application/xmlSearch for the standard model Higgs boson produced in association with a Z boson in [formula omitted] of [formula omitted] collisions at [formula omitted] using the CDF II detectorCDF CollaborationT. AaltonenB. Álvarez GonzálezS. AmerioD. AmideiA. AnastassovA. AnnoviJ. AntosG. ApollinariJ.A. AppelT. ArisawaA. ArtikovJ. AsaadiW. AshmanskasB. AuerbachA. AurisanoF. AzfarW. BadgettT. BaeA. Barbaro-GaltieriV.E. BarnesB.A. BarnettP. BarriaP. BartosM. BauceF. BedeschiS. BehariG. BellettiniJ. BellingerD. BenjaminA. BeretvasA. BhattiD. BiselloI. BizjakK.R. BlandB. BlumenfeldA. BocciA. BodekD. BortolettoJ. BoudreauA. BoveiaL. BrigliadoriC. BrombergE. BruckenJ. BudagovH.S. BuddK. BurkettG. BusettoP. BusseyA. BuzatuA. CalambaC. CalanchaS. CamardaM. CampanelliM. CampbellF. CanelliB. CarlsD. CarlsmithR. CarosiS. CarrilloS. CarronB. CasalM. CasarsaA. CastroP. CatastiniD. CauzV. CavaliereM. Cavalli-SforzaA. CerriL. CerritoY.C. ChenM. ChertokG. ChiarelliG. ChlachidzeF. ChlebanaK. ChoD. ChokheliW.H. ChungY.S. ChungM.A. CiocciA. ClarkC. ClarkeG. CompostellaM.E. ConveryJ. ConwayM. CorboM. CordelliC.A. CoxD.J. CoxF. CrescioliJ. CuevasR. CulbertsonD. DagenhartN. dʼAscenzoM. DattaP. de BarbaroM. DellʼOrsoL. DemortierM. DeninnoF. DevotoM. dʼErricoA. Di CantoB. Di RuzzaJ.R. DittmannM. DʼOnofrioS. DonatiP. DongM. DorigoT. DorigoK. EbinaA. ElaginA. EppigR. ErbacherS. ErredeN. ErshaidatR. EusebiS. FarringtonM. FeindtJ.P. FernandezR. FieldG. FlanaganR. ForrestM.J. FrankM. FranklinJ.C. FreemanY. FunakoshiI. FuricM. GallinaroJ.E. GarciaA.F. GarfinkelP. GarosiH. GerberichE. GerchteinS. GiaguV. GiakoumopoulouP. GiannettiK. GibsonC.M. GinsburgN. GiokarisP. GirominiG. GiurgiuV. GlagolevD. GlenzinskiM. GoldD. GoldinN. GoldschmidtA. GolossanovG. GomezG. Gomez-CeballosM. GoncharovO. GonzálezI. GorelovA.T. GoshawK. GoulianosS. GrinsteinC. Grosso-PilcherR.C. GroupJ. Guimaraes da CostaS.R. HahnE. HalkiadakisA. HamaguchiJ.Y. HanF. HappacherK. HaraD. HareM. HareR.F. HarrK. HatakeyamaC. HaysM. HeckJ. HeinrichM. HerndonS. HewamanageA. HockerW. HopkinsD. HornS. HouR.E. HughesM. HurwitzU. HusemannN. HussainM. HusseinJ. HustonG. IntrozziM. IoriA. IvanovE. JamesD. JangB. JayatilakaE.J. JeonS. JindarianiM. JonesK.K. JooS.Y. JunT.R. JunkT. KamonP.E. KarchinA. KasmiY. KatoW. KetchumJ. KeungV. KhotilovichB. KilminsterD.H. KimH.S. KimJ.E. KimM.J. KimS.B. KimS.H. KimY.K. KimY.J. KimN. KimuraM. KirbyS. KlimenkoK. KnoepfelK. KondoD.J. KongJ. KonigsbergA.V. KotwalM. KrepsJ. KrollD. KropM. KruseV. KrutelyovT. KuhrM. KurataS. KwangA.T. LaasanenS. LamiS. LammelM. LancasterR.L. LanderK. LannonA. LathG. LatinoT. LeCompteE. LeeH.S. LeeJ.S. LeeS.W. LeeS. LeoS. LeoneJ.D. LewisA. LimosaniC.-J. LinM. LindgrenE. LipelesA. ListerD.O. LitvintsevC. LiuH. LiuQ. LiuT. LiuS. LockwitzA. LoginovD. LucchesiJ. LueckP. LujanP. LukensG. LunguJ. LysR. LysakR. MadrakK. MaeshimaP. MaestroS. MalikG. MancaA. Manousakis-KatsikakisF. MargaroliC. MarinoM. MartínezP. MastrandreaK. MateraM.E. MattsonA. MazzacaneP. MazzantiK.S. McFarlandP. McIntyreR. McNultyA. MehtaP. MehtalaC. MesropianT. MiaoD. MietlickiA. MitraH. MiyakeS. MoedN. MoggiM.N. MondragonC.S. MoonR. MooreM.J. MorelloJ. MorlockP. Movilla FernandezA. MukherjeeTh. MullerP. MuratM. MussiniJ. NachtmanY. NagaiJ. NaganomaI. NakanoA. NapierJ. NettC. NeuM.S. NeubauerJ. NielsenL. NodulmanS.Y. NohO. NorniellaL. OakesS.H. OhY.D. OhI. OksuzianT. OkusawaR. OravaL. OrtolanS. Pagan GrisoC. PagliaroneE. PalenciaV. PapadimitriouA.A. ParamonovJ. PatrickG. PaulettaM. PauliniC. PausD.E. PellettA. PenzoT.J. PhillipsG. PiacentinoE. PianoriJ. PilotK. PittsC. PlagerL. PondromS. PoprockiK. PotamianosF. ProkoshinA. PrankoF. PtohosG. PunziA. RahamanV. RamakrishnanN. RanjanI. RedondoP. RentonM. RescignoT. RiddickF. RimondiL. RistoriA. RobsonT. RodrigoT. RodriguezE. RogersS. RolliR. RoserF. RuffiniA. RuizJ. RussV. RusuA. SafonovW.K. SakumotoY. SakuraiL. SantiK. SatoV. SavelievA. Savoy-NavarroP. SchlabachA. SchmidtE.E. SchmidtT. SchwarzL. ScodellaroA. ScribanoF. ScuriS. SeidelY. SeiyaA. SemenovF. SforzaS.Z. ShalhoutT. ShearsP.F. ShepardM. ShimojimaM. ShochetI. Shreyber-TeckerA. SimonenkoP. SinervoK. SliwaJ.R. SmithF.D. SniderA. SohaV. SorinH. SongP. SquillaciotiM. StancariR. St. DenisB. StelzerO. Stelzer-ChiltonD. StentzJ. StrologasG.L. StryckerY. SudoA. SukhanovI. SuslovK. TakemasaY. TakeuchiJ. TangM. TecchioP.K. TengJ. ThomJ. ThomeG.A. ThompsonE. ThomsonP. TiptonD. TobackS. TokarK. TollefsonT. TomuraD. TonelliS. TorreD. TorrettaP. TotaroM. TrovatoF. UkegawaS. UozumiA. VarganovF. VázquezG. VelevC. VellidisM. VidalI. VilaR. VilarJ. VizánM. VogelG. VolpiP. WagnerR.L. WagnerT. WakisakaR. WallnyS.M. WangA. WarburtonD. WatersW.C. Wester IIID. WhitesonA.B. WicklundE. WicklundS. WilburF. WickH.H. WilliamsJ.S. WilsonP. WilsonB.L. WinerP. WittichS. WolbersH. WolfeT. WrightX. WuZ. WuK. YamamotoD. YamatoT. YangU.K. YangY.C. YangW.-M. YaoG.P. YehK. YiJ. YohK. YoritaT. YoshidaG.B. YuI. YuS.S. YuJ.C. YunA. ZanettiY. ZengC. ZhouS. ZucchelliCDFHiggs boson searchesMultivariate techniquesPhysics Letters B 715 (2012) 98-104. doi:10.1016/j.physletb.2012.07.045journalPhysics Letters BCopyright © 2012 Elsevier B.V. All rights reserved.Elsevier B.V.0370-26937151-329 August 20122012-08-2998-1049810410.1016/j.physletb.2012.07.045http://dx.doi.org/10.1016/j.physletb.2012.07.045doi:10.1016/j.physletb.2012.07.045http://vtw.elsevier.com/data/voc/oa/OpenAccessStatus#Full2013-07-16T20:28:17Zhttp://vtw.elsevier.com/data/voc/oa/SponsorType#Otherhttp://creativecommons.org/licenses/by/3.0/

Journals

S300.3

PLB28763S0370-2693(12)00796-410.1016/j.physletb.2012.07.045Elsevier B.V.Experiments

Fig. 1

Final discriminant output distributions for the three tag categories (ST, LJP, DT) used in this analysis. The distributions shown are for the discriminant trained on mH=115 GeV/c2 signal events. The ZH signal is shown, for mH=115 GeV/c2, and drawn scaled up by a factor of 25.

Fig. 2

Limits on the Higgs boson production cross section times the H→bb¯ branching ratio, given as a ratio to the standard model expected value.

Table 1

Expected background and observed data events for the three independent signal regions. Also shown is the expected number of ZH signal events, for a SM Higgs boson with mH=115 GeV/c2. Quoted uncertainties include both systematic and statistical contributions.

ProcessSTLJPDTZ+LF, Z+cc¯683±6561±97.6±1.2Z+bb¯287±7258±1542±10tt¯69±729±226±3Diboson42±39.5±0.76.7±0.6Other46±123.4±0.30.2±0.1Background1127±134160±2382±15Data114316085ZH (Predicted)4.5±0.41.8±0.11.7±0.1Table 2

Expected and observed 95% CL limits on the ZH production cross section times H→bb¯ branching ratio, relative to the expected standard model value, for each Higgs mass (in GeV/c2) hypothesis.

mH100105110115120125130135140145150Exp.2.73.13.43.94.75.57.08.7121728Obs.2.83.34.44.85.44.96.67.3101422Search for the standard model Higgs boson produced in association with a Z boson in 7.9 fb−1 of pp¯ collisions at s=1.96 TeV using the CDF II detector

CDF Collaboration

Aaltonen

Álvarez González

Amerio

Amidei

Anastassov

Annovi

Antos

Apollinari

J.A.

Appel

Arisawa

Artikov

Asaadi

Ashmanskas

Auerbach

Aurisano

Azfar

Badgett

Bae

Barbaro-Galtieri

V.E.

Barnes

B.A.

Barnett

Barria

Bartos

Bauce

Bedeschi

Behari

Bellettini

Bellinger

Benjamin

Beretvas

Bhatti

Bisello

Bizjak

K.R.

Bland

Blumenfeld

Bocci

Bodek

Bortoletto

Boudreau

Boveia

Brigliadori

Bromberg

Brucken

Budagov

H.S.

Budd

Burkett

Busetto

Bussey

Buzatu

Calamba

Calancha

Camarda

Campanelli

Campbell

Canelli

Carls

Carlsmith

Carosi

Carrillo

Carron

Casal

Casarsa

Castro

Catastini

Cauz

Cavaliere

Cavalli-Sforza

Cerri

Cerrito

Y.C.

Chen

Chertok

Chiarelli

Chlachidze

Chlebana

Cho

Chokheli

W.H.

Chung

Y.S.

Chung

M.A.

Ciocci

Clark

Clarke

Compostella

M.E.

Convery

Conway

Corbo

Cordelli

C.A.

Cox

D.J.

Cox

Crescioli

Cuevas

Culbertson

Dagenhart

dʼAscenzo

Datta

de Barbaro

DellʼOrso

Demortier

Deninno

Devoto

dʼErrico

Di Canto

Di Ruzza

J.R.

Dittmann

DʼOnofrio

Donati

Dong

Dorigo

Ebina

Elagin

Eppig

Erbacher

Errede

Ershaidat

Eusebi

Farrington

Feindt

J.P.

Fernandez

Field

Flanagan

Forrest

M.J.

Frank

Franklin

J.C.

Freeman

Funakoshi

Furic

Gallinaro

J.E.

Garcia

A.F.

Garfinkel

Garosi

Gerberich

Gerchtein

Giagu

Giakoumopoulou

Giannetti

Gibson

C.M.

Ginsburg

Giokaris

Giromini

Giurgiu

Glagolev

Glenzinski

Gold

Goldin

Goldschmidt

Golossanov

Gomez

Gomez-Ceballos

Goncharov

González

Gorelov

A.T.

Goshaw

Goulianos

Grinstein

Grosso-Pilcher

R.C.

Group

Guimaraes da Costa

S.R.

Hahn

Halkiadakis

Hamaguchi

J.Y.

Han

Happacher

Hara

Hare

R.F.

Harr

Hatakeyama

Hays

Heck

Heinrich

Herndon

Hewamanage

Hocker

Hopkins

Horn

Hou

R.E.

Hughes

Hurwitz

Husemann

Hussain

Hussein

Huston

Introzzi

Iori

Ivanov

James

Jang

Jayatilaka

E.J.

Jeon

Jindariani

Jones

K.K.

Joo

S.Y.

Jun

T.R.

Junk

Kamon

P.E.

Karchin

Kasmi

Kato

Ketchum

Keung

Khotilovich

Kilminster

D.H.

Kim

H.S.

Kim

J.E.

Kim

M.J.

Kim

S.B.

Kim

S.H.

Kim

Y.K.

Kim

Y.J.

Kim

Kimura

Kirby

Klimenko

Knoepfel

Kondo

D.J.

Kong

Konigsberg

A.V.

Kotwal

Kreps

Kroll

Krop

Kruse

Krutelyov

Kuhr

Kurata

Kwang

A.T.

Laasanen

Lami

Lammel

Lancaster

R.L.

Lander

Lannon

Lath

Latino

LeCompte

Lee

H.S.

Lee

J.S.

Lee

S.W.

Lee

Leo

Leone

J.D.

Lewis

Limosani

C.-J.

Lin

Lindgren

Lipeles

Lister

D.O.

Litvintsev

Liu

Lockwitz

Loginov

Lucchesi

Lueck

Lujan

Lukens

Lungu

Lys

Lysak

Madrak

Maeshima

Maestro

Malik

Manca

Manousakis-Katsikakis

Margaroli

Marino

Martínez

Mastrandrea

Matera

M.E.

Mattson

Mazzacane

Mazzanti

K.S.

McFarland

McIntyre

McNulty

Mehta

Mehtala

Mesropian

Miao

Mietlicki

Mitra

Miyake

Moed

Moggi

M.N.

Mondragon

C.S.

Moon

Moore

M.J.

Morello

Morlock

Movilla Fernandez

Mukherjee

Th.

Muller

Murat

Mussini

Nachtman

Nagai

Naganoma

Nakano

Napier

Nett

Neu

M.S.

Neubauer

Nielsen

Nodulman

S.Y.

Noh

Norniella

Oakes

S.H.

Y.D.

Oksuzian

Okusawa

Orava

Ortolan

Pagan Griso

Pagliarone

Palencia

Papadimitriou

A.A.

Paramonov

Patrick

Pauletta

Paulini

Paus

D.E.

Pellett

Penzo

T.J.

Phillips

Piacentino

Pianori

Pilot

⁎

jrpilot@ucdavis.edu

Pitts

Plager

Pondrom

Poprocki

Potamianos

Prokoshin

Pranko

Ptohos

Punzi

Rahaman

Ramakrishnan

Ranjan

Redondo

Renton

Rescigno

Riddick

Rimondi

Ristori

Robson

Rodrigo

Rodriguez

Rogers

Rolli

Roser

Ruffini

Ruiz

Russ

Rusu

Safonov

W.K.

Sakumoto

Sakurai

Santi

Sato

Saveliev

Savoy-Navarro

Schlabach

Schmidt

E.E.

Schmidt

Schwarz

Scodellaro

Scribano

Scuri

Seidel

Seiya

Semenov

Sforza

S.Z.

Shalhout

Shears

P.F.

Shepard

Shimojima

Shochet

Shreyber-Tecker

Simonenko

Sinervo

Sliwa

J.R.

Smith

F.D.

Snider

Soha

Sorin

Song

Squillacioti

Stancari

St. Denis

Stelzer

Stelzer-Chilton

Stentz

Strologas

G.L.

Strycker

Sudo

Sukhanov

Suslov

Takemasa

Takeuchi

Tang

Tecchio

P.K.

Teng

Thom

Thome

G.A.

Thompson

Thomson

Tipton

Toback

Tokar

Tollefson

Tomura

Tonelli

Torre

Torretta

Totaro

Trovato

Ukegawa

Uozumi

Varganov

Vázquez

Velev

Vellidis

Vidal

Vila

Vilar

Vizán

Vogel

Volpi

Wagner

R.L.

Wagner

Wakisaka

Wallny

S.M.

Wang

Warburton

Waters

W.C.

WesterIII

Whiteson

A.B.

Wicklund

Wilbur

Wick

H.H.

Williams

J.S.

Wilson

B.L.

Winer

Wittich

Wolbers

Wolfe

Wright

Yamamoto

Yamato

Yang

U.K.

Yang

Y.C.

Yang

W.-M.

Yao

G.P.

Yeh

Yoh

Yorita

Yoshida

G.B.

S.S.

J.C.

Yun

Zanetti

Zeng

Zhou

Zucchelli

aDivision of High Energy Physics, Department of Physics, University of Helsinki and Helsinki Institute of Physics, FIN-00014, Helsinki, FinlandbInstituto de Fisica de Cantabria, CSIC – University of Cantabria, 39005 Santander, SpaincIstituto Nazionale di Fisica Nucleare, Sezione di Padova-Trento, ItalydUniversity of Padova, I-35131 Padova, ItalyeUniversity of Michigan, Ann Arbor, MI 48109, USAfFermi National Accelerator Laboratory, Batavia, IL 60510, USAgLaboratori Nazionali di Frascati, Istituto Nazionale di Fisica Nucleare, I-00044 Frascati, ItalyhComenius University, 842 48 Bratislava, SlovakiaiInstitute of Experimental Physics, 040 01 Kosice, SlovakiajWaseda University, Tokyo 169, JapankJoint Institute for Nuclear Research, RU-141980 Dubna, RussialTexas A&M University, College Station, TX 77843, USAmYale University, New Haven, CT 06520, USAnUniversity of Oxford, Oxford OX1 3RH, United KingdomoCenter for High Energy Physics, Kyungpook National University, Daegu 702-701, Republic of KoreapSeoul National University, Seoul 151-742, Republic of KoreaqSungkyunkwan University, Suwon 440-746, Republic of KorearKorea Institute of Science and Technology Information, Daejeon 305-806, Republic of KoreasChonnam National University, Gwangju 500-757, Republic of KoreatChonbuk National University, Jeonju 561-756, Republic of KoreauErnest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USAvPurdue University, West Lafayette, IN 47907, USAwThe Johns Hopkins University, Baltimore, MD 21218, USAxIstituto Nazionale di Fisica Nucleare Pisa, Pisa, ItalyyUniversity of Pisa, Pisa, ItalyzUniversity of Siena, Siena, ItalyaaScuola Normale Superiore, I-56127 Pisa, ItalyabUniversity of Wisconsin, Madison, WI 53706, USAacDuke University, Durham, NC 27708, USAadThe Rockefeller University, New York, NY 10065, USAaeUniversity College London, London WC1E 6BT, United KingdomafBaylor University, Waco, TX 76798, USAagUniversity of Rochester, Rochester, NY 14627, USAahUniversity of Pittsburgh, Pittsburgh, PA 15260, USAaiEnrico Fermi Institute, University of Chicago, Chicago, IL 60637, USAajIstituto Nazionale di Fisica Nucleare, Bologna, ItalyakUniversity of Bologna, I-40127 Bologna, ItalyalMichigan State University, East Lansing, MI 48824, USAamGlasgow University, Glasgow G12 8QQ, United KingdomanInstitute of Particle Physics, McGill University, Montréal, Québec, H3A 2T8 CanadaaoSimon Fraser University, Burnaby, British Columbia, V5A 1S6 CanadaapUniversity of Toronto, Toronto, Ontario, M5S 1A7 CanadaaqTRIUMF, Vancouver, British Columbia, V6T 2A3 CanadaarCarnegie Mellon University, Pittsburgh, PA 15213, USAasCentro de Investigaciones Energeticas Medioambientales y Tecnologicas, E-28040 Madrid, SpainatInstitut de Fisica dʼAltes Energies, ICREA, Universitat Autonoma de Barcelona, E-08193 Bellaterra (Barcelona), SpainauUniversity of Illinois, Urbana, IL 61801, USAavUniversity of Florida, Gainesville, FL 32611, USAawIstituto Nazionale di Fisica Nucleare Trieste/Udine, I-34100 Trieste, ItalyaxUniversity of Udine, I-33100 Udine, ItalyayHarvard University, Cambridge, MA 02138, USAazInstitute of Physics, Academia Sinica, Taipei, Taiwan 11529, ROCbaUniversity of California, Davis, Davis, CA 95616, USAbbUniversity of Geneva, CH-1211 Geneva 4, SwitzerlandbcWayne State University, Detroit, MI 48201, USAbdUniversity of Liverpool, Liverpool L69 7ZE, United KingdombeInstitut für Experimentelle Kernphysik, Karlsruhe Institute of Technology, D-76131 Karlsruhe, GermanybfIstituto Nazionale di Fisica Nucleare, Sezione di Roma 1, ItalybgSapienza Università di Roma, I-00185 Roma, ItalybhUniversity of Athens, 157 71 Athens, GreecebiUniversity of New Mexico, Albuquerque, NM 87131, USAbjMassachusetts Institute of Technology, Cambridge, MA 02139, USAbkRutgers University, Piscataway, NJ 08855, USAblOsaka City University, Osaka 588, JapanbmUniversity of Tsukuba, Tsukuba, Ibaraki 305, JapanbnTufts University, Medford, MA 02155, USAboUniversity of Pennsylvania, Philadelphia, PA 19104, USAbpThe Ohio State University, Columbus, OH 43210, USAbqArgonne National Laboratory, Argonne, IL 60439, USAbrUniversity of Virginia, Charlottesville, VA 22906, USAbsOkayama University, Okayama 700-8530, JapanbtUniversity of California, Los Angeles, Los Angeles, CA 90024, USAbuInstitution for Theoretical and Experimental Physics, ITEP, Moscow 117259, Russia⁎Corresponding author.1

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Editor: M. Doser

Abstract

We present a search for the standard model Higgs boson produced in association with a Z boson, using up to 7.9 fb−1 of integrated luminosity from pp¯ collisions collected with the CDF II detector. We utilize several novel techniques, including multivariate lepton selection, multivariate trigger parametrization, and a multi-stage signal discriminant consisting of specialized functions trained to distinguish individual backgrounds. By increasing acceptance and enhancing signal discrimination, these techniques have significantly improved the sensitivity of the analysis above what was expected from a larger dataset alone. We observe no significant evidence for a signal, and we set limits on the ZH production cross section. For a Higgs boson with mass 115 GeV/c2, we expect (observe) a limit of 3.9 (4.8) times the standard model predicted value, at the 95% credibility level.

Keywords

CDFHiggs boson searchesMultivariate techniques

Introduction

The Higgs boson is the remaining unobserved particle of the standard model (SM) [1–3] predicted by the Higgs mechanism, which is postulated to describe the origin of electroweak symmetry breaking and elementary particle masses. Direct searches at LEP and the Tevatron have excluded SM Higgs bosons with masses (mH) below 114.4 GeV/c2[4] and in the range 156⩽mH⩽177 GeV/c2[5], respectively, at the 95% credibility level (CL). Recent results from the ATLAS and CMS experiments [6,7] have extended the excluded range of masses to 127⩽mH⩽600 GeV/c2 (at the 95% confidence level). ATLAS also excludes Higgs bosons with masses in the range 112.9⩽mH⩽115.5 GeV/c2[6].Production of Higgs bosons at the Tevatron primarily proceeds through the gluon fusion mechanism, gg→H[8]. Low-mass Higgs bosons (mH<135 GeV/c2) decay predominantly to a pair of b quarks, with a branching fraction of 79% (40%) [8] for mH=100(135) GeV/c2. Due to overwhelming QCD multijet production, low-mass searches with Higgs production via gluon fusion and H→bb¯ decay are not feasible. To overcome this difficulty, we utilize the associated production of a Higgs boson with a massive vector boson, where leptonic decays of the vector boson produce distinctive event signatures.This Letter presents a search for the SM Higgs boson using the ZH→ℓ+ℓ−bb¯ process, where ℓ is an electron (e) or muon (μ). We search for events containing two oppositely-charged leptons consistent with the decay of a Z boson, and a hadronic signature consistent with the H→bb¯ decay mode. Previous searches [9,10] by the CDF and D0 Collaborations have demonstrated that this final state provides good sensitivity to a Higgs boson signal, primarily due to the ability of the experiments to reconstruct both the Z and Higgs bosons. We study data from pp¯ collisions at s=1.96 TeV recorded by the CDF II detector. We combine two independent analyses, one with Z→e+e−[11] and one with Z→μ+μ−[12], using data corresponding to 7.5 and 7.9 fb−1 of integrated luminosity, respectively.The CDF II detector [13] consists of silicon-based and wire-drift-chamber tracking systems immersed in a 1.4 T magnetic field for particle momentum determination. Surrounding the tracking systems are electromagnetic and hadronic calorimeters, providing coverage in the pseudorapidity3232

We use a cylindrical coordinate system with z along the proton beam direction, r the perpendicular radius from the central axis of the detector, and ϕ the azimuthal angle. For θ the polar angle from the proton beam, we define η=−lntan(θ/2), transverse momentum pT=psinθ and transverse energy ET=Esinθ.

range |η|<3.6. Additional drift chambers used for muon identification are located in the outermost layer of the detector.The sensitivity of this updated analysis is enhanced by using several novel techniques following two general strategies: increasing acceptance and enhancing signal discrimination. To increase acceptance, we introduce artificial neural networks (NNs) for lepton selection, and we also use several online event-selection (trigger) algorithms not previously used. Using a new technique, we are able to accurately model the combined behavior of these triggers, allowing access to ZH candidate events beyond the reach of the previous CDF searches. To enhance signal discrimination, we form a multi-stage event discriminant organized to isolate ZH candidates from known SM and instrumental processes (backgrounds).2

Multivariate lepton identification

To improve on standard cut-based lepton identification, we instead select leptons consistent with the decay of a Z boson by using several NNs. Each NN identifies individual electrons or muons, distinguishing them from both non-leptonic candidates and true leptons not originating from Z decays. A single NN is used for muon identification, and is trained [14] to distinguish between true muons from simulated Z decays and misidentified muons from a data sample containing pairs of same-charge muon candidates. The input quantities giving the best discrimination power include the energy deposited by the muon candidate in the calorimeters, the angular separation between the muon candidate and nearest jet, and the number of hits recorded by the tracking systems. In events with Z→μμ decays well contained in the detector, the muon NN selection achieves a Z identification efficiency of ∼96%, while simultaneously rejecting ∼94% of the non-Z background. The muon NN selection has increased the signal acceptance for dimuon events by ∼10%.Detector geometry [15,16] motivates three NNs for electron identification. One is optimized for identification in the pseudorapidity range |η|<1.1, and is trained to distinguish between electrons from simulated events and both jets from collision data along with electrons from data events not considered in this analysis. The other two NNs are trained for the forward regions; one considers only candidates with a silicon-based track and the other considers candidates without such a track in the region 1.1⩽|η|⩽2.8. Again, electrons from simulated events are used in the training sample, along with electron candidates from data that may or may not have an associated track. The most discriminating variables in the electron NN include the ratio of the energy deposits in the hadronic and electromagnetic calorimeters, the electron candidate transverse momentum, and shape variables related to the shower formation in the calorimeter. Compared to the selection utilized in previous searches, the electron NN has improved the rejection of jets misidentified as electrons by a factor of five. In total, the multivariate lepton selection has increased the acceptance of the analysis by ∼20% over previous searches [9].3

Event selection

Complementary to the improved lepton identification, we add additional triggers that were not previously utilized in this analysis channel. Rather than using a single trigger with a threshold for muon pT or electron ET for the respective Z selection, we consider any event selected by any trigger in three general sets. The first set includes several triggers that select events containing muon detector and drift chamber activity indicative of a high-pT muon [17,18]. Included in this category may be triggers with lower pT thresholds than the default muon trigger. The second set of triggers selects events with a large calorimeter-energy imbalance (missing transverse energy, E̸T

The missing transverse energy, E̸→T, is defined by E̸→T=−∑iETinˆi, where i is the number of the calorimeter tower with |η|<3.6 and nˆi is a unit vector perpendicular to the beam axis and pointing at the ith calorimeter tower. We also define E̸T=|E̸→T|.

). Some of these events contain muons that are not selected with the high-pT muon trigger, thereby increasing the acceptance of this analysis. A third set of triggers selects events with activity in the calorimeter suggestive of a high-ET electron [19]. By using these sets of triggers rather than just single triggers for each lepton type, we increase the event selection acceptance by ∼10%. To model the complicated correlations between kinematic variables used in the trigger selection described above, we use a novel technique that uses NN functions to parametrize trigger efficiencies as a function of kinematic observables [11,12].Utilizing the above strategies to increase acceptance, we select events containing opposite-sign,3434

Forward (|η|>1.1) electrons are exempt from this requirement.

same-flavor lepton pairs with mℓℓ in a window ([76,106] GeV/c2) centered on the mass of the Z boson. Additionally, we require at least two jets [20], with transverse energy ET>25 GeV for the leading jet, and ET>15 GeV for all other jets. All jets are required to come from the central region of the detector, |η|<2.0.4

Background composition and signal expectation

We define a pre-tag region (PT) before applying b-quark jet identification, consisting of events with a reconstructed Z boson and two or more jets passing the criteria described above. We observe 33 975 events in the PT region, and expect a total background yield of 34200±4800 events, where the quoted uncertainty includes both systematic and statistical contributions. We expect 13.6±1.1ZH signal events in the PT region, for mH=115 GeV/c2. The dominant process in the PT region is Z+light-flavor (LF) jets (u, d, s, and gluon jets), accounting for ∼85% of the total background. Z+heavy-flavor (b and c) jets events, which contribute less than 10% of the background, are a small contribution in the PT region, but become relatively more significant in the signal regions. These processes are modeled using alpgen[21] to simulate the hard-scatter process, and pythia[22] for the subsequent hadronization. The Z+jets processes are simulated at leading order and require a K-factor of 1.4 [23] for normalization to NLO cross sections. Other small backgrounds include diboson (ZZ, WZ, and WW) events and tt¯ events, simulated entirely with pythia normalized to NLO [24] and NNLO [25] predictions, respectively. Finally, other processes, such as QCD multijet production, can produce two selected leptons in the event. For muon events, this background is modeled using same-charge muon pairs from data. For electron events, we measure the rate of jets passing the electron NN using collision data to estimate the contribution from these processes. This background accounts for ∼3% of the background in the PT region.We utilize two different b-quark-identifying algorithms to search for jets consistent with the H→bb¯ decay. The secondary vertex algorithm (SV) [26] identifies jets consistent with the decay of a long-lived b hadron by searching for displaced vertices. The SV algorithm has both a tight and a loose operating point – the loose point has better b-jet identification efficiency but also has a higher rate of jets incorrectly identified as b jets. The jet probability (JP) algorithm [27] uses track impact parameters relative to the primary vertex to construct a likelihood for all jet tracks to have originated from the primary vertex. Both algorithms have imperfect rejection of c-quark jets, allowing some events containing them to contribute to the final signal regions.We use the combination of the two highest-ET jets to form potential Higgs boson candidates. We use a hierarchy of tag combinations to define three independent signal regions. We first search for events with two tight SV tags – defining the double-tag (DT) region, the most sensitive. A second signal region includes events with one loose SV tag and one JP tag (LJP), and the third contains events with just one tight SV tag (ST). These three regions are combined to search for ZH production. Table 1

shows the expected numbers of events for the signal and background processes, as well as the observed data.

Signal discrimination

In this analysis, we use a one-dimensional signal discriminant while maintaining the simultaneous separation of tt¯ and Z+jets events from the ZH signal that was previously accomplished through a two-dimensional discriminant [9]. This method also further enhances signal discrimination by using two additional NNs in a multi-stage method, as described below.We first train a NN signal discriminant, using several kinematic variables such as the dijet mass and ET, to distinguish the signal-like (trained with ZH simulated events) and background-like (trained using a mixture of all background processes) events. Each data and simulated event is sent through the same signal discriminants, with a unique function optimized for 11 different Higgs mass hypotheses, defined in increments of 5 GeV/c2 between 100 and 150 GeV/c2.The multi-stage method defines three samples (I, II, III) where events can enter the final distributions used for limit setting. The first step involves separating tt¯ and Z+jets events. This is done using a NN function trained to separate these specific processes. A cut on the output of this discriminant is chosen to define a tt¯-enhanced sample (Sample I). Events which fail this cut and fall into Sample II or III are passed through a second NN function trained to separate b jets from charm and light flavor jets [28]. A cut on the output of this flavor separator function defines a sample containing mainly Z+cc¯ and Z+LF backgrounds (Sample II), and a region enriched in b jets (Sample III).This multi-stage approach produces final output distributions with three samples enriched in various background processes, as seen in Fig. 1

, where we add (0,1,2) to the signal discriminant output score for each event when the event falls in Sample (I, II, III) as described above. By enhancing the signal discrimination in this way, we increase the sensitivity of the analysis by ∼10% over the technique used in Ref. [9]. We use these distributions to set limits on the ZH production cross section times H→bb¯ branching ratio.

Systematic uncertainties

We evaluate several systematic uncertainties on the background and signal events. A large source of systematic uncertainty arises from the cross section values used in the normalization of events: 40%, 10%, 6%, and 5%, for Z+heavy-flavor[29], tt¯, diboson, and ZH simulated events, respectively. An uncertainty of (1,2,5)% is applied to the (ST, LJP, DT) ZH samples after measuring changes in acceptance using simulated events with more or fewer particles radiated by the incoming and outgoing partons. The mistag prediction is measured using data, and carries a rate uncertainty of 14%, 27%, and 29%, for the ST, LJP, and DT tag categories, respectively. To account for differing b-jet identification efficiencies in data and simulated events, uncertainties of 5.2% (ST), 8.7% (LJP), and 10.4% (DT) are applied to the b-tagged samples. A 6% uncertainty is applied to simulated events, accounting for uncertainty in the measurement of integrated luminosity. The trigger model applied to simulated events requires a 5% normalization uncertainty. We also apply uncertainties on the lepton reconstruction and identification efficiency (1%) and lepton energy measurement (1.5%). For muons (electrons), we measure a 5% (50%) uncertainty on the normalization of the remaining background processes, based on differences in the rates of events containing same-charge and opposite-charge lepton pairs and in the rates of jets misidentified as electrons.In addition, we account for sources of uncertainty that also include shape variations to account for the migration of events in the final signal discriminant distributions when fluctuating these shape-defining quantities within their uncertainties. These include uncertainties on the jet energies [30] as well as on the expected rate of Z+mistag events.7

Results

Comparing the observed data to our background prediction including uncertainties, we do not find any evidence of a ZH signal. We set upper limits on the ZH production cross section times H→bb¯ branching ratio using a Bayesian algorithm [31], assuming a uniform prior on the signal rate. We do this by performing simulated experiments, each with a pseudo-dataset generated by randomly varying the normalizations of background processes within their respective statistical and systematic uncertainties, taking into account all background expectations in the absence of a signal. Each simulated experiment produces an upper limit on the ZH production cross section. The median of the 95% CL upper limits from the simulated experiments is taken to be the expected 95% CL upper limit of the analysis. We define the 1-sigma and 2-sigma deviations on the expected limit as the bounds which contain 68.3% and 95.5%, respectively, of the simulated experiment results. The observed data distribution is used to set the observed limit in a similar fashion. These limits are shown graphically, along with the 1-sigma and 2-sigma ranges, in Fig. 2

. Table 2

lists the expected and observed limits for each Higgs mass hypothesis considered in this analysis. We find that the observed limit is in good agreement with the expected limit for no signal, within the 1-sigma range across all Higgs mass hypotheses.

Conclusion

In conclusion, we have performed a search for the standard model Higgs boson in the process ZH→ℓ+ℓ−bb¯. The sensitivity of this analysis has improved due to several new multivariate techniques, including multivariate lepton identification, the use of NNs to obtain trigger efficiencies for simulated events, and a novel multi-stage discriminant approach used to enhance signal discrimination. We observe no significant excess and set an upper limit on the ZH production cross section times H→bb¯ branching ratio. We expect (observe) a limit of 3.9 (4.8) times the standard model predicted value, for a Higgs boson with mass mH=115 GeV/c2, at the 95% CL. The novel techniques presented here improve the sensitivity of the analysis by ∼25% above the gain expected from the ∼85% larger dataset.

Acknowledgements

We thank the Fermilab staff and the technical staffs of the participating institutions for their vital contributions. This work was supported by the U.S. Department of Energy and National Science Foundation; the Italian Istituto Nazionale di Fisica Nucleare; the Ministry of Education, Culture, Sports, Science and Technology of Japan; the Natural Sciences and Engineering Research Council of Canada; the National Science Council of the Republic of China; the Swiss National Science Foundation; the A.P. Sloan Foundation; the Bundesministerium für Bildung und Forschung, Germany; the Korean World Class University Program, the National Research Foundation of Korea; the Science and Technology Facilities Council and the Royal Society, UK; the Russian Foundation for Basic Research; the Ministerio de Ciencia e Innovación, and Programa Consolider-Ingenio 2010, Spain; the Slovak R&D Agency; the Academy of Finland; and the Australian Research Council (ARC).

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