HerMES: A search for high-redshift dusty galaxies in the HerMES Large Mode Survey - Catalogue, number counts and early results

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by  V. Asboth

MNRAS000,1–14(2016) Preprint27July2016 CompiledusingMNRASLATEXstylefilev3.0
HerMES: A search for high-redshift dusty galaxies in the
HerMES Large Mode Survey – Catalogue, number counts
and early results
V. Asboth1, A. Conley2, J. Sayers3, M. B´ethermin4, S. C. Chapman5,
D. L. Clements6, A. Cooray3,7, H. Dannerbauer8, D. Farrah9, J. Glenn2,
S. R. Golwala3, M. Halpern1, E. Ibar10, R. J. Ivison11,4, P. R. Maloney2,
6
1 R. Marques-Chaves13,14, P. I. Martinez-Navajas13,14, S. J. Oliver12,
0
2 I. P´erez-Fournon13,14, D. A. Riechers15, M. Rowan-Robinson6, Douglas Scott1,

l
u S. R. Siegel3, J. D. Vieira16, M. Viero17, L. Wang18,19, J. Wardlow20,21, J. Wheeler2
J
1Department of Physics & Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T-1Z1, Canada
6 2Center for Astrophysics and Space Astronomy 389-UCB, University of Colorado, Boulder, CO 80309, USA
2 3Division of Physics, Math, and Astronomy, California Institute of Technology, 1200 East California Blvd, Pasadena, CA 91125, USA
4European Southern Observatory, Karl-Schwarzschild-Strasse 2, 85748 Garching, Germany
] 5Department of Physics and Atmospheric Science, Dalhousie University, 6310 Coburg Road, Halifax, NS B3H 4R2, Canada
A
6Astrophysics Group, Imperial College, Blackett Laboratory, Prince Consort Road, London, SW7 2AZ, UK
G 7Center for Cosmology, Department of Physics and Astronomy, University of California, Irvine, CA 92697, USA
8Universit¨at Wien, Institut fu¨r Astrophysik, Tu¨rkenschanzstrasse 17, 1180 Wien, Austria
.
h 9Department of Physics, Virginia Tech, Blacksburg, VA 24061, USA
p 10Instituto de F´ısica y Astronom´ıa, Universidad de Valpara´ıso, Avda. Gran Bretan˜a 1111, Valpara´ıso, Chile
- 11Institute for Astronomy, Royal Observatory Edinburgh, Blackford Hill, Edinburgh, EH9 3HJ, UK
o
12Astronomy Centre, Department of Physics & Astronomy, University of Sussex, Brighton BN1 9QH, UK
r
t 13Instituto de Astrof´ısica de Canarias, E-38205 La Laguna, Tenerife, Spain
s 14Universidad de La Laguna, Departamento de Astrof´ısica, E-38206 La Laguna, Tenerife, Spain
a
15Department of Astronomy, Cornell University, Space Sciences Building, Ithaca, NY 14853, USA
[
16Department of Astronomy and Department of Physics, University of Illinois, 1002 West Green St., Urbana, IL 61801
2 17Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, 382 Via Pueblo Mall, Stanford, CA 94305, USA
v 18SRON Netherlands Institute for Space Research, Landleven 12, 9747 AD, Groningen, The Netherlands
5 19Institute for Computational Cosmology, Department of Physics, University of Durham, South Road, Durham, DH1 3LE, UK
6 20Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen, Denmark
6 21Centre for Extragalactic Astronomy, Department of Physics, Durham University, South Road, Durham, DH1 3LE, UK
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1. 27July2016
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1 ABSTRACT
: Selecting sources with rising flux densities towards longer wavelengths from Her-
v schel/SPIRE maps is an efficient way to produce a catalogue rich in high-redshift
i
X (z > 4) dusty star-forming galaxies. The effectiveness of this approach has already
been confirmed by spectroscopic follow-up observations, but the previously available
r
a catalogues made this way are limited by small survey areas. Here we apply a map-
based search method to 274deg2 of the HerMES Large Mode Survey (HeLMS) and
create a catalogue of 477 objects with SPIRE flux densities S >S >S and a
500 350 250
5σ cut-off S >52mJy. From this catalogue we determine that the total number of
500
these“red”sources is at least an order of magnitude higher than predicted by galaxy
evolution models. These results are in agreement with previous findings in smaller
HerMES fields; however, due to our significantly larger sample size we are also able
to investigate the shape of the red source counts for the first time. We have obtained
spectroscopic redshift measurements for two of our sources using the Atacama Large
Millimeter/submillimeter Array (ALMA). The redshifts z = 5.1 and z = 3.8 confirm
that with our selection method we can indeed find high-redshift dusty star-forming
galaxies.
Key words: galaxies: high-redshift – galaxies: evolution – galaxies: starburst – sub-
millimetre: galaxies – infrared:galaxies
(cid:13)c 2016TheAuthors
2 V. Asboth et al.
1 INTRODUCTION then the number density of such galaxies poses a challenge
to our current knowledge about galaxy evolution.
Akeygoalinstudyingtheformationandevolutionofgalax-
In this paper, as a continuation of the programme
ies is to understand how and when they formed the stars
started by Dowell et al. (2014), we use a similar map-based
they contain. Although stars emit most of their radiation
search technique to create a large sample of 500-µm-riser
at UV and optical wavelengths, it has been shown that
galaxies by analysing a new field in the HerMES survey.
almost half of the extragalactic background radiation is
Theinstrumentalnoiseinthismapishigherthanthenoise
made up of starlight absorbed and reprocessed by dust and
in any of the previously studied HerMES fields; however,
re-emitted at far-infrared and submillimetre wavelengths
the observed area is much larger than before; and therefore
(Puget et al. 1996; Fixsen et al. 1998; Devlin et al. 2009;
wefindastatisticallysignificantsampleofbrighterobjects,
Leiton et al. 2015). In the local Universe dusty starbursts
including some strongly-lensed galaxies with flux densities
with extreme infrared luminosities (LIR>1012L(cid:12)) are very aboveS500 =100mJyasdescribedbyNegrelloetal.(2010),
rare, and contribute only a small fraction to the total star
Paciga et al. (2009), Wardlow et al. (2013), Nayyeri et al.
formation rate density. After the discovery of a higher red-
(2016) and others.
shift dusty galaxy population at submillimetre wavelengths
In Section 2 we describe the observations of our new
withSCUBA(Smailetal.1997;Hughesetal.1998;Barger
field. In Section 3 we present the steps used to create the
etal.1998)itbecameclearthatdustystarformationplaysa
catalogue.InSection4wedeterminetherawnumbercounts
significantroleintheevolutionofgalaxiesatearlierepochs.
of our objects, examine biases in our source selection using
The contribution of the emission of dusty star-forming MonteCarlosimulations,andcompareourresultstodiffer-
galaxies to the infrared luminosity density at different ent models. In Section 5 we discuss the colour distribution,
epochshasalreadybeeninvestigateduptoz(cid:46)4(e.g.Grup- SEDfitsandapparenttemperaturedistributionofoursam-
pioni et al. 2013). While several z>4 luminous dusty star- ple and in Section 6 we present results from follow-up ob-
forminggalaxieshavebeendetected(e.g.Daddietal.2009; servations of a sub-sample of our objects with ALMA and
Coppin et al. 2009; Riechers et al. 2010; Capak et al. 2011; CSO/MUSIC.
Walter et al. 2012; Riechers et al. 2013; Weiß et al. 2013;
Vieiraetal.2013),theirroleinthestellarmassbuild-upat
thesehighredshiftsisstillunknown.Asofnow,onlyasmall
setofobservedfieldshavesufficientancillarydatacoverage,
limitingthesamplesavailablewithknownphotometricred-
shift distributions. Additionally, dusty galaxies at z>4 are
oftenundetectableatshorterwavelengths,thusthedetermi-
nation of the photometric redshifts becomes uncertain. To
2 OBSERVATIONS
study the contribution of the high-redshift dusty galaxies
to the star formation rate density we need a way to select The HerMES Large Mode Survey (HeLMS) consists of a
large samples of z>4 objects based on their properties in largeareashallowobservationofanequatorialfieldatwave-
the available far-infrared/submillimetre data sets alone. lengthsof250,350and500µm,obtainedusingtheSpectral
Dowelletal.(2014)constructedasampleofpotentially and Photometric Imaging Receiver (SPIRE, Griffin et al.
z>4 galaxies selected from 21deg2 of data from the Her- 2010)aboardtheHerschelSpaceObservatory (Pilbrattetal.
schel Multi-tiered Extragalactic Survey (HerMES, Oliver 2010). HeLMS is an extension of HerMES (Oliver et al.
et al. 2012) at wavelengths of 250, 350 and 500µm. The 2012), a“wedding cake”type survey containing small and
spectral energy distribution of local ultraluminous dusty deep maps and larger shallower observations of different
star-forming galaxies typically peaks at rest-frame wave- fields. HeLMS covers about 302deg2 of the sky, making it
lengthsλ∼100µm.Atz(cid:38)4thispeakshiftstowavelengths the largest area observed in the HerMES survey.
λ (cid:38) 500µm, thus all three SPIRE bands sample the short The HeLMS field spans 23h14m < RA < 1h16m and
wavelength side of the SED peak of such high-z objects, −9◦<Dec<+9◦, an equatorial region with low cirrus con-
where the flux densities are increasing with λ. We note tamination. It was designed to have a large overlap with
that not all high-redshift galaxies have red SPIRE colours, the Sloan Digital Sky Survey’s Stripe 82 field (Abazajian
but this method provides a way to select predominantly et al. 2009), one of the most highly observed areas of the
z>4 objects based on their SPIRE flux densities alone. sky, with extensive multi-wavelength ancillary data cover-
Dowell et al. (2014) used a map-based search method to age. The equatorial area has the advantage that it can be
find sources with rising flux densities towards longer wave- observedfromalmostanyground-basedtelescopesiteinthe
lengths (S500>S350>S250). Follow-up observations proved world.
that many of these sources are indeed at z>4 (Riechers The HeLMS field was observed with the telescope op-
et al. in prep.), and this analysis resulted in the detection erating in fast-scan mode (60 arcsec s−1 scanspeed). The
of the z=6.34 source HFLS3 (Riechers et al. 2013; Robson observations were repeated in two nearly orthogonal scan
et al. 2014; Cooray et al. 2014), the highest redshift dusty directions, and these two data-sets are co-added during the
starburst galaxy found to date, forming stars at a rate of map-making process. Having only two scans in each part of
severalthousandsolarmassesperyear.Dowelletal.(2014) the map gives shallower coverage than the deepest SPIRE
foundanexcessofthese“500-µm-riser”or“red”objectscom- maps.However,thenoiseisstillonlyafewtimeshigherthan
pared to available galaxy evolution model predictions, and the confusion level, and the large area of the survey allows
if the 10 or so red sources with spectroscopically confirmed ustofindmoreoftherareobjectscontributingtothesteep
high redshifts are representative of the whole population, bright end of the number counts.
MNRAS000,1–14(2016)
High-redshift dusty galaxies in HeLMS 3
3 CATALOGUE CREATION Fourier space after we cross-correlate our signal S with our
filter F is
WeuseasimilartechniquetofindredsourcesintheHeLMS
field as the map-based search method described in Dowell
et al. (2014). In this method, instead of matching sources SNR= ∑kFˆkTSˆk . (1)
fmouapnsdtiondthepeesnadmenetlryesoaltuteiaocnh, twhaenvelweengctohm, bwineesomuoroothbsearl-l (cid:16)∑k|FˆkTNˆk|2(cid:17)1/2
vations at different wavelengths and use the information in Here N is the total noise, including instrumental noise and
the maps directly to find red sources. As a modification to confusion, the hats denote the discrete Fourier transforms
the technique described in Dowell et al. (2014), we use a ofourvariables,the“T”superscriptreferstoatransposeof
point source-matched filter instead of a Gaussian kernel to ourfilter,andtheindexkcorrespondstocomponentsinthe
reduce the confusion noise in the smoothed maps. spatial frequency domain. We can derive the optimal filter
Themap-basedsearchmethodisbettersuitedforfind- by finding F for which
ing red sources than catalogue based techniques. The cur-
∂(SNR)
rently available catalogue of HeLMS sources (Clarke et al., =0. (2)
∂FˆT
in prep.) uses the positions of the galaxies detected in the j
250µmmapasapriortoextractthefluxdensitiesatlonger The resulting filter is
winagv.eTlehnugsthths,isindaotradesrettoisrneodtuocpettihmealefffoerctfisnodfinsgouorucretbylpeincda-l FˆT= Sˆj (cid:32)∑k|FˆkTNˆk|2(cid:33), (3)
red sources, since we expect these 500-µm-riser galaxies to j |Nˆj|2 ∑kFˆkTSˆk
havelowsignal-to-noiseratiointhe250µmmaps,andhence
manyofthemcouldgoundetectedinthiscatalogue.Onthe whereNjrepresentsthetotalnoiseateachfrequencycompo-
nent j.Whiletheinstrumentalnoiseiswhiteanditsvalueis
otherhand,theDowelletal.(2014)samplealreadydemon-
constant at all frequencies, the power spectrum of the con-
strated that with a map-based method we can find sources
fusion noise will have a similar shape to the point spread
that are not detected in various SPIRE catalogues that use
function, since confusion arises from point sources in the
shorter wavelength priors.
same beam. The shape of our matched filter can be seen in
Fig. 2, compared to the 500µm beam shape and the final
source profile after applying the filter to our maps. Due to
the smaller width of our filter, our final resolution will be
closer to the original resolution of the 500µm map than in
3.1 Maps
Dowelletal.(2014),andthiscanhelpreducesourceblend-
We use the SMAP/SHIM iterative map-maker (Levenson ing effects for nearby objects (see an example in Fig. 3).
et al. 2010) with the modifications described in Viero et al. Afterfindingtheoptimalmatchedfilterforthe500µm
(2013) to create our maps. The nominal pixel sizes at 250, map, we need to construct the smoothing kernels K250,350
350 and 500µm are 6(cid:48)(cid:48), 8.333(cid:48)(cid:48) and 12(cid:48)(cid:48), respectively, to that create the same effective source shape at 250 and
match one third of the full-width half-maximum (FWHM) 350µm that we measure in the smoothed 500µm map.
of the beam in each band (18(cid:48)(cid:48), 25(cid:48)(cid:48), 36(cid:48)(cid:48)). Since we want to First we convolve the 500µm beam (a Gaussian with 35.3(cid:48)(cid:48)
combine our observations, we create all three of our maps FWHM) with the matched filter to find the final source
with matching pixel sizes of 6(cid:48)(cid:48) instead. shapeinoursmoothedmaps.IfP250,350 denotesthenominal
We discard the edges of the map, where the telescope beamshapesat250and350µmandPmf500 isthematched-
turned around between scans and the data are not cross- filtered source-shape at 500µm, then we can find K250,350
linked.Thisareaistoonoisyandthecoverageistoosparse from the convolution
to reliably estimate the fluxes of our objects. Similarly, we P250,350⊗K250,350=Pmf500. (4)
discardasmallregioninthemiddleofthemap,wherepartof
oneoftheoverlappingscanshadtoberemovedduetostray Thus the smoothing kernels are determined by taking the
light in the telescope. The large-scale cirrus background is inverse Fourier transformation of the Fourier-space ratio of
subtracted during the source-finding method, but there is the final and initial beam shapes.
a “seagull-shaped” area in the middle of the maps, where Before filtering the maps, we subtract a local back-
the cirrus is too strong to be easily removed and the flux ground constructed by smoothing our maps with a 2D me-
estimationsarebiasedhigh,sowemaskthisregionmanually dian boxcar filter on a 3(cid:48) scale, to remove any large-scale
(see Fig. 1). The total remaining area that we use in our cirrus fluctuations that might otherwise affect our flux esti-
analysis is 273.9deg2. mation.TheSPIREmapscontainanerrorextension,which
isanoutputof themapmaker.Eachpixelinthiserrormap
contains the standard deviation of the flux density values
fromthetime-ordereddetectordatathatareprojectedonto
that given pixel. We use the inverse-variance values calcu-
latedfromtheseerrorvaluesasweightsforeachpixelwhen
3.2 Matched filter
wefilterourdatawiththematchingkernels.Sincethemap-
To create maps with matching resolution we use an opti- making pipeline does not correct for the effects of pixeliza-
mal filter that maximizes the signal-to-noise ratio in a map tion, we create our filters on an oversampled grid, and then
with non-negligible confusion noise. This filter is described rebinthemtoourfinalpixelsize.Wealsoapplyourfiltersto
indetailinChapinetal.(2011).Thesignal-to-noiseratioin ourerrormapstofindthetypicalinstrumentalnoisevalues
MNRAS000,1–14(2016)
4 V. Asboth et al.
Figure1.GreyscaleimageoftheHeLMS250µmmapwithsolidlinesshowingtheregionweuseinouranalysis.Theobservedareaspans
about30◦ inRAand18◦ inDec.Wediscardtheedgesofthemaps,wherethedatalackoverlappingscans,andwealsodiscardasmaller
regioninthemiddlewherepartofthescanhadtoberemovedandourcoverageissparse.Weadditionallymaskouta“seagull-shaped”
regionofstrongGalacticcirrusemission.Thecirrusinthisstructurecannotbeeasilyremovedandbiasesourfluxestimationofsources.
Thetotalareaoftheremainingdatasetafterapplyingthemaskis273.9deg2.
.
Figure 3.Comparisonofthematched-resolutionmapswhenus-
ing different smoothing kernels. On the left, we show a 13(cid:48)×13(cid:48)
cut-outimagefromour250µmmapsmoothedwiththematched
filterdescribedinSection3.2,whileontherightweusedaGaus-
sianfilter.Inadditiontoreducingtheconfusionnoisefromunre-
solvedfaintsourcesinourtelescopebeam,thematchedfilteralso
reducesblendingeffectsbetweenneighbouringbrightsources.
3.3 Difference map
Figure2.Shapeofthematchedfilterat500µm,comparedtothe
Gaussianpointspreadfunction(PSF)andthefinalsourceprofile Because the sources responsible for the confusion noise in
in the smoothed maps. The filter used for smoothing our maps themapsemitatallthreeSPIREwavelengths,onecanpro-
has a smaller full-width half-maximum than the beam, so the duceadifferencemapthathasasubstantiallyreducedcon-
resolutionofourmapsaftersmoothingisclosetotheunsmoothed fusion limit. It will be much more effective to search for
resolution.
bright 500µm sources in such a difference map than in the
raw 500µm flux maps. Dowell et al. (2014) found that the
difference
(cid:112)
D= 1−k2M500−kM250 (5)
in our pixels after smoothing. We test this filtering method
byinjectingfakesourceswithknownfluxdensityvaluesinto reduces confusion, while red sources remain bright in the D
our raw maps, and find no significant bias in the recovered map. While the confusion noise is strongly correlated be-
flux distribution after subtracting the background and ap- tween the SPIRE bands, the instrumental noise is uncorre-
plying our filter. lated between maps, and the arbitrary normalization was
MNRAS000,1–14(2016)
High-redshift dusty galaxies in HeLMS 5
Table 1.Noiselevelsinthemaps cataloguedecreasesfrom94%atD>4σD to77%ifweapply
a D>3σD cut.
Toselectredsourcesfromthelistofpeaksfoundinthe
σtot σinst σconf
[mJy] [mJy] [mJy] D-map, we simply require that S500>S350>S250. However,
to evaluate this we need to extract the actual flux densi-
250µm 15.61 7.56 13.66 ties from our single wavelength maps at these D-peak po-
350µm 12.88 6.33 11.21
sitions. It is not trivial to determine if it is optimal to use
500µm 10.45 7.77 6.98
our smoothed maps to measure these values or to go back
D 8.54 7.75 3.50
to the nominal resolution maps and find the sources there.
Fromsimulationsweknowthatwehaveatypicalpositional
Note.–The1σ total,instrumentalandconfusionnoiselevels
uncertainty of 6(cid:48)(cid:48), so extracting the fluxes at the precise D
inoursmoothed250,350and500µmmapsandinthedifference
map. position biases our flux estimation at 250 and 350µm. To
addressthiswecouldre-fitourpeaksineachofthesmoothed
maps to find the actual peak position in each band and ex-
chosen so that the instrumental noise properties of the dif- tract the fluxes there. However, a typical red source has an
ference map would not change if the noise values in the S500/S350fluxdensityratiothatiscloseto1,andhenceadja-
original maps are comparable. The coefficients were opti- centsourcesoftenboostour350µmfluxdensityaboveS500,
mized by investigating the efficiency of recovering artificial even if in the nominal maps we clearly detect our source
red sources injected into simulated sky maps. Dowell et al. as a red source. This is an important issue at the bright
(2014)demonstratedthatthevaluek=0.392workswellem- end, where the source counts decrease rapidly, and even in
piricallytomaximizeD/σ inthemaps.Theyalsoexper- ourverylargeareafieldweexpectonlytofindahandfulof
conf
imented with creating linear combinations using all three suchobjects.Aftercarefulconsideration,wedecidedthatfor
maps, but they found that including a 350µm term does thislaststepitisbettertomeasurethefluxesfromtheless
not improve the efficiency of the source selection. We find confused nominal resolution maps, but instead of perform-
that this same choice of coefficients also works well for our ing photometry at the measured D-map position, we find
HeLMS maps. Our final difference map is constructed as the best-fit source after taking into account our positional
uncertainty.
D=0.92M500−0.392M250. (6) To achieve this we move around our D-map peak posi-
Wealsocombinetheerrormapsanddeterminetheresulting tioninsub-pixelsteps,allowingthesearchradiustochange,
instrumental noise (σ ) levels. After measuring the total corresponding to our typical uncertainty, and we calculate
instr
variance(σ2 )ofourmapwecalculatetheconfusionnoise thePearsoncorrelationcoefficientrbetweenourdatad and
total
(σ ) in our final map as the beam shape P at each position by
conf
Npixels
(cid:113) ∑ (di−d¯)(Pi−P¯)
σconf= σt2otal−σi2nstr. (7) r= i=1 . (8)
The noise levels in our smoothed maps and the difference (cid:34)Np∑ixels(d −d¯)2(cid:35)1/2(cid:34)Np∑ixels(P−P¯)2(cid:35)1/2
i i
map are listed in Table 1. It is clearly seen that the confu-
i=1 i=1
sionnoiseinthedifferencemapisreducedcomparedtothe
We pick the position where the correlation is the largest,
confusion levels in the single band maps.
andweextractthefluxdensityatthispositionusinginverse
variance weighting:
3.4 Source extraction Npixels
∑ diPi/σi2
Tofindredsourcesinourmapswefirstsearchforthebright- S= i=1 . (9)
estpeaksinourminimumvariancedifferencemapandthen Npixels
weselectthe500-µm-riserobjectsfromtheresultinglist.We ∑ Pi2/σi2
applyalocal-maximasearchalgorithmtoourD map,which i=1
findsthepositionsofthepixelsthathavegreatervaluesthan We test the validity of this method by injecting arti-
their eight adjacent pixels. We create a list of these peaks ficial sources with known flux densities in the raw maps,
with a cut-off at D=34mJy which corresponds to 4σtot,D and we run our source extraction pipeline on these maps.
in the difference map. This cut is determined by simulating Wefindthatthismethodreducesthebiasduetopositional
the number of false-detections in the SPIRE maps. We cre- uncertainties. However we note that this method will bias
ate simulated maps from mock catalogues drawn from the our flux estimation at 250 and 350µm wavelengths if our
B´etherminetal.(2012b)model,butweremoveanyintrinsi- sources clearly break up into multiple components in these
callyredsourcesfromthesecatalogues.Thenwerunourred- higherresolutionmaps.Theonlyrequirementweimposeon
source detection pipeline on these simulated maps, and we ourcatalogueisthateachobjectisdetectedasapointsource
measurethenumberofobjectsthatwedetectasaredsource at the 500µm resolution and appears red in the maps, but
inthefinalmaps.IfourrealcataloguecontainsNcat sources we do not require a clear high signal-to-noise detection in
andinasimulatedmapwedetectN intrinsicallynon-red 250and350µminordernottobiasagainstthereddestob-
false
objects with red measured colours, then we can determine jects.Thismeansthatsourceblendingwillbeanissueinour
the purity of our catalogue as p=(Ncat−Nfalse)/Ncat. Our catalogue, and we will discuss the effects of blending in the
simulations show that the purity of our HeLMS red source following section. We include the correlation values in our
MNRAS000,1–14(2016)
6 V. Asboth et al.
Table 2.Listofthetenbrightesthigh-redshiftdustygalaxycandidatesinHeLMS.
SourceName RA Dec S r S r S r
250 250 350 350 500 500
[deg] [deg] [mJy] [mJy] [mJy]
HELMS RED 1 11.0414 1.3063 108.1±6.9 0.89 166.5±6.0 0.89 191.8±8.2 0.90
HELMS RED 2 13.2455 6.2219 68.2±6.0 0.77 111.6±5.9 0.93 131.7±6.9 0.94
HELMS RED 3 9.8731 0.4067 140.8±6.5 0.93 152.6±6.3 0.88 162.1±7.3 0.93
HELMS RED 4 5.5867 −1.9224 62.2±6.1 0.83 104.1±5.8 0.87 116.4±6.6 0.89
HELMS RED 5 12.6982 6.9556 20.8±6.0 0.46 68.2±6.4 0.71 112.0±6.8 0.80
HELMS RED 6 15.2248 3.0563 50.1±6.8 0.72 83.3±6.1 0.88 96.1±7.8 0.83
HELMS RED 7 9.5584 −0.3806 73.4±5.6 0.79 119.0±6.0 0.89 122.9±6.7 0.87
HELMS RED 8 354.5083 −1.3186 33.6±6.5 0.56 53.8±6.1 0.69 90.9±7.6 0.82
HELMS RED 9 6.8253 2.6629 65.2±5.9 0.79 76.4±5.7 0.88 99.3±6.9 0.91
HELMS RED 10 0.7683 2.6865 33.6±5.7 0.67 53.9±6.5 0.77 86.5±7.0 0.86
Note.–Thefullcatalogueof477redsourcesisavailableinthecontributeddatasectionofHeDaM/HerMES
(http://hedam.lam.fr/HerMES/).
catalogue to show how reliable our flux density estimation radiofluxdensitiesbrighterthan1mJy.Wedonotusethese
is in each band. objectsinanyfurtheranalysis.Afterdiscardingtheseradio
Our catalogues could be contaminated by cosmic ray quasars, our final catalogue contains 477 red sources with
hitsorotherspikesinthedetectortimestreamsthatarenot S500>S350>S250, S500 >52mJy and D>34mJy. The po-
properlyremovedduringdataprocessing.Aspikeleftinthe sitions, measured SPIRE flux densities and the r Pearson
500µm array data would mimic a 250µm dropout source. correlation coefficients (see equation 8) of our ten brightest
DuringtheiterativemapmakingprocessforordinarySPIRE sources are listed in Table 2. The full catalogue of 477 red
data, these residual isolated spikes are recognized as out- sourcesisavailableonlineinthecontributeddatasectionof
liers among the samples associated with a given pixel, and HeDaM/HerMES (http://hedam.lam.fr/HerMES/).
areremovedfromthedata(Vieroetal.2013).However,the
HeLMS maps are sparsely sampled and there may be too
few samples near a given pixel for this recognition proce-
dure to be reliable. The result is a“hot”pixel or a stripe of
a few very bright pixels in the map from one array, while
the neighbouring pixels show values consistent with the in-
4 NUMBER COUNTS
strumentalnoiseandnospikeispresentintheotherarrays.
After smoothing the map with our matched filters, these
We measure the raw 500µm differential number counts
corrupted pixels appear like bright sources in the 500µm
of the red sources in our final catalogue. The uncorrected
map.
numbers are shown in Table 3 and plotted in Figure 4.
A common method to detect these objects is to cre- In practice raw number counts need to be corrected for
ate two“jackknife”maps, each from one half of the data. completeness and flux-boosting effects and the expected
The false sources only show up in one of the maps. How- number of false detections needs to be subtracted from the
ever,theveryshallowdepthofourobservationcausesthese binned data in order to infer the underlying true source
half-maps to be even more sparse, and so they contain new distribution. When measuring number counts at a single
artefacts due to the lack of cross-linking. In the case of the wavelength these corrections usually only depend on the
HeLMS observations this method does not reliably remove fluxdensityandsignal-to-noiseratio,andtheyarerelatively
cosmicraysfromthemaps.Insteadweturntoadifferentap- easy to simulate when we are investigating sources with
proach. For each source we compare the raw and smoothed flux densities far above the confusion limit. Our catalogue,
mapsina5×5pixelregionaroundeachsource,anddiscard however, has a more complicated selection function, and
all candidate sources if any pixel shows a large difference, thecorrectionfactorswillalsodependonthecoloursofour
(Sraw−Ssmooth)>5σraw. Most cosmic rays produce outlier sources,withthesecolourshavingaverylargescatterdueto
pixelsalmost10σ awayfromthesmoothedvalues,andthis thelowsignal-to-noiseratioatourshorterwavelengths(see
method works well to discard these false sources. In our fi- error bars in Fig. 7). Additionally, these corrections require
nalcatalogueweadopta5σ cutofS500 >52mJytoprotect ustoassumeanintrinsicshapeforournumbercountsbased
from fainter cosmic rays that this technique may not have on model predictions and previous observations; however,
recognized. due to the small sample sizes the slope of the red source
Flat spectrum radio quasars at z<1 can have similar counts has not been measured before. Here we do not
colours to those of our high-redshift galaxy candidates, but explicitly correct our estimated counts for these biases, but
these objects can be easily identified from available radio instead, in the next subsection, we describe a simulation
surveys.Wecomparedourcataloguetothe21-cmradiocat- where we attempt to predict the most likely shape of our
alogues from the NRAO VLA Sky Survey (NVSS, Condon intrinsic source counts, while taking the biasing effects into
et al. 1998) and the FIRST survey (Becker et al. 1995) and account.
wediscarded17ofoursourcesfromourcataloguethathave
MNRAS000,1–14(2016)
High-redshift dusty galaxies in HeLMS 7
100
Raw”red”counts
Clementsetal.2010
B12mock”red”catalog
10−1 B11mock”red”catalog
redsim.:injected
redsim.:measured
2]
−
deg 10−2
1
−
y
J
m
[
Sd 10−3
N/
d
10−4
10−5
102
S500[mJy]
Figure 4.Raw500µmdifferentialnumbercountsofoursampleof“red”sources.Filledcirclesrepresenttherawredsourcecountswith
1σ Poissonerrorbars,exceptforthehighestthreefluxdensitybins,whereweplotthe95%upperconfidencelimits.Thegreendiamonds
showthetotalHerschel 500µmnumbercountsmeasuredbyClementsetal.(2010).Thedottedbluelinerepresentsmeasuredrawcounts
froma1500deg2simulation,whereartificialredsourcesweredrawnfromanintrinsicredsourcedistributionshownbythesolidblueline
andthesesourceswereinjectedintosimulatedsky-mapscontaininginstrumentalandconfusionnoise.Thesesimulationsthataccountfor
blending, Eddington bias, false detections and completeness are described in Section 4.1. The light-blue stars connected with a dashed
line are binned data from creating a simulated catalogue based on the B´ethermin et al. (2011) model, and selecting objects with the
samecolourcriteriaaswedoforourcatalogue.Theredcrossesconnectedwithadashedlineshowthenumbercountsfromasimilarly
selectedmockcataloguedrawnfromtheB´etherminetal.(2012b)model.ThemodelcomparisonsarediscussedinSection4.2.
Table 3.Raw500µmnumbercounts. length. There will be closely adjacent sources that appear
blendedintooneobjectat500µmandresolvedintoseveral
at350or250µm.Ifoneofthesesourcesisveryred,butnot
S S S N dN/dS
min max mean bin
[mJy] [mJy] [mJy] [×10−4 mJy−1deg−2] bright enough for catalogue inclusion, and the other(s) nei-
therverybrightnorveryred,thesummaywellappearboth
52.0 60.2 56.1 225 998.6±66.6
bright enough and red enough for inclusion. This would re-
60.2 69.8 65.0 154 590.1±47.5
sultinafairlyredbutslightlyfaintobjectinthecatalogue.
69.8 80.8 75.3 55 181.9±24.5
Examples of this effect are shown in Figure 5.
80.8 93.6 87.2 27 77.1±14.8
93.6 108.4 101.0 9 22.2± 7.4 Ordinarilytheseeffectscanbeestimatedandcorrected
108.4 125.5 117.0 4 8.5± 4.3 using a model or prior knowledge of the shape of the in-
125.5 145.4 135.4 1 1.84+−81..4719 trinsicsourcecounts.However,asdiscussedinDowelletal.
145.4 168.4 156.9 1 1.58+−71..2565 (2014), the existing models appear to under-predict the
168.4 195.0 181.7 1 1.37+6.27
−1.33 abundance of these sources by an order of magnitude or
more,sothisprocedurewillnotbereliable.Instead,wetake
aself-consistentapproach.Weuseexistingsourcemodelsto
4.1 Simulations constructanartificialskywithoutredsources,addapower
law distribution of red sources and vary the terms in the
The raw source counts can provide a biased estimator of
powerlawuntilsearchesoftheartificialmapsreturncounts
the intrinsic source distribution through a number of ef-
that match the raw counts in Fig. 4.
fects. If the intrinsic source counts are a steep function
of source brightness, or the flux uncertainties are large, a We use the B´ethermin et al. (2012b) model to create
larger number of faint sources may appear to satisfy our simulatedmapsatallthreewavelengths.Wediscardanyred
catalogue selection criteria than the number of acceptable sourcesinthesemaps,thenweaddthemeasuredinstrumen-
sources that appear not to. This so-called“Eddington bias” tal noise and we inject artificial red sources into this data
may be present at the cuts where we require S500>52mJy, set. To do this we first draw 500µm flux densities from a
D>34mJy, S500>S350 and S350>S250, and this will affect power-lawdistributionoftheshapedN/dS=N0×S−α,then
the observed slope of the number counts. wefixthecolourratiosofourinjectedobjectstothemedian
Additionally, there may be a bias in our counts that of the colour ratios measured in our catalogue, S500/S250 =
arisesfromthevariationofourangularresolutionwithwave- 1.55 and S500/S350 = 1.12. In Fig. 6 we plot the measured
MNRAS000,1–14(2016)
8 V. Asboth et al.
5
4
0
5
2
S
/ 3
0
0
5
S
2
1
60 80 100 120 140 160 180 200
S500[mJy]
2.5
Figure 5. Example images of sources detected in our catalogue 2.0
0
that appear to be a single object in the 500µm map and in D. 5
3
Fromlefttorightweshow3.5(cid:48)×3.5(cid:48) cut-outimagesatthenom- /S
inal resolution of the 250µm, 350µm and 500µm maps and the 00 1.5
5
difference map, respectively. Example A shows a source that is S
isolated and has a clear counterpart in each band. Example B
1.0
shows an object that clearly breaks up into two sources in the
250µmand350µmmaps,butisblendedat500µm.ExampleC
showsacomplexblend,withnoclearlyidentifiablecounterparts 60 80 100 120 140 160 180 200
thatcouldbeusedfordeblending.
S500[mJy]
colourdistributioninour500µmbins.Thelargestscatteris
inthelowesttwobins,wheremanyoftheobjectswithhigh
Figure 6. Measured SPIRE colours of red sources in our cata-
colour ratios are either blends or have very low signal-to-
logueasafunctionofthe500µmfluxdensity.Theverticallines
noise ratio counterparts at 250 and 350µm. After injecting show the edges of the flux density bins we used to measure the
the simulated population of red sources into the maps, we differentialnumbercounts.ThehorizontallinesatS500/S250=1.55
run our detection pipeline in the same way as we do for andS500/S350=1.12showthemediancoloursofoursample.The
our real maps and we compare the measured counts from sourceswithanomalouslyredcoloursaremostlyblendsorobjects
this simulated data with our raw numbers. In an iterative withoutclear250and350µmdetections.
process we change the input parameters N0 and α until the
output counts are within the uncertainties compared to the
measured raw number counts in our real data. In Fig. 4 we
then select sources the same way as we do for our observed
show the result of a simulation with an area of 1500 deg2.
The blue solid line represents a power law dN/dS=N0×Sα Ssa25m0pcloelo(uS5r0s0).>T5h2esmeJtyw,oDm>od34elmsJdye,scarnibde“trhede”toSt5a00l H>eSr3s5c0h>el
with α =−5.60 and logN0=8.61 and the dotted blue line number counts well, but, as Dowell et al. (2014) already
shows the measured number counts for simulated objects
showed, they both under-predict the number of red sources
with500µmfluxdensitiesdrawnfromthisdistributionand
in the HerMES fields. The resulting counts from these sim-
with fixed colour ratios. The simulations are in good agree-
ulations are plotted in Fig. 4. Comparing these simulated
ment with our observed counts, suggesting that assuming
number counts to our inferred intrinsic source distribution,
a power-law number counts model without a break is ade-
we can see that both of these models indeed under-predict
quate for our purposes. The parameters in our simulation
the number of red sources by at least an order of magni-
have typical uncertainties of ±0.01; changing α and logN0 tude. We note that both of these models are empirical and
by more than this value results in simulated counts outside
arebasedonextrapolationofthepropertiesoflowerredshift
of the ±1σ error bars of our measured red source counts.
starburstgalaxies,suchastheluminosityfunction(B11)or
We note that the actual colours of our objects are not all
thestellarmassfunction(B12),insteadoftheactualphysics
the same, but due to the large uncertainties in the colour
ofthesegalaxies.Asofnow,noneofthemorephysicallymo-
measurementsitisnotstraightforwardtodeterminetheun-
tivatedmodelshavebeenfine-tunedtoproperlydescribethe
derlying colour distribution.
observed Herschel number counts, hence those models are
even less optimal to predict the red counts.
In Fig. 4 we also show the total SPIRE 500µm counts
4.2 Comparison to models
measuredbyClementsetal.(2010),whichisingoodagree-
We compare our observed number counts to mock “red” mentwithotherSPIREtotalnumbercountsmeasurements
catalogues created from the B´ethermin et al. (2011, B11) (Oliver et al. 2010; Glenn et al. 2010; B´ethermin et al.
and B´ethermin et al. (2012b, B12) models. We generate 2012a). Negrello et al. (2010) predicted that the number
1000deg2 simulations from both of these catalogues, and counts of unlensed galaxies at 500µm are rapidly decreas-
MNRAS000,1–14(2016)
High-redshift dusty galaxies in HeLMS 9
ingandreachzeroat>100mJy.Manybrightobjects–that nantlyatz>4withoutsignificantcontaminationfromlower
arenotalocalgalaxyorablazar–withfluxdensitiesabove redshift objects.
S500(cid:39)100mJy are expected to be strongly lensed, and the In Fig. 7 we present the colour-colour plot for our ob-
numbercountsareexpectedtohaveabrighttailduetothis jects. As a comparison we also show the redshift tracks for
lensed population. We have found several red objects with two starburst galaxies, as well as the colours of our two red
bright 500µm flux densities, but their low numbers in our sources that have ALMA redshift measurements and four
flux density bins do not allow us to see an actual depar- z>4galaxiesfromtheDowelletal.(2014)sample.Mostof
turefromthesteeppower-lawshapeofredsourcesatlower thegalaxiesfromtheSPTlensedgalaxysample(Weißetal.
flux densities. We note that six red galaxies in our sam- 2013) that have spectroscopically confirmed z>4 redshifts
ple (HELMS RED 1, 2, 3, 4, 7, 11) are also present in the also have red SPIRE colours. We also show these sources
listofHeLMSlensedgalaxycandidateswithS500>100mJy inourcolour-colourplot.Theerrorbarsonthetwosources
discussed in Nayyeri et al. (2016), and five of our sources with known ALMA redshift are representative of the typi-
(HELMS RED 1, 3, 4, 7 and 23) are part of the nine can- cal errors for the sources in our catalogue. Due to the large
didategravitationally-lenseddustystar-forminggalaxiesde- photometricuncertaintiesthemeasuredcolourshaveavery
tectedusingtheAtacamaCosmologyTelescope(ACT)ata largescatter.Blendingwillalsochangetheobservedcolours.
wavelength of 1.4 mm (Su et al. 2015). Asdiscussedbefore,weincludedtheblendsinourcatalogue,
Our inferred number counts do not show any signif- assuming that they contain at least one red source, and we
icant differences compared with the Dowell et al. (2014) addressedthefluxboostingeffectcausedbyblendinginour
findings,althoughwenowhavemuchbetterstatistics.The number counts simulation. However, when investigating in-
Dowell et al. (2014) sample consisted of red sources with dividual sources in the catalogue we have to be careful how
S500>30mJyandD>24mJy,andtheyfoundthetotalcor- we interpret our flux density values. The correlation values
rected cumulative counts to be 3.3±0.8 sources per deg2. included in the catalogue and also visual inspection of the
If we assume that the shape of our distribution can be de- selected sources in the higher resolution SPIRE bands can
scribed by the power law that we found in Section 4.1, and help identifying sources that have less reliable photometry
that it does not have a break between 30mJy and 52mJy, due to blending effects.
then integrating this power law above 30mJy gives us a Similarly to Dowell et al. (2014) we fit our observed
total number of more than 10 red sources per deg2. How- SPIREfluxdensitieswithanopticallythickmodifiedblack-
ever, this comparison is not straightforward, since many body spectrum:
of those objects at lower 500µm flux densities would be
discarded by the D cut used in the Dowell et al. (2014) Sν =Ω×[1−exp(−(ν/ν0)β)]×Bν(T). (10)
analysis. The D>24mJy cut imposes a maximum limit of HereBν(T)isthePlanckfunction,Ωisthesolidangleofthe
S250<9.2mJyforanobjectwithS500=30mJy(seeEq.6). source, and ν0 is the frequency where the optical depth is
AtS500=40mJytheD>24mJycutgivesanS250<32.6mJy unity.RedshiftingathermalSEDhasasimilareffectonthe
upperlimit,andsincethetypicalS500/S250 ratioofourcat- observedsubmillimetrecoloursaschangingthedusttemper-
alogue is 1.55, using this limit will give a better overlap ature,andusingtheSPIREfluxesalonewecanonlymeasure
between our sample and the Dowell et al. (2014) catalogue. the combination T =T /(1+z). We use the affine in-
obs dust
Integrating our power law for S500>40mJy results in 2.8 variantMCMCcodedescribedinDowelletal.(2014),while
objects per deg2, which is closer to the Dowell et al. (2014) marginalizing over the Gaussian priors β =1.8±0.3 and
result. λ0(1+z)=(1100±400)µm. The resulting observed-frame
temperature distribution for our isolated sources is plotted
in Fig. 8. We also show the distribution of λmax, the ob-
served wavelength where the SED peaks.
The mean observed temperature is (11.03±1.91)K. As
5 COLOURS AND SED FITS
expectedfromhigh-redshiftgalaxies,thisiscoolerthanT
obs
The interpretation of our red colour-selection as leading to for SPIRE-selected galaxies in general (e.g. Amblard et al.
a catalogue rich in high-z galaxies relies on the assump- 2010;Caseyetal.2012).However,wehavetobeverycareful
tion that galaxies with rising flux densities towards longer whencomparingtemperaturevaluesquotedintheliterature.
wavelengths are at high redshifts. Although the tempera- IfweapplyanopticallythinSEDmodel,whereλ0→0,the
ture of starburst galaxies could be rising slightly towards fittedT valuescandecreasebyabout15%,sotheobserved
obs
high z, it does not rise as fast as (1+z), and the observed- temperaturescanhaveadifferentmeaningdependingonthe
frametemperatureT =T /(1+z)dropswithredshift.This specific SED model applied. Using the same SED model as
obs d
behaviour is evident in the catalogue of 25 strongly-lensed Dowell et al. (2014), our observed temperature distribution
galaxies selected at 1.4mm with the South Pole Telescope is similar to their measurements, showing that we select a
(Weiß et al. 2013; Vieira et al. 2013), where the apparent similarpopulationofsourcesinourmaps.Redsourceswith
temperatureisfitbyT =[11.1−0.8(1+z)]K.Thispattern known redshifts have a very warm inferred intrinsic dust
obs
is consistent with examining the spectral energy distribu- temperature.
tions(SEDs)ofseverallowerredshiftdustystarburstgalax- The three sources with spectroscopic redshift estima-
ies, and measuring their expected flux-ratios at the SPIRE tions listed in Dowell et al. (2014), FLS1 (z=4.29), FLS5
wavelengths for different redshifts. As we noted before, not (z=4.44) and LSW20 (z=3.36), have dust temperatures of
all high-z galaxies are 500-µm-risers (e.g. Rowan-Robinson 63K,59Kand48K,respectively,andRiechersetal.(2013)
et al. 2014; Riechers et al. 2014; Smolˇci´c et al. 2015), but quoteT =56Kforthez=6.3sourceHFLS3.Theobserved
d
weassumethatthegalaxiesweselectthiswayarepredomi- temperatures of these sources are 11K, 10K, 9K and 8K,
MNRAS000,1–14(2016)
10 V. Asboth et al.
5.7 Arp220
2.2 Eyelash
2.0
7 5
1.8
0
5
3
S 1.6
/00 z=4.9/3.8? 4.4 4.2
S5 6
1.4 6.3
4
5
1.2 5.2 5.3
4.8
4.5
44..86
4.4
1.0 4 4.3
3
3
0.8
1.0 1.5 2.0 2.5 3.0
S /S
350 250
Figure 7.SPIREcolour-colourplotofredsources.Bluefilledcirclesrepresenttheobjectsinthecurrentcatalogue.Thegreendashed
linerepresentstheredshift-trackforthestarburstgalaxyArp220(Rangwalaetal.2011)andthedottedpurplelineforSMMJ21352–102
(CosmicEyelash,Swinbanketal.2010),withthelabelsonthesecurvesshowingtheredshifts.Redfilledstarsshowtwosourcesfromthe
cataloguediscussedinourpaperthathaveALMAredshiftmeasurements(seeSection6.1).Theseare,fromlefttoright:HELMS RED 31
withz=3.798;andHELMS RED 4withz=5.162.Forthesetwosourcesweplottheerrorsinthecolourmeasurements.Theseuncertainties
are representative of the typical errors of the colours of sources in our catalogue. The black open star symbols represent four sources
fromtheDowelletal.(2014)redsourcesamplethathavespectroscopicredshiftmeasurements.Fromlefttoright:FLS1(z=4.3);FLS5
(z=4.4);LSW102(z=5.3);andFLS3(z=6.3).Greydiamondsrepresentspectroscopicallyconfirmedz>4sourcesfromtheSPTlensed
galaxysample(Weißetal.2013)thatalsohaveredSPIREcolours.
respectively. The observed SED peak wavelength λmax is Atacama Large Millimeter/submillimeter Array (ALMA)
moredirectlyconstrainedbythedatathanT .Forasam- during the Cycle 2 operational phase, to determine their
obs
ple with a similar λmax distribution as the one we measure redshifts. These two objects have photometric redshift es-
(bottom panel in Fig. 8), Dowell et al. (2014) estimated a timates of z =5.27±0.12 and z =5.28±0.27, re-
phot phot
meanphotometricredshiftofz=4.7bydeterminingpriorsof spectively,basedontheSPIREfluxdensitiesandadditional
the rest-frame peak wavelength, based on different compar- millimetre-wave interferometric follow-up observations (the
isonsamples.Whilewedidnotcarryoutasimilaranalysis, photo-zestimationmethodwillbepresentedinClementset
basedonthesimilarselectionfunction,andthesimilarmea- al. in prep.). The observations were carried out in Band 3,
sured λmax and Tobs distribution of our sample, we can as- covering frequencies between 84 and 116GHz, which con-
sumethatourcataloguealsoconsistsofmostlyhigh-redshift tains the redshifted CO rotational lines typically up to the
objects. J=6−5 transition at the expected redshifts.
TheobservedspectraareshowninFig. 9.Inthespec-
trumofHELMS RED 4wedetecttwolines,unambiguously
6 FOLLOW-UP RESULTS identifiedastheCO(5−4)andCO(6−5)transitions.These
correspond to a redshift of z=5.162, which is in very good
Previous follow-up observations of red sources have already
agreement with the photo-z estimate of 5.27. Only a single
shown that we can successfully select high-redshift dusty
stronglineisseeninthespectrumofHELMS RED 31,and
galaxies based on their red SPIRE colours, but to confirm
there are several spectral lines that could fall into this re-
that this is true for our whole sample more observations
gion.Wecandiscardthepossibilitythattheobservedlineis
will be needed. Here we summarize spectroscopic redshift
CO(6−5)orahighertransition,sincethenwewouldalways
measurements with ALMA and millimetre-wave photomet-
detectotherCOrotationallinesintheobservedspectrum.If
ric follow-up measurements with CSO/MUSIC for a sub-
weidentifythedetectedlineastheCO(5−4)transitionthen
sample of the red sources in the HeLMS catalogue.
the redshift of this source is z=4.997 and we would not see
any other lines in the observed spectral range; this is con-
sistentwiththephoto-z estimate.If,ontheotherhand,the
6.1 ALMA spectroscopy
detected line corresponds to the CO(4−3) transition then
Wecarriedoutspectroscopicobservationsfortwoofourred the redshift is z=3.798 and we should be able to detect
sources, HELMS RED 4 and HELMS RED 31, using the a [CI] line at 102.6GHz. There is a possible low signal-to-
MNRAS000,1–14(2016)