Clustering of HI galaxies in HIPASS and ALFALFA

0.17 MB·English

by  S. S. Passmoor

Mon.Not.R.Astron.Soc.000,000–000 (0000) Printed27January2011 (MNLATEXstylefilev2.2)
Clustering of HI galaxies in HIPASS and ALFALFA
S. S. Passmoor1⋆, C. M. Cress1,2, A. Faltenbacher1
1 1Physics Department, Universityof the WesternCape , Bellville 7535, South Africa
1 2Centre for HighPerformance Computing, 15 Lower Hope St, Cape Town 7700, South Africa
0
2

n 27January2011
a
J

6 ABSTRACT
2 WeinvestigatetheclusteringofHI-selectedgalaxiesintheALFALFAsurveyandcom-
pareresultswiththoseobtainedforHIPASS.Measurementsoftheangularcorrelation

] functionandthe inferred3D-clusteringarecomparedwith resultsfromdirectspatial-
O
correlationmeasurements.Weareabletomeasureclusteringonsmallerangularscales
C
and for galaxies with lower HI masses than was previously possible. We calculate the
. expected clustering of dark matter using the redshift distributions of HIPASS and
h
ALFALFA and show that the ALFALFA sample is somewhat more anti-biased with
p
- respect to dark matter than the HIPASS sample.
o
r Key words: large-scale structure of the universe - radio lines: galaxies
t
s
a
[

1 1 INTRODUCTION latter work also measures thebiasof HIPASSgalaxies rela-
v
tiveto theexpected dark matterdistribution.
7 Measurementsoftheclusteringofgalaxies allows onetoin-
In this paper we measure the clustering of HI-selected
0 vestigate the relationship between dark and luminous mat-
galaxies detected with the Arecibo L-band Feed Array
0 ter. By comparing galaxies selected in different ways one
(ALFA) and compiled in the partially completed AL-
5 gains understanding of how different galaxies trace the un-
. FALFA survey (the Arecibo Legacy Fast ALFA survey,
1 derlyingdarkmatterandalsoofprocessesatworkingalaxy
Giovanelli et al.2005).Theresultsarecomparedwiththose
0 evolution. This information is important when using galax-
obtained for HIPASS.Clustering measurements in HIPASS
1 ies as probes of cosmological parameters.
are limited to large angular scales where the beam-size of
1 A number of new radio telescopes, such as the
∼15arcminsdoes notcauseconfusion. TheALFALFAres-
: MeerKAT1 ,ASKAP2 andtheSKA,areinthepipelineand
v olution is more than four times better allowing us to probe
i they will detect huge numbers of galaxies using HI. A reli- clustering on smaller scales. The rms noise per ALFALFA
X able measure of the bias of HI-selected galaxies and insight
beam is about six times smaller, providing a catalogue of
r intotheevolutionofthebiasisimportantforforecastingthe
a sources which spansa wider range of redshifts and includes
capabilities of telescopes which will probe HI at intermedi-
galaxies withlowerHImasses. Wearethusabletomeasure
ate or high-redshifts. The clustering of HI-selected galax-
clustering of HI-selected galaxies in regimes that have not
ieshasbeenstudiedbyMeyeret al.(2007),Basilakos et al.
yetbeenexploredandtoinvestigatetrendsseeninHIPASS,
(2007) and Ryan-Weber (2006). They used data from the
using an independent survey.
HI Parkes All Sky Survey (HIPASS, Meyer et al. 2004), a
The outline of the paper is as follows: In § 2 we give a
blind survey for HI of the southern sky which generated
short introduction to the HIPASS and ALFALFA surveys.
a catalogue of 4315 sources, the bulk of which have red-
Thecomputationoftheangularandspatialtwo-pointcorre-
shifts below z ∼0.02. They showed that HI-selected galax-
lationfunctionsisdescribedin§3.Theresultsarepresented,
ies are less clustered than galaxies selected in other ways.
discussedandcomparedwithearlierworkin§4.Finally,§5
Meyer et al. (2007) investigated clustering of various sub-
concludes with a short summary.
samplesofHIPASSgalaxies,showingthatgalaxieswithhigh
rotation velocities are moreclusteredthan thosewith lower
rotationvelocities.Therewereindicationsthatgalaxiescon-
taining more HI are also more clustered but the differences 2 DATA
were not as pronounced as in Basilakos et al. (2007). The
HIPASScovers all the southern sky with δ <+2◦ and and
can detect HI with velocities in the range 300 kms−1 −
12700 kms−1. The rms noise per beam is ∼ 13 mJy
⋆ E-mail:[email protected] (Meyeret al. 2004). To exclude structure associated with
1 www.ska.ac.za theMilky-Way,likehigh-velocitycloudsandlowmasssatel-
2 www.atnf.csiro.au/SKA/ lites, we only use sources with recessional velocities larger
2 S. S. Passmoor et al.
0.1 struction of the random samples. We do not employ the
ALFALFA weighted correlation functions used in Meyeret al. (2007)
0.09 HIPASS
Virgo 1 asweareinterestedincomparingtheresultsofthetwosur-
0.08 Anti-Virgo
veys and in comparing our results with those predicted for
er 0.07 Virgo 2
mb dark matter within a ΛCDM model. The unweighted mea-
Nu 0.06 surementssufficeforthisworkandweareabletocheckour
ed 0.05 unweighted results against those of Meyeret al. (2007).
s
ali 0.04
m
or 0.03
N 3.1 The Angular Two-Point Correlation Function
0.02
0.01 The angular correlation function, ω, is a simple measure of
0 theclusteringofgalaxiesasafunctionofangularseparation
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 on the sky, θ, which does not require redshift information.
Redshift
It is calculated by counting galaxy pairs within a given an-
Figure 1. Plot of the normalised redshift distribution of the gularseparation binandcomparingthisnumbertoacorre-
HIPASSandALFALFAsurveysaswellasthedistributionofthe sponding figure derived from a random catalogue with the
ALFALFAsourcesinthethree,spatiallyseparatedstrips. same area and shape. The angular correlation, ω(θ), gives
the excess probability, over random, of finding two galaxies
separated by angle θ.
than600kms−1.TheaveragemassofHIinHIPASSgalaxies If we assume a redshift-dependent power law de-
is 3.24×109M . scribes the 3-D real-space correlation function, ξ(r,z) =
⊙
When completed, the ALFALFA Survey (r/r0)−γ(1+z)γ−(3+ǫ) (as in Peebles 1980 and Loan et al.
(Giovanelli et al. 2005) will cover 7000deg2 of sky with 1997)thentheangularcorrelation functionisrelatedtothe
high galactic latitude and to a depth of cz ∼18000kms−1. spatial correlation function by the Limber equation (Rubin
Thermsnoiseofthesurveyis∼2.2mJyandthebeam-size 1954; Limber 1954):
is ∼ 3.6 arcminutes. Currently the ALFALFA survey
Tcohnetsaeinsartehrteheesttrwipos sctorvipersincgenatrteodtalonaretaheofV∼irg4o00redgeigo2n. θ 1−γ = R0∞ N2(z)(1+z)γd−′((z3+)rǫ0−)√γπ(d(z)θ)1−γ Γ(−Γ(1221+γ12)γ) ,
and the anti-Virgo strip. They contain 1796 sources with (cid:18)θ0(cid:19) ∞N(z)dz 2
0
cz >600kms−1. The first completed Virgo strip is defined (cid:0)R (cid:1) (1)
by 11h44m < α<14h00m and 12◦ <δ <16◦ and contains whered(z)isthecomovingdistanceandN(z)istheredshift
708 sources. The second Virgo strip contains 556 galaxies number density distribution of the sources (cf. Figure 1).
within11h36m <α<13h52mand8◦ <δ<12◦(Kent et al. We use ǫ=0.8, consistent with the expected clustering be-
2008). The anti-Virgo strip contains 488 sources within haviourinlineartheory,althoughthesurveysaresoshallow
22h00m <α<03h04m and 26◦ <δ<28◦ (Saintonge et al. thattheevolutionofξ couldbeignored. Themeasured val-
2008). A circular region, with radius of 1◦ centred on uesforthelogarithmic slope aθ =1−γ andthecorrelation
M 87, has been removed from the survey area due to the lengthθ0(ω(θ0)=1)canthenbeusedtodeterminethe3-D
interference of M 87 (Giovanelli et al. 2007). The average parameters r0 and γ.
ALFALFA HI mass is 2.48×109M . The source density of The errors for ω are calculated using jack-knife re-
the ALFALFA catalogue is approx⊙imately 20 times higher sampling(Lupton1993).Forthispurposethedataaresplit
than that of theHIPASSsurvey. up into N RA-bins and the correlation function is recalcu-
Figure 1 displays the normalised redshift distributions latedrepeatedlyeachtimeleavingoutadifferentbin.Thusa
of the HIPASS and ALFALFA surveys as well as the red- setofN values{ωi,i=1,...,N}forthecorrelationfunction
shift distributions in the three separate ALFALFA regions. are obtained and the jack-knife error of the mean, σωmean,
A redshift of z = 0.02 corresponds to ∼ 70h−1Mpc which is given by
is roughly the distance to the Coma supercluster. The high
N
galaxy density near theComa cluster and Virgo cluster are
σ =v(N−1) (ω −ω)2/N . (2)
evident in the redshift distributions shown for the Virgo ωmean u i
regions. In the anti-Virgo region the effect of the Perseus- ut Xi=1
Piscessuperclustercanalsobeseenasaslightenhancement The HIPASSsample has been divided into 24 RA bins
of galaxies at a redshift of about z ≈0.025. Below, we will while for the ALFALFA catalogue we use 12 binssuch that
discusstheimpactoftheseinhomogeneitiesonthedetermi- each bin contains approximately thesame area of thesky.
nation of the two-point correlation function.
3.2 The Projected Two-Point Correlation
Function
3 TWO-POINT CORRELATION FUNCTIONS
The projected correlation function, Ξ(σ), is determined by
Herewereviewsomebasicpropertiesoftheangularandpro- the number of pairs at given radial and projected separa-
jectedtwo-pointcorrelationfunctions(ωandΞrespectively) tions, π and σ, and a subsequent integration along the ra-
and indicate their relations to the three dimensional (3-D) dialdirection.Forthatpurposetheabsoluteradialdistance
real-space two-point correlation function, ξ. Subsequently, betweenapairofgalaxies, π=|(v −v )/H |,andtheiran-
i j 0
we introduce the estimator used here and discuss the con- gularseparation,θ,areconvertedintoaprojecteddistance,
Clustering of HI galaxies 3
σ=[(vi+vj)/H0]tan(θ/2). Thus, 10
Dark matter prediction for ALFALFA
Ξ(σ) 2 Dlimit Dark matter prediction for HIPASS
= ξ(σ,π)dπ (3) ALFALFA
σ σ Z HIPASS
0
1
whereD isthelimit wheretheintegral converges.Here
limit
we set Dlimit = 25h−1Mpc ≈ 2500kms−1. The projected θ)
correlation function is related to the real-space correlation ω(
function by(e.g., Davis & Peebles 1983): 0.1
Ξ(σ) 2 ∞ rdr
= ξ(r) . (4)
σ σ Z (r2−σ2)1/2
σ
0.01
Assumingthattheprojectedandreal-spacecorrelationfunc- 0.1 1 10
tions follow power laws within the region of interest (r < θ (deg)
10h−1Mpc), the parameter for the real-space correlation
Figure 2. Angular correlation functions for HIPASS and AL-
function can be derived from the projected one by the fol-
FALFA.Errorbarswerecalculatedusingjack-knifesampling.The
lowing expression:
solidredlineandthesmall-dashedbluelineshowthecorrespond-
Ξ(σ) = r0 γ Γ(1/2)Γ((γ−1)/2) (5) ingpowerlawfitsforanglesbetween0.1and8◦forALFALFAand
σ (cid:16)σ(cid:17) Γ(γ/2) between1and10◦fortheHIPASSdata.Theprojectedclustering
ofdarkmatter(inaΛCDMmodel)withredshiftdistributionsof
More specifically, we calculate r0 and γ by fitting a power HIPASSandALFALFA areshownbythe greendashed lineand
law, Ξ(σ)/σ = (σ/σ0)−aσ, using the Levenberg-Marquardt themagentadotted linerespectively
nonlinearleast-squaresmethod.Theparametersofthereal-
space correlation function are then given by
We repeat the random catalogue generation 20 times and
1
r =σ Γ(1/2)Γ((aσ−1)/2) −aσ (6) thecalculation of random pairs in order to reduce the vari-
0 0(cid:20) Γ(aσ/2) (cid:21) ance from therandom sampling.
γ =a . (7)
σ
3.4 The angular correlation function of dark
Therefore, similar to the angular correlation function the
matter
projected correlation functioncanbeusedtodeterminethe
real-space clustering. We apply both methods to determine Based on Limber’s equation, the redshift distributions of
thereal-spaceclusteringstrengthbasedontheHIPASSand HIPASS and ALFALFA and the expression for the non-
theALFALFA surveysand compare the results. linearpowerspectrumdiscussedinPeacock & Dodds(1996)
we predict the angular correlation function of dark matter
using the cosmological parameters given in Komatsu et al.
3.3 Estimator and random sampling
(2009). The bias parameter, b, at various angles is then de-
Threedifferent estimators are commonly used todetermine termined by
the two-point correlation function (Davis & Huchra 1982;
ω
Hamilton 1993; Landy & Szalay 1993). In this work we use b= HI . (9)
the Landy& Szalay (1993) estimator as it reduces errors rωdarkmatter
caused byedgesand holes within agiven catalogue. Inpar-
ticular this is important for the ALFALFA survey with the
hole caused by M 87 (Giovanelli et al. 2007) and the large 4 RESULTS
edge effects due to the three strips. The estimator is of the
4.1 The full HIPASS and ALFALFA samples
form:
DD(r)−2DR(r)+RR(r) 4.1.1 Angular correlation functions
ξ(r)= , (8)
RR(r)
Figure2showstheangularcorrelationfunctionsforHIPASS
whereristheseparationdistancewhichhasdifferentmean- andALFALFAdataaswellasthepredictedcorrelationfunc-
ings for the different correlation functions. For the angular tions of cold dark matter weighted with the redshift distri-
correlation it denotes the separation angle, θ, for the pro- butionsof thesurveys.Thestraight lines are powerlaw fits
jected correlation function it is the projected distance, σ, forpairseparationsintherangebetween0.1and8◦ forAL-
and for thereal-space correlation it indicates thereal-space FALFA and between 1 and 10◦ for HIPASS. The effect of
distancer.DD(r)isthenumberofdata-datapairs,DR(r)is source confusion in HIPASS is evident at smaller angular
thenumberofdata-randompairs,andRR(r)isthenumber scales. The corresponding parameters, θ and a , are given
0 θ
of random-random pairs all with separations r. in Table 1.
The random catalogues were generated with uniform The measured slopes in the two surveys agree reason-
distributionsontheskyandredshiftdistributionswhichre- ablywell.Asexpected,thevalueofθ islowerforALFALFA
0
semblethedistributionofrecessionalvelocitiesinthesurvey since it is deeper than HIPASS and the clustering in 3-D
smoothed using kernel density estimation (Wand & Jones iswashed outinthe2-Dprojection. ALFALFAalso detects
1995). Throughout this work we use random samples that galaxieswithlowerHImasseswhicharepotentiallylessclus-
are equal in size compared to the corresponding data set. tered.
4 S. S. Passmoor et al.
HIPASS ALFALFA
10000
ALFALFA regions with clusters removed
θa0θ 00..65063±±00.0.024◦ 00..50944±±0.00.6013◦ 1000 AnVVti-iiVrrggiroog 12o
-1c) 100
Table 1.Theangularclusteringfittedparameters,θ0 andaθ. Mp 10
h
σ ( 1
HIPASS ALFALFA Ξ(σ)/ 0.1
σ0 6.29±0.36h−1Mpc 5.34±1.08h−1Mpc 0.01
aσ 1.62±0.04 1.68±0.13
0.001
0.1 1
Table 2.Theprojectedclusteringfittedparameters,σ0 andaσ. σ (h-1Mpc)
4.1.2 Projected correlation functions Figure 4. Comparison of the projected correlation functions
of the three strips of the current ALFALFA survey as well as
Figure 3 shows the projected correlation functions for the thecorrelationfunctionmeasuredwhenALFALFAdatabetween
HIPASS and ALFALFA surveys. The lines represent power 0.02<z<0.03isexcluded(thatis,whencluster galaxiesinthe
law fits and the corresponding parameters are presented in Coma and Perseus-Pisces regions areexcluded) The data points
Table 2. Once again the slopes, a , agree well while the forthethreestripsareslightlyoffsettoimprovereadability.Error
σ
amplitudeofclustering,σ ,inALFALFAislower(although barsarecalculatedusingjack-knifesampling.
0
theuncertainties are fairly large).
consistentwiththevalueof0.68obtainedbyBasilakos et al.
(2007). ForALFALFA, on thesame angular scales, theval-
4.1.3 Inferred spatial correlations
ues range between 0.41 and 0.62, with an average of 0.52.
Table3showsthespatialcorrelationfunctionparametersin- OurresultsthusindicatethattheALFALFAsampleissome-
ferred from the angular and projected correlation functions whatmoreanti-biasedthantheHIPASSsample.Thisiscon-
obtainedusingEq.1andEq.6.Thesubscripts,θandσ,indi- sistent with the idea that ALFALFA includes galaxies with
catewhichcorrelationfunctionhasbeenusedtoderivethese lowerHImasswhicharelessclusteredthanthehighermass
parameters. The two values obtained for r0 in HIPASS are galaxies detected in HIPASS. We note, however, that the
within 2σ ofeach otherand agreewell with theunweighted lower values are found at large scales where thenarrowness
value of 2.7 obtained by Meyeret al. (2004). The two AL- of thestrips may effect measurements more severely.
FALFA values are consistent with each-other and indicate
somewhat lower clustering than HIPASS.
4.2 Correlation functions of different ALFALFA
subsamples
4.1.4 Bias estimation
4.2.1 Flux and HI mass subsets
Thepredictedangularcorrelationfunctionofdarkmatteris
TheALFALFAdatahasbeensubdividedintotwoequivalent
compared with our results in Figure 2. We have calculated
parts based on the flux of the sources. For these subsam-
the bias for the two surveys at each data point in the plot.
ples the angular and projected correlation functions were
ForHIPASS,inthe1−10◦ range,wefindbiasvaluesrang-
recalculated as described in section 3. In agreement with
ingfrom 0.54 to0.70, with anaverage of0.63. Thisisfairly
Meyeret al. (2007) we find that the two correlation func-
tionscomparewellwitheachotherandwiththecorrelation
10000 functionofthewholedatasetindicatinganegligibledepen-
ALFALFA
1000 HIPASS denceof clustering on HI flux.
We also split the samples evenly into high and low HI-
-1c) 100 mass subsamples. The clustering parameters obtained are
Mp 10 shown in Table 3. Our results for HIPASS are consistent
σ (h 1 withthoseofMeyeret al.(2007),indicatingthatthegalax-
σ)/ ies with higher HI-masses are more clustered. Interestingly,
Ξ( 0.1 thesametrendisnotapparentintheALFALFAsurveybut
0.01 theuncertainties are fairly large.
We did not attempt to separate the galaxies according
0.001
0.1 1 10 to their rotation velocities as this requires additional data
σ (h-1Mpc) to estimate inclinations.
Figure 3. Projected correlation functions for HIPASS and AL-
4.2.2 Small Field effects
FALFA.Errorbarsarecalculatedusingjack-knifesampling.Lines
show the corresponding power law fits for separations between
The Virgo regions contain over-densities of galaxies that
0.1and3.5h−1MpcforALFALFAand0.2and8h−1Mpcforthe are associated with the Virgo and Coma clusters. There is
HIPASSdata.
the concern that the results will be biased by the presence
Clustering of HI galaxies 5
HIPASS ALFALFA 0.62at1◦ andanaveragevalueof0.52overthewholerange.
Thesignificantanti-biasofgalaxieswithlowHI-massisim-
r0,θ 2.89±0.08h−1Mpc 2.00±0.40h−1Mpc portant to consider when estimating the signal-to-noise of
γθ 1.56±0.02 1.59±0.06 experimentsplanned for theSKA and its pathfinders.
r0,σ 2.51±0.20h−1Mpc 2.30±0.53h−1Mpc
γσ 1.62±0.04 1.68±0.13
MHI <109.25h−2M ACKNOWLEDGEMENTS
⊙
r0,σ 2.26±0.36h−1Mpc 2.48±0.69h−1Mpc We thank the South African Square Kilometre Array
γσ 1.60±0.08 1.59±0.13 Project,NationalResearchFoundationandCentreforHigh
PerformanceComputingforsupport.Wealsothanktheref-
MHI >109.25h−2M⊙ eree for helpful comments.
r0,σ 3.32±0.55h−1Mpc 2.04±0.65h−1Mpc
γσ 1.50±0.08 1.74±0.24
REFERENCES
Table3.Thereal-spaceclusteringparameters,r0 andγ derived
from the angular correlation function (indicated by subscript θ) Basilakos S., Plionis M., Kovaˇc K., Voglis N., 2007, MN-
and from the projected correlation function (indicated by sub- RAS,378, 301
scriptσ. Davis M., HuchraJ., 1982, ApJ,254, 437
Davis M., Peebles P. J. E., 1983, ApJ, 267, 465
Giovanelli R., HaynesM. P., Kent B. R.,Perillat P., Sain-
of such dominant large-scale structure within the relatively
tonge A., Brosch N., Catinella B., Hoffman G. L., et al.,
smallsurveyfields.Toinvestigatethis,thecorrelation func-
2005, AJ, 130, 2598
tionsofthethreeregionswerecalculatedseparatelyandare
Giovanelli R., Haynes M. P., Kent B. R., Saintonge A.,
showninFigure4.Measurementsinthethreeregionsagree
StierwaltS.,AltafA.,BalonekT.,BroschN.,etal.,2007,
to within their uncertainties, indicating that the presence
AJ,133, 2569
of the big clusters within the Virgo regions do not effect
Hamilton A. J. S., 1993, ApJ,417, 19
the results significantly. We note however, that the Anti-
Kent B. R., Giovanelli R., Haynes M. P., Martin A. M.,
Virgo region is near the Perseus-Pisces supercluster which
Saintonge A., Stierwalt S., Balonek T. J., Brosch N., et
causes a slight over-density in that field at a similar red-
al., 2008, AJ, 136, 713
shift (z ≈0.025). To be sure that over-densities in all three
KomatsuE.,DunkleyJ.,NoltaM.R.,BennettC.L.,Gold
fields at this redshift were not biasing our results, we cut
B.,HinshawG.,JarosikN.,LarsonD.,etal.,2009,ApJS,
the galaxies with redshifts between ∼ 0.02 and 0.03 out of
180, 330
thesamples and recalculated thecorrelation functions. The
Landy S. D.,Szalay A. S., 1993, ApJ, 412, 64
results are also shown in Figure 4 and it is clear that the
Limber D.N., 1954, ApJ,119, 655
correlation functionswithandwithout theredshift cutsare
Loan A.J.,WallJ. V.,LahavO.,1997, MNRAS,286, 994
completely consistent within theuncertainties.
LuptonR.,1993, Statistics intheory andpractice. Prince-
Asanadditionalcheckontheeffectofthesmallfieldson
ton Univ Pr
themeasuredclusteringstrength,wecalculatedtheintegral
MeyerM.J.,ZwaanM.A.,WebsterR.L.,BrownM.J.I.,
constraint (Peebles 1980; Ratcliffe et al. 1998) for a single
Staveley-SmithL., 2007, ApJ, 654, 702
field,andobtainedavalueof0.145whichindicatesaneffect
MeyerM.J.,Zwaan M.A.,WebsterR.L.,Staveley-Smith
within the uncertainty of the correlation function derived
L., Ryan-Weber E., Drinkwater M. J., Barnes D. G.,
from the ALFALFA data.
Howlett M., et al., 2004, MNRAS,350, 1195
Peacock J. A., DoddsS. J., 1996, MNRAS,280, L19
Peebles P.,1980, Thelarge-scale structureof theuniverse.
5 SUMMARY AND CONCLUSIONS Princeton UnivPr
RatcliffeA.,ShanksT.,ParkerQ.A.,FongR.,1998, MN-
WehavemeasuredtheclusteringofHI-selectedgalaxies us- RAS,296, 173
ing the ALFALFA survey data and compared this with re- Rubin V. C., 1954, Proceedings of the National Academy
sults for HIPASS. Our two methods for determining the
of Science, 40, 541
real-space correlation function agree well and our results Ryan-WeberE. V., 2006, MNRAS,367, 1251
for HIPASS agree with those found by Meyer et al. (2004). SaintongeA.,GiovanelliR.,HaynesM.P.,HoffmanG.L.,
The real-space clustering in ALFALFA appears to be even
Kent B. R., Martin A. M., Stierwalt S., Brosch N., 2008,
lower than in HIPASS, consistent with the idea that AL- AJ,135, 588
FALFA probes galaxies with lower HI-masses that are less Wand M., Jones M., 1995, Kernel smoothing. Chapman
clustered than their high-mass counterparts. Our measure-
and Hall/CRC
ments of high- and low-mass subsamples in ALFALFA do
notprovideevidencetosupportthisideabuttheuncertain-
ties on themeasurements are large.
We have calculated the clustering of dark matter ex-
pected within a ΛCDM model with redshift distributions
of HIPASS and ALFALFA. We then calculated the bias of
ALFALFAsourcesovertherange1−10◦,findingavalueof