤语版，却找不到

34、他不爱我----莫文蔚 35、你是爱我的----张惠妹 36、同类----孙燕姿 37、漩涡----孙燕姿 38、爱上你等于爱上寂寞----那英在KTV点这个歌曲的MTV不是会很尴尬吗？ 39、梦醒了----那英 40、出卖----那英 41、梦一场----那英 42、愿赌服输----那英没有征服？也许是太大众了吧。 43、蔷薇----萧亚轩 44、你是我心中一句惊叹----萧亚轩 45、突然想起你----萧亚轩 46、类似爱情----萧亚轩 47、Honey----萧亚轩 48、他和他的故事----萧亚轩 49、一个人的精彩----萧亚轩 50、最熟悉的陌生人----萧亚轩还记得她第一张专籍里面最喜欢那首《没有人》 51、想你零点零一分----张靓颖 52、如果爱下去----张靓颖 53、我想我是你的女人----尚雯婕 54、爱恨恢恢----周迅 55、不在乎他----张惠妹 56、雪地----张惠妹 57、喜欢两个人----彭佳慧 58、相见恨晚----彭佳慧 59、囚鸟----彭羚 60、听说爱情回来过----彭佳慧 61、我也不想这样----王菲 62、打错了----王菲 63、催眠----王菲 64、执迷不悔----王菲 65、阳宝----王菲 66、我爱你----王菲 67、闷----王菲

适合女生唱的歌曲

1、郭静：《下一个天亮》《心墙》《你的香气》 2、梁咏琪：《胆小鬼》《短发》《顺时针》《原来感情这么伤》《爱得起》 4、张韶涵：《亲爱的那不是感情》《隐形的翅膀》《欧若拉》《遗失的完美》《寓言》《梦里花》《不想懂得》《如果的事》《手心的太阳》《呐喊》 5、萧亚轩：《一个人的精彩》《爱的主打歌》《突然想起你》《冲动》《最熟悉的陌生人》《吻》《cappuccino》《我要的世界》《正因你》《类似感情》婚庆主持词 6、王心凌：《第一次爱的人》《当你》《我会好好的》《那年夏天宁静的海》《羽毛》《爱你》 7、蔡依林：《倒带》《天空》《假装》《日不落》《马德里不思议》《说爱你》《我知道你很难过》《看我变》《一个人》《你快乐吗》《听说感情回来过》 8、杨丞琳：《左边》《带我走》《暧昧》伤感短文 9、孙燕姿：《开始懂了》《遇见》《我怀念的》《天黑黑》《我不难过》《我要的幸福》《我也很想他》《懂事》《橄榄树》《我的爱》《爱从零开始》 10、刘若英：《当爱在靠近》《之后》《很爱很爱你》《原来你也在那里》《成全》《一辈子的孤单》《为爱痴狂》《听说》《光》 11、she：《一眼万年》《superstar》《他还是不懂》《热带雨林》《痛快》《星光》《说你爱我》《怎样办》 12、莫文蔚：《盛夏的果实》《忽然之间》《阴天》《如果没有你》《爱》 13、fir：《我们的爱》《lydia》《千年之恋》《你的微笑》《flyaway》《把爱放开》《北极圈》

14、王菲：《棋子》《但愿人长久》《红豆》《流年》 15、范玮琪：《最初的梦想》《一个像夏天一个像秋天》《是非题》《黑白配》《可不能够不勇敢》《没那么爱他》更多推荐： 1、《陪着我的时候想着他》郭静每次听郭静的歌思想都会飞到很远的地方，那个只属于他的城市。期望他过得好想着和他的每个细节。不知道你是不是也会感觉到在遥远的城市还有个人在牵挂你。下一个天亮等待着他会出此刻我的城市，期盼你有机会陪着我的时候不会想着她。 2、《我的歌声里》曲婉婷最近多少个夜晚，情绪总有空空的失落，怅惘若失陷入久久回忆不自拔，朋友说听着这首歌，不一样于以往情歌的酸腻，简单的旋律，却又似曾相识的情感悲欢，有一种洒脱不羁，有一种简单惬意，释放所有，久久沉醉，而往的失落更多暖暖的情谊填补，谢谢婷婷的歌声。自由而又惬意，今虽不能至，而心向往之。 3、《怎样唱情歌》刘惜君刘惜君的嗓音当中总是蕴含着一种安静却坚定的力量，在轻而易举的打动你之后，又轻绕在心间久久难以褪去。我想再完美的唱功都是离不开那颗细腻的心，似在诉说你我共同的境况。 4、《还是要幸福》田馥甄虽然歌曲很苦情，但不管什么样的歌，都会有可能在一个你适合听的心境下。让你觉得对，这首歌就是我此刻最需要的！也许是在你差一点就要笑出来的时刻，也许是你差一点就要哭出来的时候。这首歌会变成k歌金曲吧。这首歌会让很多人在ktv唱到哭吧

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18、趁早----张惠妹 19、一念之间----戴佩妮 20、两难----戴佩妮 21、怎样----戴佩妮 22、一颗心的距离----范玮琪 23、我们的纪念日----范玮琪 24、启程----范玮琪 25、最初的梦想----范玮琪 26、是非题----范玮琪 27、你是答案----范玮琪 28、没那么爱他----范玮琪 29、可不可以不勇敢----范玮琪 30、一个像夏天一个像秋天----范玮琪 31、听，是谁在唱歌----刘若英 32、城里的月光----许美静 33、女人何苦为难女人----辛晓琪 34、他不爱我----莫文蔚 35、你是爱我的----张惠妹 36、同类----孙燕姿 37、漩涡----孙燕姿 38、爱上你等于爱上寂寞----那英 39、梦醒了----那英

40、出卖----那英 41、梦一场----那英 42、愿赌服输----那英 43、蔷薇----萧亚轩 44、你是我心中一句惊叹----萧亚轩 45、突然想起你----萧亚轩 46、类似爱情----萧亚轩 47、honey----萧亚轩 48、他和他的故事----萧亚轩 49、一个人的精彩----萧亚轩 50、最熟悉的陌生人----萧亚轩 51、和你一样----李宇春 52、蜀绣----李宇春 53、下个路口见----李宇春 54、对不起，只是忽然很想你----李宇春 55、不在乎他----张惠妹 56、雪地----张惠妹 57、喜欢两个人----彭佳慧 58、相见恨晚----彭佳慧 59、囚鸟----彭羚 60、听说爱情回来过----彭佳慧 61、我也不想这样----王菲

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36L：懂事——孙燕姿 37L：天真——弦子 38L：属于——梁静茹 39L：说爱我——梁一贞 40L：放逐爱情——解伟苓 41L：飞走了——金海心 42L：原谅——张玉华 43L：烟火——郭采洁 44L：狠狠哭——郭采洁 45L：我很快乐——刘惜君 46L：蓝色雨——温岚 47L：你是爱我的——张惠妹 48L：问——梁静茹 49L：趁早——张惠妹 50L：哭了——范晓萱 51L：温柔的慈悲——阿桑 52L：礼物——刘力扬 53L：爱你胜过爱自己——岳夏54L：其实不快乐——岳夏 55L：孤单心事——蓝又时 56L：我们没有在一起——刘若英57L：一个人失忆——薛凯琪 58L：眼泪笑了——刘力扬 59L：还是好朋友——王心凌 60L：错的人——萧亚轩 61L：旅行的意义——陈绮贞 62L：你还在不在——梁静茹 63L：越来越不懂——蔡健雅 64L：双栖动物——蔡健雅 65L：口香糖——梁咏琪 66L：顺时针——梁咏琪 67L：不远——萧亚轩 68L：后来的我们——萧亚轩 69L：阳光下的星星——金海心70L：一个人的美术馆——杨千嬅71L：阴天——莫文蔚 72L：不怕付出——蓝心湄 73L：倒带——蔡依林 74L：他不爱我——莫文蔚 75L：开始懂了——孙燕姿 76L：静止——杨乃文 77L：人间——王菲 78L：单身旅记——陶晶莹 79L：单人房双人床——莫文蔚

适合女生KTV唱的100首好听的歌

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15、蓝天----张惠妹 16、记得----张惠妹 17、简爱----张惠妹 18、趁早----张惠妹 19、一念之间----戴佩妮 20、两难----戴佩妮 21、怎样----戴佩妮 22、一颗心的距离----范玮琪 23、我们的纪念日----范玮琪 24、启程----范玮琪 25、最初的梦想----范玮琪 26、是非题----范玮琪 27、你是答案----范玮琪 28、没那么爱他----范玮琪 29、可不可以不勇敢----范玮琪 30、一个像夏天一个像秋天----范玮琪 31、听，是谁在唱歌----刘若英 32、城里的月光----许美静 33、女人何苦为难女人----辛晓琪 34、他不爱我----莫文蔚 35、你是爱我的----张惠妹 36、同类----孙燕姿 37、漩涡----孙燕姿 38、爱上你等于爱上寂寞----那英 39、梦醒了----那英 40、出卖----那英 41、梦一场----那英 42、愿赌服输----那英 43、蔷薇----萧亚轩 44、你是我心中一句惊叹----萧亚轩 45、突然想起你----萧亚轩 46、类似爱情----萧亚轩 47、Honey----萧亚轩 48、他和他的故事----萧亚轩 49、一个人的精彩----萧亚轩 50、最熟悉的陌生人----萧亚轩 51、想你零点零一分----张靓颖 52、如果爱下去----张靓颖 53、我想我是你的女人----尚雯婕 54、爱恨恢恢----周迅 55、不在乎他----张惠妹 56、雪地----张惠妹 57、喜欢两个人----彭佳慧 58、相见恨晚----彭佳慧

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9. 校花 10. 星光依旧灿烂 11. 美丽心情 12. 奔跑黄征/羽泉 13. 青春无悔 14. 老同学 15. 下雨天 16. 迟到 17. 心愿 18. 没有如果《败犬女王》偶像剧主题曲 19. 丁香花 20. 长大后我就成了你 21. 在你的耳边轻轻唱首歌 22. 放心去飞毕业季最催泪的歌 1. 难舍难分 2. 年轻的战场 3. 别在我离开之前离开 4. 我的快乐《命中注定我爱你》电视剧插曲 5. 红茶

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适合女生唱的100首歌

偏爱----张芸京 2、阴天----莫文蔚 3、眼泪----范晓萱 4、我要我们在一起---=范晓萱 5、无底洞----蔡健雅 6、呼吸----蔡健雅 7、原点----蔡健雅&孙燕姿 8、我怀念的----孙燕姿 9、不是真的爱我----孙燕姿 10、我也很想他----孙燕姿 11、一直很安静----阿桑 12、让我爱----阿桑 13、错过----梁咏琪 14、爱得起----梁咏琪 15、蓝天----张惠妹 16、记得----张惠妹 17、简爱----张惠妹 18、趁早----张惠妹 19、一念之间----戴佩妮 20、两难----戴佩妮 21、怎样----戴佩妮 22、一颗心的距离----范玮琪 23、我们的纪念日----范玮琪 24、启程----范玮琪 25、最初的梦想----范玮琪 26、是非题----范玮琪 27、你是答案----范玮琪 28、没那么爱他----范玮琪 29、可不可以不勇敢----范玮琪 30、一个像夏天一个像秋天----范玮琪 31、听，是谁在唱歌----刘若英 32、城里的月光----许美静 33、女人何苦为难女人----辛晓琪 34、他不爱我----莫文蔚 35、你是爱我的----张惠妹 36、同类----孙燕姿 37、漩涡----孙燕姿 38、爱上你等于爱上寂寞----那英 39、梦醒了----那英 40、出卖----那英 41、梦一场----那英 42、愿赌服输----那英 43、蔷薇----萧亚轩 44、你是我心中一句惊叹----萧亚轩

绝美吟唱精选28首

绝美吟唱精选28首欧美女声吟唱精选集1、Molde canticle 由挪威著名的女歌手Sissel Kyrkjebo演唱，她的声音犹如俗世中的清泉，像水晶般闪铄透明。整首歌没有一句歌词，只有一个缥缈空灵的女声在哼唱，动情的演绎，用旋律去演绎那心底的悲伤，静静地去聆听，聆听它的意境，美丽与惆怅在一瞬间悄然释怀…… 2、Armenian Song 亚美尼亚颂歌是《神奇的地球》节目片中的插曲，闭上眼睛聆听，横跨天际穹窿般的回响共鸣，并没有咄咄逼人的气势，你能感到的只是温和、悠长、祥和的触及…… 3、NOCTURNE 夜曲由神秘园Rolf Lovland和Fionnuala Sherry亲自作词，苏格兰歌手Karen Matheson演唱，纯净飘忽的女声平空而起，像夜色中一泓旖旎的月光，于是夜不再是死寂的，那温柔的月色引领着我，走进一个神秘奇异的世界…… 4、a perfect indian 爱尔兰的女歌星sineado’connor演绎的完美的印第安人，听着淡淡的歌感觉到一丝淡淡的忧郁，会有些黯然的感觉。那音乐轻轻的如耳语一般，又仿佛在静静地述说一个故事，带着回忆带着心酸，伤感、缥缈而细腻的温柔心碎，那一刻，你心里的世界与音乐契合了。

5、THE WELL 由挪威Skien的二重唱组合Shine Dion 演绎，他们的音乐被认为是挪威和爱尔兰民谣的结合体，其中Janne Hansen是主唱兼歌词创作，Per Sel or则主作曲和吉他弹奏，歌词灵感来自挪威当地的神话，从中我们可以感受到田园般的宁静,灵魂的脆弱及时间的无涯。the well 歌词像诗一样地美，月下凭栏，纯净的夜晚，纯洁的心灵，一种纯美的情感似涓涓细流涌入心田…… 6、Two Horizon 由爱尔兰的天籁女声Moya.Brennan 演唱，Moya的旋律是空旷的凯尔特山谷，蓝天碧野，山林梦境，玄妙而空灵，妹妹Enya的音乐则是寂静的爱尔兰大海，沧浪水雾，海风絮语，绵绵而深切。优美的旋律给人神圣、洁净、温柔、美丽、清幽、空旷、辽阔的感觉………… 7、perfect time 出自Moya.Brennan 的专辑《Perfect Time》，优美的旋律，晶莹剔透的音色，轻轻的吟唱，如梦如幻的呢喃，柔和的安抚着人们纤细的心灵…… 8、It Doesn't Matter 由美国兰草音乐的领军者Alison Krauss演唱，她单薄而清亮的歌声，时而欢喜昂扬，时而抑郁低吟，很简单的音乐，很平实地唱着生活中的点点滴滴，有一种不愠不火，温暖却忧郁，柔美又坚强的感动…… 这首歌曲的吉他配音，旋律优美，悦耳动听，音符飞溅，音色也是相当圆润和谐，清晰通透，乐队技艺精湛，弹风潇洒自如，让人感觉暖如春风9、when you say nothing at all

Properties of the Intracluster Medium in an Ensemble of Nearby Galaxy Clusters

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