application/xmlSearch for Higgs bosons of the minimal supersymmetric standard model in [formula omitted] collisions at [formula omitted]D0 CollaborationV.M. AbazovB. AbbottB.S. AcharyaM. AdamsT. AdamsG.D. AlexeevG. AlkhazovA. AltonG. AlversonM. AokiA. AskewB. ÅsmanS. AtkinsO. AtramentovK. AugstenC. AvilaJ. BackusMayesF. BadaudL. BagbyB. BaldinD.V. BandurinS. BanerjeeE. BarberisP. BaringerJ. BarretoJ.F. BartlettU. BasslerV. BazterraA. BeanM. BegalliC. Belanger-ChampagneL. BellantoniS.B. BeriG. BernardiR. BernhardI. BertramM. BesançonR. BeuselinckV.A. BezzubovP.C. BhatS. BhatiaV. BhatnagarG. BlazeyS. BlessingK. BloomA. BoehnleinD. BolineE.E. BoosG. BorissovT. BoseA. BrandtO. BrandtR. BrockG. BrooijmansA. BrossD. BrownJ. BrownX.B. BuM. BuehlerV. BuescherV. BunichevS. BurdinT.H. BurnettC.P. BuszelloB. CalpasE. Camacho-PérezM.A. Carrasco-LizarragaB.C.K. CaseyH. Castilla-ValdezS. ChakrabartiD. ChakrabortyK.M. ChanA. ChandraE. ChaponG. ChenS. Chevalier-ThéryD.K. ChoS.W. ChoS. ChoiB. ChoudharyS. CihangirD. ClaesJ. ClutterM. CookeW.E. CooperM. CorcoranF. CoudercM.-C. CousinouA. CrocD. CuttsA. DasG. DaviesS.J. de JongE. De La Cruz-BureloF. DéliotR. DeminaD. DenisovS.P. DenisovS. DesaiC. DeterreK. DeVaughanH.T. DiehlM. DiesburgP.F. DingA. DominguezT. DorlandA. DubeyL.V. DudkoD. DugganA. DuperrinS. DuttA. DyshkantM. EadsD. EdmundsJ. EllisonV.D. ElviraY. EnariH. EvansA. EvdokimovV.N. EvdokimovG. FaciniT. FerbelF. FiedlerF. FilthautW. FisherH.E. FiskM. FortnerH. FoxS. FuessA. Garcia-BellidoG.A. García-GuerraV. GavrilovP. GayW. GengD. GerbaudoC.E. GerberY. GershteinG. GintherG. GolovanovA. GoussiouP.D. GrannisS. GrederH. GreenleeZ.D. GreenwoodE.M. GregoresG. GrenierPh. GrisJ.-F. GrivazA. GrohsjeanS. GrünendahlM.W. GrünewaldT. GuilleminG. GutierrezP. GutierrezA. HaasS. HagopianJ. HaleyL. HanK. HarderA. HarelJ.M. HauptmanJ. HaysT. HeadT. HebbekerD. HedinH. HegabA.P. HeinsonU. HeintzC. HenselI. Heredia-De La CruzK. HernerG. HeskethM.D. HildrethR. HiroskyT. HoangJ.D. HobbsB. HoeneisenM. HohlfeldZ. HubacekV. HynekI. IashviliY. IlchenkoR. IllingworthA.S. ItoS. JabeenM. JaffréD. JaminA. JayasingheR. JesikK. JohnsM. JohnsonA. JonckheereP. JonssonJ. JoshiA.W. JungA. JusteK. KaadzeE. KajfaszD. KarmanovP.A. KasperI. KatsanosR. KehoeS. KermicheN. KhalatyanA. KhanovA. KharchilavaY.N. KharzheevJ.M. KohliA.V. KozelovJ. KrausS. KulikovA. KumarA. KupcoT. KurčaV.A. KuzminS. LammersG. LandsbergP. LebrunH.S. LeeS.W. LeeW.M. LeeJ. LellouchH. LiL. LiQ.Z. LiS.M. LiettiJ.K. LimD. LincolnJ. LinnemannV.V. LipaevR. LiptonY. LiuA. LobodenkoM. LokajicekR. Lopes de SaH.J. LubattiR. Luna-GarciaA.L. LyonA.K.A. MacielD. MackinR. MadarR. Magaña-VillalbaS. MalikV.L. MalyshevY. MaravinJ. Martínez-OrtegaR. McCarthyC.L. McGivernM.M. MeijerA. MelnitchoukD. MenezesP.G. MercadanteM. MerkinA. MeyerJ. MeyerF. MiconiN.K. MondalG.S. MuanzaM. MulhearnE. NagyM. NaimuddinM. NarainR. NayyarH.A. NealJ.P. NegretP. NeustroevS.F. NovaesT. NunnemannG. ObrantJ. OrdunaN. OsmanJ. OstaG.J. Otero y GarzónM. PadillaA. PalN. ParasharV. PariharS.K. ParkR. PartridgeN. ParuaA. PatwaB. PenningM. PerfilovY. PetersK. PetridisG. PetrilloP. PétroffR. PiegaiaM.-A. PleierP.L.M. Podesta-LermaV.M. PodstavkovP. PolozovA.V. PopovM. PrewittD. PriceN. ProkopenkoJ. QianA. QuadtB. QuinnM.S. RangelK. RanjanP.N. RatoffI. RazumovP. RenkelM. RijssenbeekI. Ripp-BaudotF. RizatdinovaM. RominskyA. RossC. RoyonP. RubinovR. RuchtiG. SafronovG. SajotP. SalcidoA. Sánchez-HernándezM.P. SandersB. SanghiA.S. SantosG. SavageL. SawyerT. ScanlonR.D. SchambergerY. ScheglovH. SchellmanT. SchliephakeS. SchlobohmC. SchwanenbergerR. SchwienhorstJ. SekaricH. SeveriniE. ShabalinaV. SharyA.A. ShchukinR.K. ShivpuriV. SimakV. SirotenkoP. SkubicP. SlatteryD. SmirnovK.J. SmithG.R. SnowJ. SnowS. SnyderS. Söldner-RemboldL. SonnenscheinK. SoustruznikJ. StarkV. StolinD.A. StoyanovaM. StraussD. StromL. StutteL. SuterP. SvoiskyM. TakahashiA. TanasijczukM. TitovV.V. TokmeninY.-T. TsaiK. Tschann-GrimmD. TsybychevB. TuchmingC. TullyL. UvarovS. UvarovS. UzunyanR. Van KootenW.M. van LeeuwenN. VarelasE.W. VarnesI.A. VasilyevP. VerdierL.S. VertogradovM. VerzocchiM. VesterinenD. VilanovaP. VokacH.D. WahlM.H.L.S. WangJ. WarcholG. WattsM. WayneM. WeberJ. WeichertL. Welty-RiegerA. WhiteD. WickeM.R.J. WilliamsG.W. WilsonM. WobischD.R. WoodT.R. WyattY. XieR. YamadaW.-C. YangT. YasudaY.A. YatsunenkoW. YeZ. YeH. YinK. YipS.W. YounT. ZhaoB. ZhouJ. ZhuM. ZielinskiD. ZieminskaL. ZivkovicPhysics Letters B 710 (2012) 569-577. doi:10.1016/j.physletb.2012.03.021journalPhysics Letters BCopyright © 2012 Elsevier B.V. All rights reserved.Elsevier B.V.0370-26937104-520 April 20122012-04-20569-57756957710.1016/j.physletb.2012.03.021http://dx.doi.org/10.1016/j.physletb.2012.03.021doi:10.1016/j.physletb.2012.03.021http://vtw.elsevier.com/data/voc/oa/OpenAccessStatus#Full2014-01-01T00:14:32ZSCOAP3 - Sponsoring Consortium for Open Access Publishing in Particle Physicshttp://vtw.elsevier.com/data/voc/oa/SponsorType#FundingBodyhttp://creativecommons.org/licenses/by/3.0/

Journals

S300.3

PLB28433S0370-2693(12)00285-710.1016/j.physletb.2012.03.021Elsevier B.V.Experiments

Fig. 1

Main Higgs boson production mechanisms in the MSSM in the 5-flavor scheme where c and b quarks are included in parton density functions. The gluon fusion (a) and bb¯ annihilation (b) processes dominate the inclusive production, while (c) is the dominant process for associated bϕ production.

Fig. 2

Distribution of Mhat in the inclusive ττ sample on (a) linear and (b) logarithmic scale. (c) Df in the bττ sample trained for mϕ=100 GeV, and (d) for mϕ=190 GeV adding the final requirements on DMJ and Dtt¯. All τh types are combined. The predicted signal is shown in the case of the mhmax scenario (μ=+200 GeV and tanβ=40) for mϕ=100 GeV in (a) and (c), and mϕ=190 GeV in (b) and (d).

Fig. 3

Distributions of the dijet invariant mass, taken from Ref. [8], in the signal region defined by Dbbb>0.65 (a) and in a control region defined by Dbbb<0.12 (b) for the dominant bbb 3-jet channel is shown. The line shows the background model, the solid histogram the component coming from bbb, the points with error bars show the data. The background is normalised to the data yield for illustration purposes. The difference between data and the background model is shown at the bottom of each panel.

Fig. 4

Comparison of the expected limits in the (tanβ,MA) plane for the three channels separately, and their combination for the mhmax scenario with (a) μ<0 and (b) μ>0.

Fig. 5

Constraints in the (tanβ,MA) plane from the di-tau combination in the mhmax scenario. These limits very weakly depend on the other MSSM parameters.

Fig. 6

Constraints in the (tanβ,MA) plane for different MSSM scenarios from the combined Higgs bosons searches.

Table 1

Searches combined in this Letter.

Final stateL(fb−1)Referenceϕ→τμτh (b-jet veto)7.3bϕ→bτμτh7.3

[7]

bϕ→bb¯b5.2

[8]

Table 2

Expected background yield, observed data yield, and expected signal yields for the di-tau selections with their total systematic uncertainties. The signal yields are given for the mhmax scenario (μ=+200 GeV and tanβ=40).

ττbττZ(+jets)11547±634218±17tt¯25±4183±32MJ1343±23636±6Other560±2540±2Total background13474±684476±40Data13 344488Signal mϕ=100 GeV116581Signal mϕ=190 GeV7012Table 3

Observed data yield and expected signal yields in the bbb channel. The signal yields are given for the scenario described in Table 2.

Njets34Data15 21410 417Signal mϕ=100 GeV335166Signal mϕ=190 GeV7036Search for Higgs bosons of the minimal supersymmetric standard model in pp¯ collisions at s=1.96 TeV

D0 Collaboration

V.M.

Abazov

Abbott

B.S.

Acharya

Adams

G.D.

Alexeev

Alkhazov

Alton

Alverson

Aoki

Askew

Åsman

Atkins

Atramentov

Augsten

Avila

BackusMayes

Badaud

Bagby

Baldin

D.V.

Bandurin

Banerjee

Barberis

Baringer

Barreto

J.F.

Bartlett

Bassler

Bazterra

Bean

Begalli

Belanger-Champagne

Bellantoni

S.B.

Beri

Bernardi

Bernhard

Bertram

Besançon

Beuselinck

V.A.

Bezzubov

P.C.

Bhat

Bhatia

Bhatnagar

Blazey

Blessing

Bloom

Boehnlein

Boline

E.E.

Boos

Borissov

Bose

Brandt

Brock

Brooijmans

Bross

Brown

X.B.

Buehler

Buescher

Bunichev

Burdin

T.H.

Burnett

C.P.

Buszello

Calpas

Camacho-Pérez

M.A.

Carrasco-Lizarraga

B.C.K.

Casey

Castilla-Valdez

Chakrabarti

Chakraborty

K.M.

Chan

Chandra

Chapon

Chen

Chevalier-Théry

D.K.

Cho

S.W.

Cho

Choi

Choudhary

Cihangir

Claes

Clutter

Cooke

W.E.

Cooper

Corcoran

Couderc

M.-C.

Cousinou

Croc

Cutts

Das

Davies

S.J.

de Jong

De La Cruz-Burelo

Déliot

Demina

Denisov

S.P.

Denisov

Desai

Deterre

DeVaughan

H.T.

Diehl

Diesburg

P.F.

Ding

Dominguez

Dorland

Dubey

L.V.

Dudko

Duggan

Duperrin

Dutt

Dyshkant

Eads

Edmunds

Ellison

V.D.

Elvira

Enari

Evans

Evdokimov

V.N.

Evdokimov

Facini

Ferbel

Fiedler

Filthaut

Fisher

H.E.

Fisk

Fortner

Fox

Fuess

Garcia-Bellido

G.A.

García-Guerra

Gavrilov

Gay

Geng

Gerbaudo

C.E.

Gerber

Gershtein

Ginther

Golovanov

Goussiou

P.D.

Grannis

Greder

Greenlee

Z.D.

Greenwood

E.M.

Gregores

Grenier

Ph.

Gris

J.-F.

Grivaz

Grohsjean

Grünendahl

M.W.

Grünewald

Guillemin

Gutierrez

Haas

Hagopian

Haley

Han

Harder

Harel

J.M.

Hauptman

Hays

Head

Hebbeker

Hedin

Hegab

A.P.

Heinson

Heintz

Hensel

Heredia-De La Cruz

Herner

Hesketh

M.D.

Hildreth

Hirosky

Hoang

J.D.

Hobbs

Hoeneisen

Hohlfeld

Hubacek

Hynek

Iashvili

Ilchenko

Illingworth

A.S.

Ito

Jabeen

Jaffré

Jamin

Jayasinghe

Jesik

Johns

Johnson

Jonckheere

Jonsson

Joshi

A.W.

Jung

Juste

Kaadze

Kajfasz

Karmanov

P.A.

Kasper

Katsanos

Kehoe

Kermiche

Khalatyan

Khanov

Kharchilava

Y.N.

Kharzheev

J.M.

Kohli

A.V.

Kozelov

Kraus

Kulikov

Kumar

Kupco

Kurča

V.A.

Kuzmin

Lammers

Landsberg

Lebrun

H.S.

Lee

S.W.

Lee

W.M.

Lee

Lellouch

Q.Z.

S.M.

Lietti

J.K.

Lim

Lincoln

Linnemann

V.V.

Lipaev

Lipton

Liu

Lobodenko

Lokajicek

Lopes de Sa

H.J.

Lubatti

Luna-Garcia

A.L.

Lyon

A.K.A.

Maciel

Mackin

Madar

Magaña-Villalba

Malik

V.L.

Malyshev

Maravin

Martínez-Ortega

McCarthy

C.L.

McGivern

M.M.

Meijer

Melnitchouk

Menezes

P.G.

Mercadante

Merkin

Meyer

Miconi

N.K.

Mondal

G.S.

Muanza

Mulhearn

Nagy

Naimuddin

Narain

Nayyar

H.A.

Neal

J.P.

Negret

Neustroev

S.F.

Novaes

Nunnemann

Obrant

Orduna

Osman

Osta

G.J.

Otero y Garzón

Padilla

Pal

Parashar

Parihar

S.K.

Park

Partridge

Parua

Patwa

Penning

Perfilov

Peters

Petridis

Petrillo

Pétroff

Piegaia

M.-A.

Pleier

P.L.M.

Podesta-Lerma

V.M.

Podstavkov

Polozov

A.V.

Popov

Prewitt

Price

Prokopenko

Qian

Quadt

Quinn

M.S.

Rangel

Ranjan

P.N.

Ratoff

Razumov

Renkel

Rijssenbeek

Ripp-Baudot

Rizatdinova

Rominsky

Ross

Royon

Rubinov

Ruchti

Safronov

Sajot

Salcido

Sánchez-Hernández

M.P.

Sanders

Sanghi

A.S.

Santos

Savage

Sawyer

Scanlon

R.D.

Schamberger

Scheglov

Schellman

Schliephake

Schlobohm

Schwanenberger

Schwienhorst

Sekaric

Severini

Shabalina

Shary

A.A.

Shchukin

R.K.

Shivpuri

Simak

Sirotenko

Skubic

Slattery

Smirnov

K.J.

Smith

G.R.

Snow

Snyder

Söldner-Rembold

Sonnenschein

Soustruznik

Stark

Stolin

D.A.

Stoyanova

Strauss

Strom

Stutte

Suter

Svoisky

Takahashi

Tanasijczuk

Titov

V.V.

Tokmenin

Y.-T.

Tsai

Tschann-Grimm

Tsybychev

Tuchming

Tully

Uvarov

Uzunyan

Van Kooten

W.M.

van Leeuwen

Varelas

E.W.

Varnes

I.A.

Vasilyev

Verdier

L.S.

Vertogradov

Verzocchi

Vesterinen

Vilanova

Vokac

H.D.

Wahl

M.H.L.S.

Wang

Warchol

Watts

Wayne

Weber

Weichert

Welty-Rieger

White

Wicke

M.R.J.

Williams

G.W.

Wilson

Wobisch

D.R.

Wood

T.R.

Wyatt

Xie

Yamada

W.-C.

Yang

Yasuda

Y.A.

Yatsunenko

Yin

Yip

S.W.

Youn

Zhao

Zhou

Zhu

Zielinski

Zieminska

Zivkovic

aUniversidad de Buenos Aires, Buenos Aires, ArgentinabLAFEX, Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro, BrazilcUniversidade do Estado do Rio de Janeiro, Rio de Janeiro, BrazildUniversidade Federal do ABC, Santo André, BrazileInstituto de Física Teórica, Universidade Estadual Paulista, São Paulo, BrazilfUniversity of Science and Technology of China, Hefei, Peopleʼs Republic of ChinagUniversidad de los Andes, Bogotá, ColombiahCharles University, Faculty of Mathematics and Physics, Center for Particle Physics, Prague, Czech RepubliciCzech Technical University in Prague, Prague, Czech RepublicjCenter for Particle Physics, Institute of Physics, Academy of Sciences of the Czech Republic, Prague, Czech RepublickUniversidad San Francisco de Quito, Quito, EcuadorlLPC, Université Blaise Pascal, CNRS/IN2P3, Clermont, FrancemLPSC, Université Joseph Fourier Grenoble 1, CNRS/IN2P3, Institut National Polytechnique de Grenoble, Grenoble, FrancenCPPM, Aix-Marseille Université, CNRS/IN2P3, Marseille, FranceoLAL, Université Paris-Sud, CNRS/IN2P3, Orsay, FrancepLPNHE, Universités Paris VI and VII, CNRS/IN2P3, Paris, FranceqCEA, Irfu, SPP, Saclay, FrancerIPHC, Université de Strasbourg, CNRS/IN2P3, Strasbourg, FrancesIPNL, Université Lyon 1, CNRS/IN2P3, Villeurbanne, and Université de Lyon, Lyon, FrancetIII. Physikalisches Institut A, RWTH Aachen University, Aachen, GermanyuPhysikalisches Institut, Universität Freiburg, Freiburg, GermanyvII. Physikalisches Institut, Georg-August-Universität Göttingen, Göttingen, GermanywInstitut für Physik, Universität Mainz, Mainz, GermanyxLudwig-Maximilians-Universität München, München, GermanyyFachbereich Physik, Bergische Universität Wuppertal, Wuppertal, GermanyzPanjab University, Chandigarh, IndiaaaDelhi University, Delhi, IndiaabTata Institute of Fundamental Research, Mumbai, IndiaacUniversity College Dublin, Dublin, IrelandadKorea Detector Laboratory, Korea University, Seoul, Republic of KoreaaeCINVESTAV, Mexico City, MexicoafNikhef, Science Park, Amsterdam, The NetherlandsagRadboud University Nijmegen, Nijmegen, and Nikhef, Science Park, Amsterdam, The NetherlandsahJoint Institute for Nuclear Research, Dubna, RussiaaiInstitute for Theoretical and Experimental Physics, Moscow, RussiaajMoscow State University, Moscow, RussiaakInstitute for High Energy Physics, Protvino, RussiaalPetersburg Nuclear Physics Institute, St. Petersburg, RussiaamInstitució Catalana de Recerca i Estudis Avançats (ICREA) and Institut de Física dʼAltes Energies (IFAE), Barcelona, SpainanStockholm University, Stockholm, and Uppsala University, Uppsala, SwedenaoLancaster University, Lancaster LA1 4YB, United KingdomapImperial College London, London SW7 2AZ, United KingdomaqThe University of Manchester, Manchester M13 9PL, United KingdomarUniversity of Arizona, Tucson, AR 85721, USAasUniversity of California Riverside, Riverside, CA 92521, USAatFlorida State University, Tallahassee, FL 32306, USAauFermi National Accelerator Laboratory, Batavia, IL 60510, USAavUniversity of Illinois at Chicago, Chicago, IL 60607, USAawNorthern Illinois University, DeKalb, IL 60115, USAaxNorthwestern University, Evanston, IL 60208, USAayIndiana University, Bloomington, IN 47405, USAazPurdue University Calumet, Hammond, IN 46323, USAbaUniversity of Notre Dame, Notre Dame, IN 46556, USAbbIowa State University, Ames, IA 50011, USAbcUniversity of Kansas, Lawrence, KS 66045, USAbdKansas State University, Manhattan, KS 66506, USAbeLouisiana Tech University, Ruston, LA 71272, USAbfBoston University, Boston, MA 02215, USAbgNortheastern University, Boston, MA 02115, USAbhUniversity of Michigan, Ann Arbor, MI 48109, USAbiMichigan State University, East Lansing, MI 48824, USAbjUniversity of Mississippi, University, MS 38677, USAbkUniversity of Nebraska, Lincoln, NE 68588, USAblRutgers University, Piscataway, NJ 08855, USAbmPrinceton University, Princeton, NJ 08544, USAbnState University of New York, Buffalo, NY 14260, USAboColumbia University, New York, NY 10027, USAbpUniversity of Rochester, Rochester, NY 14627, USAbqState University of New York, Stony Brook, NY 11794, USAbrBrookhaven National Laboratory, Upton, NY 11973, USAbsLangston University, Langston, OK 73050, USAbtUniversity of Oklahoma, Norman, OK 73019, USAbuOklahoma State University, Stillwater, OK 74078, USAbvBrown University, Providence, RI 02912, USAbwUniversity of Texas, Arlington, TX 76019, USAbxSouthern Methodist University, Dallas, TX 75275, USAbyRice University, Houston, TX 77005, USAbzUniversity of Virginia, Charlottesville, VA 22901, USAcaUniversity of Washington, Seattle, WA 98195, USA1

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Editor: H. Weerts

Abstract

We report results from searches for neutral Higgs bosons produced in pp¯ collisions recorded by the D0 experiment at the Fermilab Tevatron Collider. We study the production of inclusive neutral Higgs boson in the ττ final state and in association with a b quark in the bττ and bbb final states. These results are combined to improve the sensitivity to the production of neutral Higgs bosons in the context of the minimal supersymmetric standard model (MSSM). The data are found to be consistent with expectation from background processes. Upper limits on MSSM Higgs boson production are set for Higgs boson masses ranging from 90 to 300 GeV. We exclude tanβ>20–30 for Higgs boson masses below 180 GeV. These are the most stringent constraints on MSSM Higgs boson production in pp¯ collisions.

Introduction

In the minimal supersymmetric standard model (MSSM) [1], the SU(2) symmetry is broken via two Higgs doublets; the first doublet couples to down-type fermions only while the second couples to up-type fermions. This leads to five physical Higgs bosons: two neutral CP-even bosons, h and H, one neutral CP-odd boson A, and two charged bosons H±. The neutral Higgs bosons are collectively denoted as ϕ. At leading order the mass spectrum and the couplings of the Higgs bosons are determined by only two parameters, conventionally chosen to be tanβ, the ratio of the two Higgs doublet vacuum expectation values, and MA, the mass of the pseudoscalar Higgs boson. Radiative corrections introduce additional dependencies on other model parameters. Although tanβ is a free parameter in the MSSM, some indications suggest it should be large (tanβ≳20). A value of tanβ≈35[2] would naturally explain the top to bottom quark mass ratio. The observed density of dark matter also points towards high tanβ values [3].At large tanβ, one of the CP-even Higgs bosons (h or H) is approximately degenerate in mass with the A boson. In addition, they have similar couplings to fermions, which are enhanced (suppressed) by tanβ compared to the standard model (SM) for down-type (up-type) fermions. This enhancement has several consequences. First, the main decay modes become ϕ→bb¯ and ϕ→τ+τ− with respective branching ratios B(ϕ→bb¯)≈90% and B(ϕ→τ+τ−)≈10%. Secondly, the main production processes at a hadron collider involve b quarks originating from the sea. Inclusive Higgs boson production is dominated by gluon fusion (ggϕ) and bb¯ annihilation (bbϕ), as shown in Fig. 1

. The latter process may produce a b quark in the acceptance of the detector in addition to the Higgs boson. This associated production gb→ϕb (bgbϕ) is shown in Fig. 1(c). In this case, the detection of the associated b quark is a powerful experimental handle for reducing backgrounds.

MSSM Higgs boson masses below 93 GeV have been excluded by experiments at the CERN e+e− collider (LEP) [4]. The CDF and D0 Collaborations have searched for MSSM neutral Higgs bosons decaying to tau pairs both inclusively [5,6] and in association with a b quark [7]. They have also searched for bϕ→bbb production [8,9], which is challenging due to the high rate of multijet (MJ) production. Since these results have comparable sensitivities, combining them further enhances the potential reach. Recently, similar searches were performed at the LHC [10,11]. In this Letter, we present a combination of three searches performed by the D0 Collaboration in the ϕ→ττ, bϕ→bττ, and bϕ→bbb final states. Since the inclusive and bgbϕ production signal samples in the di-tau final states are not mutually exclusive, the D0 result presented in [6] cannot be directly combined with [7]. Hence, we re-analyse here the inclusive ϕ→ττ production: we require that there are no b jets, we extend the dataset to 7.3 fb−1 of integrated luminosity, and we increase the trigger acceptance and refine the treatment of systematic uncertainties. The di-tau channels are restricted to final states where one τ lepton (τμ) decays via τ→μνμντ and the other (τh) decays hadronically.2

Detector and event reconstruction

The data analysed in the different studies presented here have been recorded by the D0 detector [12]. It has a central-tracking system, consisting of a silicon microstrip tracker and a central fibre tracker, both located within a 2 T superconducting solenoidal magnet, with designs optimised for tracking and vertexing at pseudorapidities [13]|η|<3 and |η|<2.5, respectively. A liquid-argon and uranium calorimeter has a central section covering pseudorapidities |η| up to ≈1.1, and two end calorimeters that extend coverage to |η|≈4.2, with all three housed in separate cryostats [14]. An outer muon system, at |η|<2, consists of a layer of tracking detectors and scintillation trigger counters in front of 1.8 T toroids, followed by two similar layers after the toroids.The integrated luminosities (L) [15] associated with each search are summarised in Table 1

. Di-tau events were recorded using a mixture of single high-pT muon, jet, tau, muon plus jet, and muon plus tau triggers. The efficiency of this inclusive trigger condition is measured in a Z→τμτh data sample with respect to single muon triggers. We also verify this measurement in a sample of Z(→τμτh)+jets events. Depending on the kinematics and on the decay topology of the τh, the trigger efficiency ranges from 80% to 95%. For the bbb analysis, we employ triggers selecting events with at least three jets. In addition, 95% of the bbb data sample was recorded with b-tagging requirements at the trigger level. The trigger efficiency for mϕ=150 GeV is approximately 60% for events passing the analysis requirements.

Muons are reconstructed from track segments in the muon system. They are matched to tracks in the inner tracking system. The timing of associated hits in the scintillators must be consistent with the beam crossing to veto cosmic muons.Hadronic tau decays are characterised by narrow jets that are reconstructed using a jet cone algorithm with a radius of 0.3 [16] in the calorimeter and by low track multiplicity [17]. We split the τh candidates into three different categories that approximately correspond to one-prong τ decays with no π0 meson (τh type 1), one-prong decay with π0 mesons (τh type 2), and multi-prong decay (τh type 3). In addition, a neural-network-based τh identification (NNτ) has been trained to discriminate light parton jets (u, d, s quarks or gluon) from hadronic τ decays [17]. We select τh candidates requiring NNτ>0.9 (0.95 for τh type 3). This condition has an efficiency of approximately 65% while rejecting ∼99% of quark/gluon jets.Jets are reconstructed from energy deposits in the calorimeter [18] using the midpoint cone algorithm [16] with a radius of 0.5. All jets are required to have at least two reconstructed tracks originating from the pp¯ interaction vertex matched within ΔR(track,jet-axis)=(Δη)2+(Δφ)2<0.5 (where φ is the azimuthal angle). To identify jets originating from b quark decay, a neural network b-tagging algorithm (NNb) [19] has been developed. It uses lifetime-based information involving the track impact parameters and secondary vertices as inputs.The presence of neutrinos is inferred from the missing transverse energy, E̸T, which is reconstructed as the negative of the vector sum of the transverse energy of calorimeter cells with |η|<3.2, corrected for the energy scales of all reconstructed objects and for muons.3

Signal and background Monte Carlo simulation

Signal samples are generated with the LO event generator pythia[20]. The inclusive production is simulated with the SM ggϕ process. We checked that the kinematic differences between bbϕ and ggϕ do not have any impact on our final result. The associated production with a b-quark is generated with the SM gb→ϕb process. The contributions to the bϕ cross section and event kinematics from next-to-leading order (NLO) diagrams are taken into account by using mcfm[21] to calculate correction factors for the pythia generator as a function of the leading b quark pT and η in the range pTb>12 GeV and |ηb|<5.In the final states with a tau pair, the dominant backgrounds are due to Z→ττ(+jets), diboson (WW, WZ and ZZ), W+jets, tt¯ pair and MJ production, the latter being estimated from data. Diboson events are simulated with pythia while the Z+jets, W+jets, and tt¯ samples are generated using alpgen[22]. In the bbb channel, the dominant background is due to MJ production. We simulate MJ background events from the bb¯j, bb¯jj, cc¯j, cc¯jj, bb¯cc¯, and bb¯bb¯ processes, where j denotes a light parton, with the alpgen event generator. The small contribution from tt¯ production to the background is also simulated with alpgen. The contribution from other processes, such as Z+bb¯ and single top quark production, is negligible.The alpgen samples are processed through pythia for showering and hadronisation. tauola[23] is used to decay τ leptons and evtgen[24] to model b hadron decays. All samples are further processed through a detailed geant[25]-based simulation of the D0 detector. The output is then combined with data events recorded during random beam crossings to model the effects of detector noise and pile-up energy from multiple interactions and different beam crossings. Finally, the same reconstruction algorithms as for data are applied to the simulated events. Data control samples are used to correct the simulation for object identification efficiencies, energy scales and resolutions, trigger efficiencies, and the longitudinal pp¯ vertex distribution. Signal, tt¯ pair, and diboson yields are normalised to the product of their acceptance and detector efficiency (both determined from the simulation), their corresponding theoretical cross section and the luminosity.In the bbb final state, the relative contribution of the different MJ backgrounds is determined from data; its overall normalisation is constrained by a fit done in the final limit-setting procedure which exploits the dijet-mass shape differences between signal and background. In the di-tau channels, a dedicated treatment of the dominant Z→ττ background has been developed to reduce its systematic uncertainties. The simulation of the Z boson kinematics is corrected by comparing a large sample of Z→μμ events in data and in the simulation. We measure correction factors in each jet multiplicity bin as a function of the Φ⁎ quantity introduced in Ref. [26], leading jet η, and leading b-tagged jet NNb. This affects both the normalisation and the kinematic distributions. For the W+jets background, the muon predominantly arises from the W boson decay while the τh candidate is a mis-reconstructed jet. The W+jets simulation is normalised to data, for each jet multiplicity bin, using a W(→μν)+jets data control sample.4

Analysis strategy

In this section, we describe the search strategy as well as the selection of the final signal samples. Further details of the bττ and bbb analyses can be found in Refs. [7] and [8], respectively.4.1

Di-tau final states

The ττ and bττ searches follow a similar strategy. We first define a common selection by retaining events with one reconstructed pp¯ interaction vertex with at least three tracks, exactly one isolated muon and exactly one reconstructed τh. We require the muon to have a transverse momentum pTτμ>15 GeV, |ημ|<1.6, and to be isolated in the calorimeter and in the central tracking system, i.e., ΔR(μ,jet)>0.5 relative to any reconstructed jet. The τh candidate must have a transverse momentum, as measured in the calorimeter with appropriate energy corrections, pTτh>10 GeV, |ητh|<2.0, ΔR(τh,μ)>0.5 relative to any muon, and τh tracks must not be shared with any reconstructed muons in the event. The sum of the transverse momenta, pTtrk, of all tracks associated with the τh candidate must satisfy pTtrk>7/5/10 GeV, respectively, for τh types 1/2/3. We require the distance along the beam axis between the τh and the muon, at their point of closest approach to the pp¯ interaction vertex, Δz(τh,μ)<2 cm. In addition, the τh and the muon must have an opposite electric charge (OS) and a transverse mass MT(μ,E̸T)<60 GeV (100 GeV for τh type 2) where MT(μ,E̸T)=2⋅pTτμ⋅E̸T⋅[1−cosΔφ(μ,E̸T)].4.1.1

Inclusive ττ selection

For the inclusive ττ selection, we tighten the requirements on the τh transverse momentum to suppress the MJ background: pTτh>12.5 GeV (15 GeV for τh type 3) and pTtrk>12.5/7/15 GeV respectively for τh type 1/2/3. We further reduce the W+jets background by requiring MT(μ,E̸T)<40 GeV. We define Mhat, which represents the minimum centre-of-mass energy consistent with the decay of a di-tau resonance, byMhat≡(Eμτh−pzμτh+E̸T)2−|p→Tτh+p→Tτμ+E̸→T|2, where Eμτh is the energy of the μτh system and pzμτh is its momentum component along the beam axis. We require Mhat>40 GeV to suppress the MJ background. Finally, to prevent any overlap with the bττ sample, we select only events for which no jet has NNb>0.25.4.1.2

bττ selection

The complementary sample with at least one b-tagged jet with NNb>0.25 constitutes the bττ sample. This b-tagged sample suffers from large Z+jets, tt¯ and MJ backgrounds. We build separate multivariate discriminants, DMJ and Dtt¯, to discriminate against the MJ and tt¯ processes. We require DMJ>0.1 and Dtt¯>0.1, then we combine NNb, DMJ, and Dtt¯, to form a set of final discriminating variables Df (one for each τh type and mϕ) to be used in the limit-setting procedure. Further details can be found in Ref. [7].4.1.3

MJ background estimation

In both di-tau channels, the MJ background is estimated from data control samples applying two different methods. The first is based on the small correlation between the electric charge of muon and τh in MJ events. For each analysis, we select a data sample with identical criteria as the signal sample but with the two leptons having the same electric charge (SS). We subtract the residual contribution from other SM backgrounds from this MJ-dominated SS sample. We measure the ratio of the number of OS to SS events to be 1.09±0.01 and 1.07±0.01, respectively, in the ττ and bττ channels. We then multiply the SS sample yields by this ratio. This method is used in the inclusive ττ channel but it suffers from large statistical uncertainties of the bττ SS sample. Therefore, we develop an alternate method that uses a MJ-enriched control sample with identical requirements as applied to the signal samples but reversing the muon isolation criteria. In a MJ-dominated SS sample, obtained without any requirement on the number of jets (Njets), the ratio of the probabilities for a muon of a MJ-event to appear isolated or not isolated, Riso/iso¯≡P(μiso|MJ)/P(μiso¯|MJ), is measured as function of ητh, pTτh, and leading-jet pT (if Njets>0). The ratio Riso/iso¯ is then applied to the distributions of the non-isolated-muon sample, predicting the MJ background in the two signal samples. This method is used in the bττ study. In each analysis, the alternate method is used to determine the systematic uncertainty on the MJ-background normalisation.The distributions of Mhat for the ττ study and two different Df discriminants for the bττ analysis are presented in Fig. 2

. The observed data, expected signal and background yields are given in Table 2

for the two di-tau event selections.

4.2

bbb final state

In the bbb analysis, at least three jets, each satisfying pT>15 GeV, |η|<2.5 and NNb>0.775, are required. The two leading jets must have pT>25 GeV. To improve the signal sensitivity, the events are separated into two channels, containing exactly 3 or 4 jets. The data and signal yields are given in Table 3

. In addition, a likelihood discriminant, Dbbb, based on six kinematic variables is employed. Two separate likelihoods, one for the mass region 90⩽MA<140 GeV and the other for 140⩽MA<300 GeV, are used. The dominant heavy flavor multijet backgrounds are estimated using a data driven technique. The background in the triple b-tagged sample is estimated by applying a 2D-transformation in Mbb¯ and Dbbb, derived from the ratio of the number of MC events in the triple and double b-tagged samples, to the double b-tagged data sample. The method significantly reduces the sensitivity of the background model to the underlying kinematics of the simulated events and the modelling of the geometric acceptance of the detector. The appropriate composition of the simulated samples is determined by comparing the sum of the transverse momenta of the jets in each event in simulation and data for various b-tagging criteria. The invariant mass distribution of the jet pairing with the highest Dbbb value is used as the final discriminant. The distribution for the dominant 3-jet channel is shown in Fig. 3

(a). In Fig. 3(b), good agreement is observed between the data and background model in a control sample selected using an inverted likelihood criterion Dbbb<0.12.

4.3

Systematic uncertainties

Depending on the source, we consider the effect of systematic uncertainties on the normalisation and/or on the shape of the differential distributions of the final discriminants.In the di-tau channels, the Z(+jets) background uncertainties are estimated using Z/γ⁎→μ+μ− data control samples, resulting in normalisation uncertainties of 3.2% (5%) for Z(+b-tagged jets) boson production, an inclusive trigger efficiency uncertainty of 3% (common to all simulated backgrounds) and a shape-dependent uncertainty of ∼1% from the modelling of the Z boson kinematics. The MJ-background uncertainty ranges from 10% to 40% on the bττ channel yields while it is found to be shape dependent in the ττ channel (up to 100% at high Mhat). For the remaining backgrounds and for signal, we consider uncertainties affecting the normalisation: luminosity (6.1%), muon reconstruction efficiency (2.9%), τh reconstruction efficiency (4–10%), single muon trigger efficiency (1.3%), tt¯ (11%) and diboson (7%) production cross sections. Further sources of uncertainty affecting the shape of the final discriminant are considered: the jet energy scale (10%) and the modelling of the b-tagging efficiency (∼4%) mostly affect the bττ signal modelling but are negligible in the ττ channel, while the τh energy scale (∼10%) only impacts significantly the ττ search for both Z boson background and signal Mhat distribution. With the exception of the τh reconstruction efficiency, τh energy scale and MJ estimation, which are evaluated for each τh type, these uncertainties are assumed to be 100% correlated across both di-tau channels.In the bbb channel, for the dominant MJ background, only systematic variations in the shape of the Mbb¯ distribution are considered, as only the shape, and not the normalisation, is used to distinguish signal from background [8]. The dominant sources arise from the measurement of the rate at which light partons fake a heavy flavor jet and the b-tagging efficiency. For the signal model, the b-tagging efficiency (11–18%), the luminosity (6.1%) and the jet energy scale (2–10%) dominate the experimental uncertainties.Most of the experimental uncertainties are uncorrelated between the di-tau and the bbb analyses with the exceptions of the b-quark efficiency, luminosity, and jet energy scale, which are assumed to be 100% correlated. The theoretical uncertainties on the signal are other sources of correlated systematic uncertainty among all channels. They are dominated by parton density function uncertainties, renormalisation and factorisation scales. We assign an uncertainty of 15% on the theoretical cross sections that is correlated across all processes.5

Results

We combine the ττ, bττ and bbb channels using the modified frequentist approach [27]. The test statistic is a negative log-ratio of profiled likelihoods [28]:LLR=−2lnp(data|H1)p(data|H0), where H1 is the test (background+signal) hypothesis, H0 is the null (background only) hypothesis and p are the profile likelihoods based on Poisson probabilities for obtaining the observed number of events under each hypothesis. We define CLs by CLs≡CLs+b/CLb, where CLs+b and CLb are the confidence levels for the test and null hypothesis respectively. We exclude signal yields with CLs<0.05.The LLR quantity is computed from the Mhat distribution for the ττ channel, the Df distributions for the bττ channel and the Mbb¯ distribution for the bbb channel. The NNLO SM cross sections σggϕ and σbbϕ are taken from [29–36] and [37], respectively, while the NLO SM cross section σbgbϕ is taken from mcfm. The model-dependent MSSM to SM cross section ratios are computed with feynhiggs[38]. To avoid double counting between the bbϕ and bgbϕ processes, we obtain the expected signal yield Nττ+Xexp in the di-tau channels byNττ+XexpL=Aggϕ×σggϕmodel+Abbϕ×(σbbϕmodel−σbgbϕmodel)+Abgbϕ×σbgbϕmodel, where the acceptances A are computed using the simulation and include the experimental efficiency. The two first terms of this equation refers to Higgs boson production without any b quark within the acceptance, while the third term is used for bgbϕ production. There is no difference in the experimental acceptance for the ggϕ and bbϕ processes with no outgoing b quark within the acceptance. Therefore, we set Abbϕ≡Aggϕ. The Higgs boson width, calculated with feynhiggs, is also taken into account [8].We test two MSSM benchmark scenarios [39], no-mixing and mhmax, and we vary the sign of the Higgsino mass parameter, μ. The expected sensitivities for two mhmax scenarios are shown in Fig. 4

for the three different searches and for their combination. At low MA, the bττ channel dominates the sensitivity. For intermediate MA, the ττ and bττ channels have similar sensitivities, while at high MA, the bbb sensitivity becomes appreciable especially in μ<0 scenarios. While the sensitivity in the ττ+X channels are barely sensitive to other MSSM parameters than MA and tanβ, the bbb signal yields is much more model dependent. Therefore we also provide a combination of the ττ and bττ searches only. We do not observe any significant excess in data above the expected background fluctuations and we proceed to set limits. The limit from the ττ+X combination is shown in Fig. 5

and the full combination limits in different MSSM scenarios are shown in Fig. 6

In summary, we present MSSM Higgs boson searches in three final states: ττ, bττ and bbb. These different searches are combined to set limits in the (tanβ,MA) plane in four different MSSM scenarios. Furthermore, we combine the ττ and bττ channels to obtain MSSM-scenario-independent limits. We exclude a substantial region of the MSSM parameter space, especially for MA<180 GeV where we exclude tanβ>20–30. These are the tightest constraints from the Tevatron on the production of neutral Higgs bosons in the MSSM and are comparable to the published LHC limits [10,11], especially at low MA.

Acknowledgements

We thank the staffs at Fermilab and collaborating institutions, and acknowledge support from the DOE and NSF (USA); CEA and CNRS/IN2P3 (France); FASI, Rosatom and RFBR (Russia); CNPq, FAPERJ, FAPESP and FUNDUNESP (Brazil); DAE and DST (India); Colciencias (Colombia); CONACyT (Mexico); KRF and KOSEF (Korea); CONICET and UBACyT (Argentina); FOM (The Netherlands); STFC and the Royal Society (United Kingdom); MSMT and GACR (Czech Republic); CRC Program and NSERC (Canada); BMBF and DFG (Germany); SFI (Ireland); The Swedish Research Council (Sweden); and CAS and CNSF (China).

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