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ou An IOP and SISSA journal An IOP and 2017 IOP Publishing Ltd and Sissa Medialab srl Department of Physics, Cornell University,Department Ithaca, of NY Physics 14853, and U.S.A. and Astronomy, University Pittsburgh of Particle Pittsburgh, Physics,3941 Astrophysics, O’Hara and Street, Cosmology Pittsburgh, Center, PADepartment 15260, of U.S.A. Astrophysical Sciences,Princeton, Peyton NJ Hall, 08544, Princeton U.S.A. University, National Institute of Standards andCITA, University Technology, Boulder, of CO Toronto, 60 80305, St. U.S.A. George St., Toronto, ON M5S 3H8, Canada 1 2 3 4 5 c J.R. Stevens, and E.J. Wollack B. Partridge, J. Sievers, J. Dunkley, M. Halpern, J.C. Hill, J. Hubmayr, S. Naess, B.J. Koopman, M.S. Madhavacheril, M.D. Niemack, E. Calabrese,
The ACTPol collaboration F. De Bernardis,
J Detection of the pairwiseSunyaev-Zel’dovich kinematic effect with BOSS DR11 and the AtacamaTelescope Cosmology JCAP03(2017)008 Sub-Department of Astrophysics, University ofKeble Oxford, Road, Oxford, OX1 3RH,SLAC National U.K. Accelerator Laboratory, 2575 Sand Hill Road,Department Menlo of Park, Physics, CA University 94025, of U.S.A. Department Michigan of Ann Physics Arbor, and MI209 Astronomy, University 48109, South of U.S.A. 33rd Pennsylvania, Street,Instituto Philadelphia, de PA 19104, Astrof´ısicaand Centro U.S.A. dePontificia Astro-Ingenier´ıa,Facultad Universidad de Cat´olicade F´ısica, Chile, Av. Vicu˜naMackenna 4860, 7820436 Macul,Miller Santiago, Institute Chile for BasicBerkeley, Research CA in 94720, Science, U.S.A. UniversityAstronomy of Department, California, University of California,University Berkeley, of CA British 94720, Columbia, U.S.A. 6224 Department of Agricultural Physics Road, and Vancouver BC Astronomy, Department V6T of 1Z1, Astronomy Canada andUniversity Astrophysics, Park, The PA Pennsylvania 16802, State U.S.A. University, Institute for Gravitation andUniversity the Park, Cosmos, PA The 16802, Pennsylvania U.S.A. StateDept. University, of Astronomy, Pupin Hall,School Columbia of University, Mathematics, New Statistics York,Durban, NY and 4041, 10027, Computer South U.S.A. Science, Africa UniversityDepartment of of KwaZulu-Natal, Physics, University ofPiazzale Rome Aldo La Moro Sapienza, 5,Dunlap I-00185 Institute, Rome, University Italy of Toronto,Florida 50 State St. University, George Tallahassee, St., FLDepartment Toronto, 32306, ON of U.S.A. M5S3H4, Physics Canada and136 Astronomy, Rutgers Frelinghuysen Road, University, Piscataway, NJUPMC 08854-8019, Univ U.S.A. Paris 06, UMR7095,Stony Institut d’Astrophysique Brook de University, Paris, Stony F-75014, Brook,Joseph Paris, NY France Henry 11794, Laboratories U.S.A. ofPrinceton, Physics, NJ Jadwin 08544, Hall, U.S.A. PrincetonDepartment University, of Physics and Astronomy,School Haverford of College, Chemistry and Haverford, Physics, PA University 19041,National of KwaZulu-Natal, U.S.A. Institute Durban, for 4041, Theoretical South Africa Department Physics of (NITheP), Physics, KZN West node, ChesterWest Durban, Chester, University South of PA Africa 19383, Pennsylvania, U.S.A. NASA Goddard Space Flight8800 Center, Greenbelt Road, Greenbelt, MarylandE-mail: 20771, U.S.A. [email protected] 6 7 8 9 Received July 26, 2016 Revised January 12, 2017 Accepted February 8, 2017 Published March 7, 2017 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 JCAP03(2017)008 1607.02139 CMBR experiments, galaxy surveys, hydrodynamical simulations, Sunyaev- We present a new measurement of the kinematic Sunyaev-Zel’dovich effect using Abstract. data from thescopic Atacama Cosmology Survey (BOSS). Telescope (ACT) Usingmean and pairwise 600 baryon the square momentum Baryon degreesthe associated Oscillation BOSS of with DR11 Spectro- overlapping the Large sky positionsmotions Scale Structure of area, of catalog. 50,000 we halos bright A evaluate containingwell, galaxies non-zero the with the signal in the arises sample from optical galaxies.ing the depth the large-scale The to overall data microwave signalmomentum photon fits amplitude. as scattering an a as We analytical function aevaluation, estimate signal of free the and model parameter galaxy covariance bootstrap matrix determin- separation,signal-to-noise estimates. of estimates using between the microwave 3.6 The sky mean andhow most simulations, the pairwise 4.1 other for jackknife conservative error varying simulation-based determinations galaxyimpact can errors luminosity of lead cuts. several to give possible higher We signal-to-noise systematic discuss thermal values, errors. Sunyaev-Zel’dovich and signal Estimates consider at the of thethose sample the galaxy optical obtained positions depth from are from the broadly the mean consistent average pairwise with momentum signal. Keywords: Zeldovich effect ArXiv ePrint: JCAP03(2017)008 3 4 9 – 1 – ), the kSZ signal is more than an order of magnitude
M 14 10 M > 900 km/s [6]. This analysis modeled the tSZ signal from X-ray data and then ± = 3450 For large clusters ( v 2.1 ACTPol2.2 data3 BOSS-SDSS data3 3.1 Pairwise3.2 momentum estimator and Filtering kSZ3.3 CMB effect5 maps6 Model fitting6 4.1 Contributions to the covariance8 matrix 5.1 ACTPol+ACT5.2 and DR119 redMaPPer 5.3 Null tests 7.1 Overview 7.2 Thermal SZ 12 13 15 16 1 Introduction The kinematic Sunyaev-Zel’dovich effect [1]the (kSZ) peculiar is velocities the of only objectsing known way at ionized to cosmological directly gas distances. measure radiation creates A passing moving a through galaxy near-blackbody it, clusterthe with contain- spectral line-of-sight an velocity distortion amplitude component, proportional in butis to independent the both of hence microwave the the a gas background total temperature. valuablegravity gas between source mass The megaparsec kSZ and of and effect information gigaparsec for scales cosmology, [2–4]. allowingsmaller tests than of the dark typicaltecting energy thermal the and SZ velocity of [5]high individual (tSZ) clusters precision spectral requires distortion along measurementsdetection at at with multiple most of models frequencies wavelengths. and a ofthe De- the peculiar cluster intracluster velocity MACS medium.ter J0717.5+3745, for giving a To a single date, high object the peculiar only comes velocity claimed from for a Bolocam particular observations subclus- of Contents 1 Introduction1 2 Data 3 Analysis 4 Covariance matrix6 5 Results 6 Comparison with the previous7 ACT analysis Optical depth from the thermal SZ signal 8 Systematic effects 9 Discussion 13 15 18 20 JCAP03(2017)008 evidence σ 3 . kSZ detection, σ and 3 σ 9 . detection, showing for the first of overlap with the DR11 release σ 2 2 . – 2 – detection of the mean pairwise kSZ effect. This σ 1 . For lower-mass clusters, the kSZ and tSZ signals are comparable but both signals are A statistical detection of the kSZ effect has also been achieved with a different technique: Here we report a detection of the pairwise kSZ signal using data from two-year maps of A different way to detect the kSZ effect that involves squaring the CMB anisotropy small compared toCosmology the Telescope noise and levelkSZ in SDSS signal current collaborations by microwave madeidentified background estimating the by maps. the their first mean bright Thenonzero statistical pairwise central Atacama mean cluster detection galaxies pairwise in momentum ofof the velocity from the clusters Sloan of a Digital to galaxy sample Skystatistic be clusters of is Survey moving reflects clusters also (SDSS) towards the advantageous [9].at because each slight the The it other, positions tendency is due of of aRecently, clusters, detections to linear any using and the difference the pair most same of attractive other estimatorusing measured have signals, force galaxies been sky from like reported of SDSS temperatures tSZ by [10] gravity. the andthe and Planck dust Dark the collaboration This emission, South Energy Pole Survey average Telescope out. [11].of collaboration using The missing galaxies former from baryons. work has been The used in latter [12] analysis to measure reported the amount a 4 jointly fitted the thermalkSZ and effect kinematic for SZ a signals.dipolar single signature cluster A associated using more with data recent two from analysis merging the [7] subclusters. New mapped IRAM the KID Arrays [8] detecting the a velocity templatecontinuity equation, is then the constructed velocity template frommap is [10, cross-correlated the13]. with the BOSS Schaan CMB etas temperature large-scale al. a (2016) density function [13] of in field particular the measured assuming angular the radius the amplitude around of the the kSZ clusters signal and reported 2 time that the pairwiseuncertainty kSZ is signal properly can taken be into extracted account. using photometric data once the redshift the Atacama Cosmology Telescope Polarimeter (ACTPol)positions experiment, and combined redshifts with from galaxy portion the Baryon of Oscillation the Spectroscopic Survey ACTPol (BOSS). maps The provides deep around 600 deg depending on thenot galaxy require bias redshift constraints. estimateswithout for treating The redshift individual uncertainty. clusters, advantage of which allows this use method of is photometric that data it does of BOSS, which we use to obtain a 4 maps [15] has been implementedforeground-cleaned by CMB Hill et temperature al.WMAP maps [16] data constructed using were publicly filtered, from available squared,Wide-field data. multi-frequency and Infrared cross-correlated Planck Specifically, with Survey and galaxy Explorer measurements (WISE) from [17], the which yielded a 3.8–4.5 of the kSZ using twoused different by Schaan velocity et reconstruction al. methods.CMASS (2016) galaxy The is catalog similar ACTPol from [14] to BOSS the CMBis DR10 one map for based used the on in analysis. this convertingthen The work. galaxy Schaan to It et stellar an was al. mass combined (2016) opticalpotential with estimates approach depth the probe to by total of using massesThese the the for detections cosmological fraction the are baryon of host consistent abundance.complementary free halos with tool It and electrons to the provides investigate pairwise and the another systematic measurements physics the effects. of presented baryon galaxy here clusters profile and and of offer to galaxy understand a potential clusters. is an improvementcomparable over the to initial thelacks detection significance spectroscopic in reported redshifts. Hand in et [11], al. which (2012), uses H12 a hereafter, deeper and optical is data set but JCAP03(2017)008 ◦ and 35 ◦ 5 − of the coadded map. 2 from the BOSS survey [21]. 15 mJy are discarded. In [9], 1 > – 3 – with a white noise level that ranges from 10 (for the 2 arcmin. Figure1 shows the ACTPol data used in this paper with · K µ . ACTPol map used for this analysis, showing the overlap with the 67938 DR11 sources In section2 we describe the ACT, ACTPol, and BOSS data used for this analysis. http://data.sdss3.org/sas/dr11/boss/lss/. 1 near the celestial equatorswas observed and in the 2014 regionby by ACTPol encompassing and ACTPol D5 covers [20]. about and 700 This D6, deg map called is deep56, the which deepest patch of the2.2 sky observed BOSS-SDSS data We use the publicWe Large measure Scale a temperature Structureassume signal (LSS) that from DR11 the our catalog CMB mostsources maps luminous within in of 5 the these arcmin direction galaxies radius of coincide from these with point objects the sources and center with of flux clusters. BOSS Section3 summarizes the pairwise statisticmodel approach, fitting the map technique filtering thatthe method significance we used of use and the the tomate detection. estimate the In the covariance section4 matrix clustersection5 we of opticalwe describe the show depths three data: the and different results simulatedresults of approaches to reported CMB our to by quantify analysis maps, H12, esti- and discussing bootstrap in thevariance and matrix section6 differences in estimationcompare jackknife. the method. with In map the filtering Finally,the previous in approach tSZ ACT and component section in and VI the use we the co- the use results optical the of depth same hydrodynamical independent simulations dataset of to tothe the obtain extract tSZ one an obtained estimate combined of with by the hydrodynamicalcal kSZ. simulations We depth can show and be that thereby used measurements convert to of the estimate pairwise the momentum average opti- measurement into a2 pairwise velocity. Data 2.1 ACTPol data The CMB data usedseasons for this of analysis ACT are observationsreceiver. the and combination of The two sky seasons ACTthe maps of data sky, at with 148 nighttime and partial GHz observations thefor overlap. from with the ACTPol two The the first data ACT ACTPol season data useddeep used (2013) patches in here of labeled this are ACTPol D5 presented are and work in D6, presented cover centered [18], in different respectively while [19]. at areas the right This of data ascensions coadded (RA) map includes the the sources from the BOSS catalog, which overlaps with about 600 deg Figure 1 (green dots). The longmeasurement strip of across the the pairwise field kSZ is effect the in region observed [9]. by ACT that was used for the first deepest regions) to 20 JCAP03(2017)008 56 for . software [22]. The correct k 20. > s . 48 for the DR11 catalog and 0 . λ
L 12 10 × – 4 – 25 . to 1
L 8 10 × 5 20 after including a correction factor accounting for masked clusters . > 8 with an average reshift of 0 s . λ 05 to 0 . ACT map was combined with a selection of the DR9 sources. For comparison we 2 . Comparison between the DR9 selection used in [9] (green) for the 220 sq. deg ACT region For the coadded map we find 67938 sources in the same redshift range from the DR11 We also analyze the redMaPPer [23–25] SDSS DR8 catalog of galaxy clusters. The catalog. The luminosities ofreddened these magnitudes sources are and calculated applying based a on K-correction their r-band using Petrosian the de- the DR9 selection. repeat our analysis usingroughly the the same DR9 number selection of objects used26357 in for in the the ACT-only that map, DR11 paper. i.e. LSS(see 27291 catalog, These for although the figure2), two the DR9 catalogs redshift selection with and distributionsrange contain are the from significantly different DR9 0 selection having more high-redshift objects. The redshifts Figure 2 and the DR11 LSS26357 catalog galaxies overlapping respectively. the The sameACT+ACTPol blue map area histogram that (yellow). shows the has Thethe DR11 about two distribution redshift catalogs 600 for distribution contain sq the in 27291 deg redMaPPer the and sources of coadded with overlap richness with the DR11a catalog. 220 deg The light blue is luminosities range from 1 and incompleteness. See section 5.2 for more details. 3 Analysis We implement aand pairwise BOSS statistic DR9 followingsignificantly the data from approach in [9]. successfully [9] As employed for described on the below, ACT first we kSZ use aperture measurement, photometry although to our filter analysis the differs CMB redMaPPer algorithm providesat a the more precise expensephotometrically. determination of We of a find the smaller2242 31600 have center clusters a sample richness of in of the the objects cluster, coadded and ACTPol-ACT map, with of some which of only the redshifts estimated JCAP03(2017)008 ij is c r (3.1) (3.2) (3.3) (3.4) . ij c ))] ) j is the comoving separation can be still estimated from θ z ] ) ( r 2 z p θ z cos T ij /σ ] j c 2 2 z in equation (3.1). The factor r − ) ) i i j j r r z r /σ T 2 2 · along the line of sight: ) − = j i − )(1 + cos (∆ 2 ij | z z j p 2 ij j c 2 j ( ij c r r − − r − − | − i z i
and M 14 N 10 013), the . & 0 . We used the M ' N sky f that provided a stable S/N. N the S/N ratio from bootstrap is 150 Mpc, which is the range of
L r < 10 10 2 even at the larger separations, which is . × 9 . as based on the halo model. For this mass cut 7
– 8 – 26 for bins . M L > 14 10 & 1. The sharp change in S/N results largely from ignoring . M 1 factor in equation (4.1). For the jackknife covariance matrix − N 150 Mpc. This can be confirmed by using only the diagonal part of the mock r < The jackknife approach, on the other hand, produces results more consistent with the In order to estimate the relative contribution of the various noise sources to the co- It should be noted that none of the methods mentioned above, including the CMB 8 while simulated maps provide 4 55 for . . bootstrap and jackknife methods. Itoverestimates can the be signal-to-noise seen that compared theabove to bootstrap 150 simulated of Mpc. temperature maps differences when For example considering for separations the case 8 the correlation between comoving binsWe when find the a bootstrap maximum covariance correlation matrix value is of constructed. around 0 we find a maximum correlation value of 0 significantly lower than the0 expected correlation frommap simulated covariance maps matrix. with In a that maximum case of we find a S/N of 7 from simulations, closer to the 8 simulated maps. Asonly discussed removing above, subsamples from thistion the is main between because cluster bins. with sample,independent which the Moreover better it with jackknife preserves is the resampling the straightforward we correla- to are account for the realizations not being from bootstrap. separations that contributes to mosttion of found the from S/N. simulatedapproach. This maps, is Despite suggesting a this that factor difference, simulationswith of the are simulations. 2 S/N still lower for To the check than the thelooked the most jackknife convergence for correla- method conservative of the is the value close jackknife thatsame to algorithm provided approach the we for a one have the stable found varied other significance cases against in variations table1 of and4.1 looked for the Contributions toThe the covariance covariance matrix matrix for theterms: mean cosmic pairwise momentum variance, includes primordialtion contributions CMB to from these and several terms, instrumental the and thermal SZ atmospheric effect, noise. particularly for In clusters addi- with masses variance matrix, we haveof produced noise-free two primary additional CMB, setsincluding and of simulated primary maps simulated CMB of maps: anisotropies.times instrumental for simulated and each We maps atmospheric set run noise,that and our not the calculate estimator instrumental the plus on covariance atmosphericwhile matrix these noise the for contributes simulated primary each 80% maps of CMB of these represents 400 the the total sets. covariance remaining We matrix, 20% havesimulations found of approach, the uncertainty. accountsvariance for contribution can the be cosmic estimatedin analytically, variance for [2–4]. example of by following the the We velocity calculations have field. found that, The despite cosmic the relatively small sky area ( can potentially contaminate the kSZused statistics in as found this bysignificant Soergel work contribution et are al. to associated in the [11].sample, with noise, corresponding The less we to galaxies masses have massive removedwe halos. have the removed most only To luminous 237 verifyof sources galaxies the that from from kSZ the our our pairwise sample tSZ momentum and detection is we is not have not found a affected that by the this significance cut. redshift range is large enoughestimate (z that = the 0.1–0.8) cosmic toof variance provide the contribution a is significant current cosmological 1% uncertainty volume. or and We less is (depending not on expected the separations) to provide a significant contribution to the JCAP03(2017)008 20 Mpc. . , providing 4 − 10 × 36) . 0 of 23 for 37 dof, includ- 2 ± χ 46 . Mpc that can be seen in figure3. = (1 1 τ − h , corresponding to the brightest 50000 and 5000 – 9 –
L statistic including the full covariance matrix of the 10 2 χ 10 × 6 . 7, implying that even correlations at the 20–30% level like ' –11
L ) from the DR11 Large Scale Structure catalog and a filter aperture 10 S/N
10 L 150 Mpc have a significant impact on the overall signal-to-noise. 10 × 3 . 10 r < × 9 . 7 L > . Considering only the diagonal part of the covariance matrix overestimates the signal σ evidence of the kSZ signal in the coadded ACTPol-ACT map as determined from 400 The best fit average optical depth for this case is ¯ We have excluded from the fit the two data points at comoving separations 8 arcmin. This aperture is consistent with the radius required to efficiently remove the 1 . . σ 0 1 . noise. Future morethe sensitive cosmological volume surveys used mightselection for of have the redshift to kSZ bins, pairwise account surveysof statistic. attempting for redshift to For this will example, reconstruct the depending effect,become need pairwise on significant depending to signal the in apply as on those a the cases. function estimator to smaller5 volumes. The Results cosmic variance can 5.1 ACTPol+ACT andWe DR11 explore the dependencebins of in the the signal range on 5 the minimum luminosity of the sample in eight 4 simulations. The model is a good fit to the data with a best fit CMB plus map noiseexample from [41]). the The ACTPolAs signal-to-noise map, mentioned ratio which above, is can significantly stableapertures be smaller would for estimated not apertures small remove analytically would variations the (see around subtract background for this the efficiently. aperture. signal while larger of 1 sources respectively. Theincreasing bins the are number of chosenWe objects study by the in using significance steps the oflook-elsewhere of the 5000 effects, 5000 detection where most (and as one luminousfor function can steps the of find objects, of highest the an 10000 fluctuation then arbitrarily luminosity in formomentum cut high the the estimator to signal-to-noise S/N ratio avoid largest and as possibe by a bins). theobjects looking function best ( of the fit luminosity model cut. are The shown mean pairwise in figure3 for a selection of 20000 ing correlations betweensimulations bins. of the Error CMB barsbest sky are fit including noise. average estimated optical We byestimator. depth estimate running the with In our detection a figure3 significance estimatorWewe and find on also the a show 400 significant the correlationbins. correlation between For comoving matrix this separation estimatedestimator bins reason from and for we the the the bin mock covariance larger differentlywith matrix maps. separation the but size large conduct 10 the separation Mpc.the analysis data binning. on We points have the The when verified increased 40calculations plotting that correlation equally (see the the at spaced for the bins significance largest example ofbelonging separations [3]) the to is because detection neighboring expected is the bins frommost not number analytical increases of affected of at the by sources larger contributionWe in find separations. to common that the The between excluding S/N. correlation bins pairs is above implies determined 150 that by Mpcto the changes noise, the lower significance yielding comoving of up separation the to bins. measurement by those observed at Scales smaller than those areare affected not described by nonlinear by the effectsthe linear and pairwise model. redshift velocities space Nonlinear around distortions modelsTo separations show that [32–34] this of predict we about the implement a change 10 nonlinear of calculation sign following in [32, 33]. The solid line in figure3 JCAP03(2017)008 : binned Right 3% for larger . 8 arcmin. The best fit . detection, demonstrating σ 1 . 20 Mpc), the overlap between < . The filter aperture is 1
M when fitting the linear model (dashed line) to separa- 13 – 10 – 4 10 − 5% for the second bin and less than 0 . 2 arcmin, the pairs affected by overlapping filters are × 10 6 √ . × 8 4 . 36) . 0 evidence of the kSZ signal in the coadded ACTPol-ACT map. The M > ± σ 1 . 46 . = (1 τ ) from the DR11 Large Scale Structure catalog. Based on the halo model these
L 10 10 : mean pairwise momentum estimator and best fit model for a selection of 20000 ob- × 9 . Left 7 . 20 Mpc, providing a 4 detection. Fitting to the nonlinear model recovers the 4 for the 5 Mpc bin) but the low separation points can have an effect on the total S/N. If L > The low comoving separation part of the kSZ pairwise estimator might also be affected r > σ σ 8 . 8 . tions we fit to the entire rangea of 3 comoving separations from 0 to 400 Mpc the linear model provides about 11% of the total in the first bin, 1 shows the expected nonlinear(1 model. The evidence for this effect is not particularly strong by the overlapseparations between the disks overlap between andthe filters rings can overlap between occur associated filters aswe with a simply have found different projection provides that, effect; another sources for however source a [12]. in radius of this of background case 1 For noise. large Moreover, jects ( luminosities correspond to masses average optical depth is ¯ solid line shows thematrix model is prediction estimated including fromcorrelation nonlinear four matrix redshift hundred for CMB space the plusfrom comoving corrections. separation mock noise The bins CMB realizations of covariance maps. of the the Note estimator same the shown area. in strong the correlation left at plot, large estimated separations even with large bins. that the low separation pointson fit the better signal-to-noise when is including small.of nonlinear the These effects, halo nonlinear although model models the usedmatter are to effect strongly particles. calculate dependent them, on including Hence, therange the we details small of do scale separations velocity not dispersion andrange, attempt of but we dark to we only extract emphasize astrophysical quotecan that information the the be from significance expected clearly this of changevaluable seen of for the future sign using measurement more in spectroscopic for powerful the surveys, the data. mean and linear pairwise has momentum the A potential proper to probe modeling intra-halo of physics. these effects will be separations. Since in this workfilters we discard does small not separationshowever ( have might need a to significant be effect modeled on or corrected the when measured interpreting data optical at depth. low separations. This systematic Figure 3 JCAP03(2017)008
L and 10 0 1 8 7 9 7 2 3 ...... − − 10 for the 2 2 3 3 3 3 3 4 10 ) repre- σ 10
× 1 . L S/N jackknife 3 × . 10 5 8 . 10 9 . These results 4 1 × 9 9 9 1 8 9 0 3 6 . L > < ...... − 2 2 4 7 8 8 9 9 3 8 10 . 10
9 × S/N bootstrap for the 30000 sources in L > σ 20) . < L/L 4 . 0 10 ± 2.4 1.5 1.9 3.8 4.1 3.7 3.6 3.7 3.8 2.1 10 , the overall signal-to-noise ratio 84 and 2 . σ × 10 S/N Simulations 9 . = (0 10 37 82 60 43 36 31 28 21 20 68 ...... 4 . The inclusion of fainter sources in the 0 0 0 0 0 0 0 0 0 0 − τ ×
± ± ± ± ± ± ± ± ± ± 10 . For a luminosity cut of 8 L . 4 ¯ τ/ 89 22 13 66 46 17 99 78 84 42 9 ...... − 10 0 1 1 1 1 1 0 0 0 1 < 10 10 8 and ¯ . – 11 –
for the most luminous 10000 sources, 2 × N × . 5000 30000 10000 15000 20000 25000 30000 40000 50000 10000 σ 9 = 3 10 . 9 ) 28) . 7 .
10 0 M < L/L × as a function of the luminosity cut and of the number of sources 13 ± , corresponding to the 30000 brightest sources, provides 1 6 S/N . . L > 9 τ 6 4
10 10 . 6 2 8 2 8 6 1 2 / 99 ...... 7 L . 4 4 3 3 2 7 6 5 10 . These two cases are listed in the two bottom rows of1. table 200 10 < 10 M × 10 < M < < M <
= (0 M > M > M > M > M > M > M > M > 9 10 . 6 8 . . × τ 4 2 , specifically 1 × 9 σ 9 . . 6 7 < L/L Mass cut ( <
10 L
L > 10 10 8 9 . . × 9 7 6 1 3 8 7 9 4 9 ...... 3 . 6 5 9 8 7 7 6 11 < L/L 6 and a best fit ¯ . < L < < L < L > L > L > L > L > L > L > 10 . Signal-to-noise and best fit ¯ L > 9 3 . . 7 5 10 = 3 We have found that the significance of the detection increases consistently with de- While the amplitude of the signal is expected to decrease with low luminosity objects, Finally, we note that the best fit optical depth values reported above might include a × 3 Luminosity cut/10 . creasing luminosity cut, down to Table 1 (N). The bottom part ofratio the for table different shows disjoint estimationhalo luminosity methods ranges. model of and We the also it report covariance is the matrix. used signal-to-noise to The calculate mass the cut mean is pairwise estimated velocity from curves. the S/N estimator does not improveminosity the cut significance to of the detection. For example, lowering the lu- (50000 brightest sources) we find are summarized in table1.While the In detection4 figure remainsdoeswe generally not show strong, increase theThe close as estimator first to might for 8 4 naively several binsTo be luminosity of facilitate expected cuts. 1 table the becausearetwo not of comparison disjoint independent the between samples because increased different of all number luminosity share the of the ranges catalog pairs. most with we massive luminosity run sources. 7 the analysis on 5 Note that the case reported in the second row of the same table ( sents another sample thata is significance disjoint close from tonext these 2 10000 two ranges. sources with For 7 these cases we have found the statistical uncertainty decreasesof as a well, significant because improvement of couldfor the be less larger caused number massive by of systematic clusters.the pairs. effects center For becoming The of example, more lack clusters dominant lowthe and luminosity amplitude to objects of be are the satellitecould galaxies. more signal lead likely [11, to Mis-centering counting not effects42]the low are to and signal mass known lie including per halos to at a comoving multiple reduce separation significant times, bin. amount which of would reduce satellitecontribution the galaxies from amplitude of gas notthe same bound filtering to approach thefrom (aperture the clusters. photometry) seventh was release This used ofIn for has the a particular Sloan previously catalog Hernandez-Monteagudo Digital been of Sky et central observed Survey al. combined galaxies when (2015) with [12] Planck data find [10, that12]. simulations of the smooth, the range 5 JCAP03(2017)008 values of our τ = 20 threshold we did not s λ and does not improve significantly
L 10 10 × 9 . 7 . This is encouraging and might imply that the L > 4 – 12 – − 10 × 2–2 . is a correction factor. In our region we find only 2242 clusters s in the range 0 20 and with an average redshift of 0.35. These are shown in figure2. 20 have larger uncertainties on the richness and we do not include them , where τ > < s s λ/s λ λ = s λ . Mean pairwise momentum estimator as a function of luminosity cut, see also table1 for 1 for the sample used in [12], and by the different angular resolutions involved. . 0 ∼ z in the analysis. Given the reduced number of clusters above the pairwise kSZ signal detected into this the work clusters. is also We sensitive note topaper however that the and a motion detailed of those direct electrons of comparison notto between [12] bound the is complicated by the different redshift population, which is limited 5.2 redMaPPer redMaPPer is a red-sequence clusterbased finder algorithm, on which the provides cluster bestredMaPPer center esimates SDSS positions for DR8 theaccount catalog position for has of incompleteness about and therichness, masking central 31683 effects, galaxy. clusters redMaPPer As inwith recommends described the richness using above, a ACT+ACTPol the corrected Clusters area. with To find a significant detection ofof the having kSZ deep signal optical using catalogs this to catalog. overlap This with stresses the the CMB importance maps for these kinds of studies. linear velocity field aroundelectrons the around central galaxies these galaxies provide avelocity compared of better to the mock description catalogs halos ofconsistent of hosting the with halos, motion the those which of central describe reported galaxies.masses, only in The estimated the [12], optical which, depth for estimates similar in physical table1 aperturesare and similar halo comparison. Each luminositycases cut in has the adecrement horizontal relevant is comoving offset always for separation visible,luminosity. clarity. range the The where The amplitude signal-to-noise ratio inner the of isfor box the signal largest fainter shows signal at is objects the decreases expected. (see same systematically text with for While the further the minimum discussion). negative Figure 4 JCAP03(2017)008 , and a 5 − = 17 for 19 10 2 × χ of 16 for 19 dof 5) . 2 3 and χ ± 5 − 07 . 0 10 and a − × 5 − = ( 14) . 10 0 null × τ ± 19) 06 . . 0 ± = (0 14 . . The red points are the average of 400 null tests 0 5 null τ − − – 13 – 10 = ( × 11) null . τ 0 . All these null tests are consistent with zero signal as expected. 5 ± : average of 400 null tests obtained from mock maps of CMB and − 10 10 . × Right . = (0 : same as in figure3 but changing the sign in (3.4). In this case we find 5 14) weights of the estimator), and take the average of these realizations. . − 0 ij null Left 10 ± c . τ σ × 05 . 0 5) . map from ACT, combined with a selection of the BOSS DR9 galaxy catalog, − 3 2 ± = ( 07 . 0 . Null tests. null τ − Finally, we randomly shuffle the temperature values of the sources, keeping their position Despite the 2.7 times wider area used in this paper and the lower map noise, the evidence The CMB simulated maps that we use for the covariance matrix provide a more stringent = ( of 13 for 19 degrees of freedom (dof) with a probability to exceed (PTE) of 0.84. 2 null This case is also consistent with no signal: fixed (and hence the and reported the first evidencea for significance the of kSZ pairwise 3.1 momentum, rejecting the null signal with for the pairwise momentum presentedthe in the one previous found sectionmatrix is by not calculation H12. substantially and higher than This mapH12 the is filtering CMB due approaches maps were and to filtered the with a a different combination matched optical filter of (MF) catalog the assuming used. that more the cluster conservative In profile covariance τ (PTE = 0.66), again consistent with no signal. dof (PTE = 0.59). The null tests are shown in65. figure Comparison with theThis previous analysis ACT is analysis anused extension a of 220 the deg work presented by H12 in [9]. In that work, the authors Figure 5 obtained by shuffling the temperaturewe values, find: keeping the sources fixed at their positions. In this case 5.3 Null tests We performed several null teststhe to maps or confirm in that the theference analysis. in signal (3.4) The is into simplest not a nullto due sum test the to so can temperature systematic that be fluctuations the effects performed associated kSZan in by with will amplitude transforming the average consistent the clusters. to with dif- zero The theχ like fit null all to signal the the other as template contributions expected, provides null test. Themaps average of of CMB 400 and null noise tests provides obtained by applying the estimator to these mock noise (blue). We find JCAP03(2017)008 to ◦ . The ) DR9 25 : ratio 4 .
1 − L − 10 10 Right . The filter × 10
9) 150 Mpc. The L × . 0 7 10 ± 10 r < 8 . , consistent with the × L > σ 7 = (3 and declination τ ◦ L > rejection of the null signal. σ 1 . to +45 ◦ 43 − – 14 – 4 arcmin FWHM (similar to the ACT beam profile) . 7 arcmin, consistent with the typical cluster size in the . 002, corresponding to a 3 . 7 arcmin aperture photometry filter. We used 40 comoving sepa- . = 0 p with right ascension ranging from 2 : mean pairwise momentum from the 7800 most luminous ( Left . and the same selection of DR9 sources. H12 reported evidence of the kSZ pairwise ◦ To compare with H12, we apply our approach to the same ACT region used in H12, In6 figure we show the results of this new analysis with 7800 objects (instead of the To study the effect of changing the optical sample, we have repeated the same analysis 25 . covering 220 deg galaxies in the ACTration region bins for for a thethem 1 analysis, but for the plotting large purposes.Note separations points that We are show the strongly errorbetween bootstrap correlated bars error and uncertainty bars from we estimates from mock re-binned range are the CMB between bootstrap much maps 10% approach smaller and (blue) to 20% at and those up large bootstrap from to separations. simulations. (red). 150 Mpc, The which error was bar the differences maximumwas separation described used by by H12 a inand [9]. Gaussian the with covariance a matrixmore 1 was conservative calculated model with independentestimate a aperture the bootstrap photometry covariance resampling. (AP) matrix filter using Here described simulations. we in adopt3.2 the and +1 momentum in the rangedue of to separations random 0–150 errors Mpc, of with a probability of the signal being Figure 6 5000 used in H12), corresponding to a minimum luminosity of After exploring variousand luminosity estimating cuts the weseparations covariance have matrix used confirmed using by this H12(0–150 simulations.result result reported Mpc) using For by the H12. the the null same AP signal range filter is of rejected comoving at 3 using the sameAs ACT described map above used this catalog by has H12 about combined 1000 with fewer objects the and DR11 more LSS low redshift catalog sources (figure7). aperture used for thissample considered, analysis and is the 1 significancefitting is to stable the against pairwise small kSZcovariance variations velocity matrix of template we this calculation find aperture. an isbetween By amplitude based this ¯ on work and simulationsthe the and uncertainties. H12 it6 Figure paper, isshowsthe where the one results two the of for methods. bootstrap the this approachand analysis main As was 20% with differences used described error smaller bars to than above,difference estimated estimate those the becomes with calculated more uncertainties with relevant from at simulations bootstrap larger for separations. are separations between 10% JCAP03(2017)008 . σ 6 . , making e T ) DR11 galaxies
L 10 10 × 56, while for the DR11 . 7 arcmin. The best fit 3 . . 7 = 0 , corresponding to the 9800
z L > L 10 , slightly less significant than the 4 10 − × 10 3 . × 7 7) . 0 L > ± 7 . – 15 – degeneracy. Here we use a different approach, by e T = (2 - τ τ of the objects used for the statistics. In the kSZ effect this from the tSZ effect would require either assuming a temper- τ τ 7 arcmin aperture photometry filter. Error bars are estimated from mock . , to break the e T 48. The higher number of low-redshift sources in DR11 might imply that . = 0 z . Mean pairwise momentum from the 9800 most luminous ( from the tSZ signal [43]. This measurement can then be used to convert the pairwise τ A direct measurement of Figure 7 in the ACT regionCMB for maps. a 1 compared to theachieved DR9 for H12 a slightly selection. differentbrightest luminosity objects We cut, find inamplitude that the for the catalog, the maximum model forsame template signal the analysis is to same ¯ using noise filter the ratio DR9 aperture is catalog. of 1 The rejection of the null signal is also lower: 2 measuring the average tSZ signal from the same sample used for the kSZ analysis by stacking Since both the CMBsignificance map achieved and is the related filterthe to scheme DR9 are the analysis the differences providescut same sources between we we with catalogs. find conclude an that The average the redshift luminosity different of cut for a larger fraction ofof these clusters. sources This are difference satelliteoptical stresses sample galaxies the used that dependence for on do the the not catalog kSZ properly and pairwise trace the analysis. the importance7 center of the Optical depth from7.1 the thermal SZ Overview signal Reconstructing pairwise velocitiesknowledge from of the the optical mean depth pairwise momentum estimator requires is completely degenerate with the peculiarfrom velocity itself. the Hence, kSZ estimating requires peculiareffect velocities including (tSZ) additional has a information. dependence The on thermal the optical component depth of and the the SZ electron temperature ature or estimating it possible toinfer measure the tSZmomentum effect into for a the pairwise velocity. same This sources section used shows an for example pairwise of statistics this and approach. JCAP03(2017)008 2 x 2 . (7.1) (7.2) varies 28 SZ 9 arcmin − . f x = 5 79 . per luminosity . The 3 arcmin 1 500 y θ R 2 √ . The error associated ) = 1 + 3 tSZ t ( by: = 0) is obtained from the δT , rel y θ from the cluster center, and f ( 2 y θ ) 5 keV is assumed, and . 500 K. These decrements are averaged µ θ/θ , = 0 ) where ) t θ ( T 500 ( y rel R is found for each source by integrating over f y SZ + ( f 992 2 . per luminosity bin. s = 0 – 16 – ) p − θ tSZ ( = T δT CMB T dsP = 3 arcmin and an outer radius of ∆ SZ 1 f depends on observed radiation frequency alone. At an Z R ∝ SZ ) f with the one measured from the kSZ pairwise momentum. The θ ( τ y . A cluster gas temperature of 2 c e /m e T B ) is the Compton parameter at a projected angle k θ ( y = The angular averaged Compton parameter ¯ To obtain the comptonization parameter we follow the steps detailed in [45]. The tSZ x the generalized Navarro-Frenk-White (GNFW) pressure profile [48], bin is reported in table2. and minimally with this choicecentral of pixel temperature. temperature decrement For via each equation source, (7.1), and the averaged ¯ effective frequency of 146.9 GHz, where in the non-relativistic limit, temperature signal is related to the Comptonization parameter size of this annulushas is the a advantage conservative of estimateWe not have of verified requiring the that the localaround the modeling noise the distribution in of sources of and the variations the forthe map. of annuli standard presence the selected This deviation of noise in approach values source sources, acrossWe is free have there the similar regions, also can map. for implying verified be that, that annuli larger regardless non-negligible the rings. of error variations In increases in table2 monotonically thewe up noise present to across the 3 the arcmin and map. is stable for on the positions of sourcesto belonging the entire to coadded the ACTPol-ACT sameprovides map luminosity an using bins. estimate a of cluster We apply theuse profile a template. central this matched SZ The central filter temperature matched valueshape decrement filter to as associated the normalize with one a the usedin cluster. for cluster the a We profile, matched 1.8 filter. which, arcmina We by then circle, theoretical assumption, estimate the relation that has comptonization between is, the parameter namical optical simulations same the depth of same clusters and aperture tocompare Comptonization this infer used parameter tSZ the for estimated from optical the hydrody- next depth section kSZ (Battaglia discusses (2016) analysis the [44]). limits above and Finally, and possible we systematic use effects7.2 associated with this approach. Thermal SZ For tSZ analysis the best signal-to-noisefilter. is obtained We by filter filtering our theet coadded CMB al. map map with [45], using a based the matched on same matched a filter Universal approach Pressure used Profile in46], [ Hasselfield with a fixed scale of (see also discussion below).binned After using the the filter same is10.5 applied, luminosity arcmin the bins sources submap as from centered table1. thearcsec on For DR11 per each each catalog pixel. source luminosity are is bin, Resizingpixel repixelized a the size from 10.5 pixels of 0.5 the arcmin minimizes ACTPol-ACT arcmin by the mapswith per (0.5 errors each arcmin) associated pixel source [47]. with to is The the 3.75 takenwithin temperature relatively to each decrement large luminosity associated be bin the to centralwith obtain pixel a each value stacked temperature in tSZ decrement signalwithin is per an bin, obtained annulus by of taking inner the radius standard deviation of the pixels JCAP03(2017)008 0 4 y − 10 (7.3) / 1.11 0.78 0.68 0.60 0.60 0.58 0.54 0.49 0.21 0.40 ± ± ± ± ± ± ± ± ± ± ), where , angular sims 5 0 σ y − 4 10 − 0.28 0.29 0.28 0.29 0.26 0.25 0.24 0.24 1.04 0.45 10 y/ ± ± ± ± ± ± ± ± ± ± / 8 . ln(¯ 1 τ 4.22 2.87 2.47 2.14 2.13 2.01 1.83 1.64 0.88 1.17 8 arcmin, the same . m 7 for the same luminosity − relation, accounting for parameter: 0.81 0.58 0.48 0.42 0.37 0.34 0.30 0.27 0.60 0.35 ) + τ 10 0 θ ± ± ± ± ± ± ± ± ± ± τ / τ - y 8 . estimates, corresponding to y 1 59(sys) (see figure8) where ¯ y . 6.09 2.78 2.05 1.52 1.51 1.34 1.11 0.88 0.25 0.45 τ 0 is along the line of sight, with ± s ) = ln( 7 1.82 1.29 1.06 0.92 0.82 0.75 0.65 0.58 1.30 0.75 τ − ± . 10 ± ± ± ± ± ± ± ± ± 0 / 0 dθ y , central Compton parameter 0 6.17 4.52 3.35 3.32 2.92 2.41 1.91 0.54 1.00 θ 49(stat) 13.60 ) . tSZ 0 0 θ ( δT K) ± y 0.49 0.35 0.29 0.25 0.22 0.20 0.18 0.16 0.35 0.20 µ θ ( 0 ± ± ± ± ± ± ± ± ± ± 60 for the sample. Note that the number of sources per . Z – 17 – i tSZ inferred from tSZ measurements and hydrodynam- 2 z 2 h θ δT τ = 1 -3.70 -1.67 -1.23 -0.91 -0.90 -0.79 -0.65 -0.52 -0.15 -0.26 = 8 . > 1 θ z 9 arcmin. The integral ¯ y . 0.47 0.45 0.45 0.52 0.51 0.50 0.49 0.48 0.48 0.47 < m ) is the angular diameter distance to the source with redshift = 5 z ( 8.22 8.77 6.53 9.64 8.85 A 14.90 12.73 11.56 10.75 10.13 < L > 500 49 at z = 0.5. θ D . ), and the cluster optical depth (Battaglia (2016), [44]). Specifically, in N z 9299 4650 9269 ( y 46448 27880 13898 18586 23251 27877 37190 and average redshift A to fix i
L L /D h 500 10 8 9 R 500 . . . 9 7 6 R 8 7 9 4 9 1 3 . m ...... 40 and m = 0 . 9 8 7 7 6 6 5 for a 1.8 arcmin radius circle and estimated optical depth = 11 6 y − relation and find a slope and . Extracted tSZ temperature decrements < L < < L < 500 L > L > L > L > L > L > L > L > θ 0 9 3 τ . . τ - We compare the kSZ optical depths to those obtained from tSZ measurements using We check the viability of using the A recent analysis of cluster hydrodynamical simulations has found a strong relationship 7 5 ) = y 0 ) being the pressure profile, defined as in [45]. We normalize this integral with the τ r and we vary ( Luminosity cut/10 the ¯ P value of each luminosity bin and calculate an averaged Compton ¯ ical simulations to obtainmomentum. an estimate Unless of systematic the biasesby mean are fitting pairwise present to in velocity the from the pairwisethe the analysis, velocity thermal mean template the SZ should optical pairwise within be depthmeasurements the consistent obtained we same with fit angular the a aperture. one linearthe estimated To relation from first quantify to the bin the agreement three andkSZ between independent the and these last the two tSZaveraged bins comptonization measurements. parameter in Battaglia (7.3) table1, and (2016)radii accounting 1.3, the provides for 1.8, optical fitting 2.6 depth the and relations averagedof uncertainties 5.2 in for arcmin. the both circular the These systematic on areas hydrodynamical angular uncertainty of the differences simulations on also between provide the an radiative best estimate coolingaperture fit and used parameters for AGN the of feedback kSZ theon models. analysis ln of ¯ this For paper, 1 the uncertainties are 4% and 8% respectively luminosity bin differs from thosediscarding in more table1 because sources the at wider the submaps edges needed of for tSZ the stacking map. require where z ln( between the averaged ¯ averaged ¯ ranges as table1.average luminosity The number of sources (N) considered per luminosity cut is listed along with simulations with AGN feedback Battaglia (2016) finds ln( Table 2 the systematic uncertainty is related to the uncertainty on the best fit parameters for the JCAP03(2017)008 = 193 km/s. ΛCDM 5, higher than the . v statistical (dark) and σ = 0 of the ΛCDM model pre- z σ at
). The cyan points correspond to M
13 L of unity considering the statistical 10 10 σ ) and find a mean pairwise velocity of × 4 − and 1 = 9 10 σ c relation from hydrodynamical simulations we find × 3 . τ 500 - – 18 – y 26) M . 0 ± 93 . 59(sys). The gray areas represent the 1 . 0 ± = (1 8 . 1 , tSZ 49(stat) τ . 0 ± 60 . 72(sys) km/s at separation 35 Mpc. This is within 1 ± = 1 8 . 1 . Best-fit line for the tSZ and kSZ optical depth measurements. We only fit to the dark m 40(stat) In table2 we summarize the tSZ temperature decrement, the averaged comptonization relation. The resulting slope is within 1 relationship for clusters with masses ± τ τ - - ¯ ¯ diction for the same mass and average redshift of the8 sample considered, Systematic effects Several systematic effectspotential can sources affect of thegalaxies error with when amplitude respect using of to galaxies the the to cluster kSZ trace centers. signal. clusters The details is of One the the mis-centering of sub-halo of structure the the can main affect 145 parameters for anthe aperture uncertainty of on 1.8 thethe arcmin optical best and depth fit the fromsame pairwise corresponding simulations luminosity momentum for optical cut of each depth, ( 3 figure luminosity as bin.using well the as We tSZ rescale estimated optical depth for the mass range predicted from the halo model for the clusters used in this analysis. y y systematic (light) uncertainty range. and systematic uncertaintiesestimates respectively, are suggesting consistent that givenextracting current the pairwise uncertainties tSZ and velocities and that fromneeded this to kSZ pairwise perform approach optical momentum a is more measurements. depth contamination promising detailed of search for Improved for the systematics, data tSZ such signal,We are as note modeling mis-centering, dusty that of galaxy the a pairwise potential velocities, source or of filtering bias of is the that maps. the hydrodynamical simulations provide the a slope Figure 8 blue points, whichtwo correspond rows to in the table1) three indicated independent by luminosity the bins labels (second (in row units and of and 10 last the remaining bins. Fitting to the 1.8 arcmin ¯ JCAP03(2017)008
L 10 10 × 9 . 7 > relation is affected τ - 51(sys). This value is y . 0 ± 32(stat) . 0 conversion. In addition to the quoted ± τ - 20 y . . Removing those objects from the sample
= 1 M relation provided by Battaglia (2016) does not m 14 τ - – 19 – y 10 M > 50%. Comparing the AP filter with a matched filter they ∼ ) is important to avoid contamination. The sample used in this paper
M 14 10 we apply the same matched filter used in our analysis to the projected simulated τ M > Imperfect removal of other effects, especially of the tSZ, can bias the pairwise kSZ For the thermal SZ stacking, a potential systematic is emission from dusty star-forming Another potential source of systematics is the ¯ has mostly clusters withwe have masses found below only 237 that objects range. with Based on halo model estimated masses signal. For the pairwisea statistic perfect this cancellation couldet happen of al. if the there [13] tSZ areclusters found effect, ( not that, enough especially for pairs for to the the achieve reconstructed most velocity massive method, clusters. removing the Schaan most massive have found that theS/N former from could 10% be to slightly 18% more lower, conservative depending than on the the latter, kSZ providing model. a involve any filtering of the simulatedmeasured clusters. To assess whethermaps the used matched by filter Battaglia affects (2016), the withoutthe instrumental mean noise nor optical CMB depth backround. estimatedthe We find optical after that depth applying without the filtering. matchedtensive work. filter A However, is detailed under about the investigation pessimistic 30%with of assumption larger this the that than bias all matched the will filter optical requiredepths depths are more estimated fit biased ex- is 30% in high, better we agreement with find unity: that the slope for the tSZ-kSZ optical galaxies. At theble 148 [49, GHz].50 frequency useddust Data contribution for at and this remove 220 analysis itan GHz, from the appropriate the where frequency dust measured scaling the emission temperature (see220 tSZ is for decrement GHz at example effect not covering [51]). 148 the vanishes, negligi- GHz full We cannoisier with do coadded ACT be not ACTPol only + yet used region. have ACT measurements to map. Usingthe at these estimate A value data 220 of the we GHz have the found map estimated that is removing tSZ available the for optical dust the depth can increase by about 20% for luminosities the measured pairwise momentumThe especially filtering at of lowquantified the separations several CMB of and these map is effectsof also not using sources N-body can easily can simulations affect modeled. andpessimistic reduce the found scenario. the that recovered the They amplitude S/N. mis-centering haveamplitude of Flender also of the et found the pairwise signal that al. kSZ star at [42] by formation most have and by feedback 10% can reduce at the all separations, in a does not affect the significance of the pairwise kSZ measurement. and up tothe 50% dust for contribution the willACTPol lower hence [52] survey be luminosity will very bin, soon importantfor provide where an multi-frequency for maps the appropriate future covering analysis tSZ data. a of wider signal The region, the is allowing ongoing dust Advanced contamination. weaker.systematic Estimating uncertainty between differenttainty in hydrodynamical extrapolating from simulations, themass larger there masses objects is in in the also hydrodynamical ouruncertainty. simulations uncer- sample. to Alternatively, wider the surveys Future lower with simulationscatalogs, deeper extending providing optical to a data lower significant will number massesrent allow of simulations. the can sources use We address with also of this masses observe larger in that the the same ¯ range as the cur- well within the error barsthis of stage, the the best approach fit usedtainties. quoted in Addressing in this proper the filtering paper previous of isby section. the dominated the simulated We maps by filter conclude and statistical should how that, and the be at ¯ systematic addressed uncer- before applying this approach to data from future surveys. JCAP03(2017)008 , τ σ 3 - 1 − . y 10 × 89) . 0 ± 75 . measurement of the σ 2 . 5, while the SDSS-based . in the range 6–20%. These = 0 τ z . We have explored the depen- 4 ). Moreover, the matched filter −
10 M 14 × 10 36) . > 0 ± 500 M 46 . – 20 – = (1 20 and average redshift ¯ τ 5. We have checked the redshift dependence of the ¯ > . s λ of our estimates but suggest that future larger surveys, which σ relation. τ - y 7. We have found variations in the value of . 35. The best fit optical depth value reported in [11] is (3 . = 0 z = 0 z 5. This is a reasonable choice, because, as shown in2, table the average redshift of 3 and . . We found that the optical depth estimated with tSZ measurements is consistent with This work represents an extension and an improvement over the first kSZ measurement The recent work presented by Soergel et al. [11] reported a 4 In this analysis we used the best fit relation provided by Battaglia (2016) for a redshift = 0 = 0 the one estimatedpairwise by velocity, fitting assuming the aviable kSZ ΛCDM approach pairwise cosmology. for momentum This normalizing measurements the shows mean that to pairwise using the velocity. tSZpresented analytical data in may [9] be usingaperture ACT a photometry data filtering and a approachmatrix and sample estimation, a of based more the on conservative DR9the simulations but BOSS evidence of more galaxies. the of stable CMB We the covariance have sky kSZ used and effect an noise. in [9] We have over also the confirmed same range of comoving separations. dence of the estimated signal-to-noisematrix. ratio on The the method correlationand used between we to show bins reconstruct that the of the covariance on comoving approach the used separation estimated to significance has estimate ofis a the the based covariance complicated measurement. on matrix The structure has simulations most important of stable effects the and CMB conservative sky method plus noise. pairwise momentum using CMB dataEnergy from the Survey, South using Pole an Telescopemodel and template. approach data The similar from the Soergel toric Dark et the redshifts, al. work one showing is that,photometric presented particulary with catalogs in interesting because can an this it provide appropriateet paper, uses significant al. treatment photomet- fitting used evidence of clusters to for the withredMaPPer a the richness photometric catalog pairwise uncertainty, overlapping momentum. withredshift, Soergel the ¯ CMB map used in our work has a lower average all the luminosity bins is close to 0 z conversion by repeating thez analysis for the bestvariations are fit still values within provided 1–2 bywill Battaglia be able (2016) to for splitdependence the of cluster the sample ¯ into redshift bins, will need to account9 for the redshift Discussion We have presented new measurementsPol of the maps pairwise combined kSZ with signalOscillation the from Spectroscopic the ACT Survey most data. to recentdetection ACT- trace We of used the the the positions kSZvelocity LSS of signal template DR11 from galaxy corresponding to the catalog clusters thehave 20000 from and same found most the found average an luminous redshift a Baryon average and sources. optical 4 mass depth By cut ¯ fitting of this to sample a we pairwise approach used inwhile their the work aperture photometry isaround used more here the sensitive measures center the to ofone average the optical the presented clusters. depth signal in in The at this afactors S/N wider work, the may area reported even contribute center by though to of Soergel our this, the et analysis such al. clusters, uses as is spectroscopic comparable the redshifts. to use the of Several a catalog of clusters at higher redshifts higher than the valuesclusters reported used in in the this Soergel paper. et al. This analysis difference ( is due to the more massive JCAP03(2017)008 evidence of ) and it was 2 σ – 21 – and a stage IV CMB experiment [54] can apply 2 evidence for the kSZ, depending on the galaxy bias con- σ Improved measurements from ACTPol are expected with the 2015 data, which has Recently, Ferraro et al. [15] and Hill et al. [16] have studied and applied a projected Different methods have been used and proposed to extract the kSZ signal. Schaan et al. . http://simonsobservatory.org/ 2 the kSZ pairwise statistic,strong detections velocity of reconstruction the kSZ andfrequency effect data projected and will to enable fields probe measurementsand optical methods the depth baryon for to and content single of peculiar achieve galaxy clusters. velocitiesnew simultaneously clusters. cosmological With probe Multi- these that is datacosmology complementary the over to pairwise current a observables large kSZ and range signal is able of may to physical become constrain scales. anAcknowledgments important We thank Shirley Ho,Story Daisuke for Nagai, useful Eduardostrengthened Rozo, discussions. our Eli paper. Rykoff, Wetion Bjoern (NSF) This thank Soergel through work the and awardsawards was AST-0408698 Kyle referee PHY-0855887 and supported for AST-0965625 and by for PHY-1214379. thegrant the the AST-1454881 very ACT U.S. FDB project, and National useful and as1312380. AST-1517049. Science MDN comments well EMV as Founda- acknowledge which acknowledges SA support support andunder from from Grant the AK NSF NSF No.DGE-1650441. were Graduate NBlowship. partly Research acknowledges Fellowship RD supported Program the thanks by support CONICYTRH for from NSF-AST- acknowledges grants the funding FONDECYT Lyman 1141113 from Spitzer andton the PIA Fel- University, Dunlap the Anillo Institute. University ACT-1417. of Fundingfor Pennsylvania, was Cornell Innovation also (CFI) University provided and award by a to Prince- Canada UBC. Foundation ACT operates in the Parque Astron´omicoAtacama in roughly 3 times largerSPT-3G overlap with [53], BOSS the [20]. Future Simons surveys, Observatory like Advanced ACTPol [52], straints. This method requiresof knowledge of single the objects, redshift distributionand allowing but provides use not an of additional of the methodand photometric redshifts for redshift. probing data the without baryon treating distribution as redshift a uncertainties function of scale field approach, consisting oftemperature squaring maps the constructed CMB from anisotropysquared, multi-frequency maps. and Planck cross-correlated Foreground-cleaned and with CMB WMAP galaxyExplorer measurements data (WISE), from were finding the filtered, Wide-field 3.8–4.5 Infrared Survey combined with the CMASS galaxywere catalog converted to from BOSS total DR10.cosmological masses baryon for Galaxy abundance. the stellar This mass host methodfraction estimates halos provides in and a galaxy potential then probe clusters tocomplementary for and an to the for pairwise optical free kSZ electron depth the measurementspotential by baryon and systematic using profile could effects. the of be clusters. used to This understand different and approach remove is the kSZ signal, dependingmap on used by the Schaan velocity et reconstruction al. is method similar used. to the The one ACTPol used CMB in this work (about 660 deg in [13] used aas velocity a reconstruction function of approach the to angular measure radius the around amplitude the of clusters, reporting the a kSZ 2.9 signal and 3.3 which might be less affectedthe by covariance centering matrix issues, (jackknife and instead the of different simulations). approach used to estimate JCAP03(2017)008 ]. (1972) 173 4 (2015) 47 ]. Astrophys. Space SPIRE , IN 808 ][ SPIRE ]. ]. (2015) 063501 IN ]. ][ SPIRE D92 SPIRE IN SPIRE IN ][ IN ][ Constraints on gravity and dark Constraints on massive neutrinos Astrophys. J. ][ , arXiv:1102.0870 Phys. Rev. , arXiv:1606.07721 Comments Astrophys. Space Phys. (2011) 24[ arXiv:1312.3680 , arXiv:1203.4219 arXiv:0712.0034 – 22 – 194 (2017) A115 [ Small scale fluctuations of relic radiation Dark Energy Constraints from Galaxy Cluster Peculiar The Observations of relic radiation as a test of the nature of (2013) 52[ 598 778 ]. ]. (2012) 041101[ (2008) 083004[ ]. 109 SPIRE SPIRE D77 A dual-band millimeter-wave kinetic inductance camera for the IRAM IN IN Astrophys. J. Suppl. ][ ][ , SPIRE Mapping the kinetic Sunyaev-Zel’dovich effect toward MACS J0717.5+3745 IN A Measurement of the Kinetic Sunyaev-Zel’dovich Signal Toward MACS Evidence of Galaxy Cluster Motions with the Kinematic Sunyaev-Zel’dovich Astrophys. J. , Astron. Astrophys. , Phys. Rev. , ]. Phys. Rev. Lett. (1970) 3 [ , 7 SPIRE IN arXiv:1412.0592 arXiv:1408.6248 Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Partic- SDSS-III is managed by the Astrophysical Research Consortium for the Participating [ Velocities [ energy from the pairwise kinematic Sunyaev-Zeldovich effect X-Ray radiation from the clusters of galaxies from the pairwise kinematic Sunyaev-Zel’dovich effect [ Sci. J0717.5+3745 with NIKA 30-meter telescope Effect [2] S. Bhattacharya and A. Kosowsky, [4] E.-M. Mueller, F. de Bernardis, R. Bean and M.D. Niemack, [1] R.A. Sunyaev and Y.B. Zeldovich, [3] E.-M. Mueller, F. de Bernardis, R. Bean and M.D. Niemack, [6] J. Sayers et al., [5] R.A. Sunyaev and Ya.B. Zeldovich, [7] R. Adam et al., [8] A. Monfardini et al., [9] N. Hand et al., ipating Institutions, the NationalOffice Science of Science. Foundation, and The the SDSS-III U.S. web site Department is ofInstitutions. http://www.sdss3.org/ of Energy the SDSS-IIIParticipation Collaboration Group, including Brookhaven the National University ofsity Laboratory, Carnegie Arizona, of the Mellon Florida, Brazilian University, the Univer- University, French the Participation Instituto Group, de theParticipation Astrofisica German de Group, Participation Canarias, Johns Group, theMax Harvard Michigan Hopkins Planck State/Notre University, Institute Dame/JINA LawrenceNew for Berkeley Astrophysics, Mexico National Max StateState Laboratory, Planck University, University, Institute New University of for York Portsmouth,Group, Extraterrestrial University, Princeton University of Ohio Physics, University, Tokyo, the University State of SpanishUniversity Utah, University, Participation of Vanderbilt University, Pennsylvania University Washington, of and Virginia, Yale University. 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