Search for new particles leading to Z + jets final states in <em>pp</em> collisions at s√=1.96  TeV

Article

Reference

Search for new particles leading to Z + jets final states in pp collisions at s√=1.96  TeV

CDF Collaboration

CAMPANELLI, Mario (Collab.), et al.

Abstract

We present the results of a search for new particles that lead to a Z boson plus jets in pp collisions at s√=1.96  TeV using the Collider Detector at Fermilab (CDF II). A data sample with a luminosity of 1.06  fb−1 collected using Z boson decays to ee and μμ is used. We describe a completely data-based method to predict the dominant background from standard model Z+jet events. This method can be similarly applied to other analyses requiring background predictions in multijet environments, as shown when validating the method by predicting the background from W+jets in tt production. No significant excess above the background prediction is observed, and a limit is set using a fourth generation quark model to quantify the acceptance. Assuming BR(b′→bZ)=100% and using a leading-order calculation of the b′ cross section, b′ quark masses below 268  GeV/c2 are excluded at 95% confidence level.

CDF Collaboration, CAMPANELLI, Mario (Collab.), et al . Search for new particles leading to Z + jets final states in pp collisions at s√=1.96  TeV. Physical Review. D , 2007, vol. 76, no. 07, p.

072006

DOI : 10.1103/PhysRevD.76.072006

Available at:

http://archive-ouverte.unige.ch/unige:38440

Disclaimer: layout of this document may differ from the published version.

Search for new particles leading to Z jets final states in p p collisions at p s

1:96 TeV

T. Aaltonen,²³A. Abulencia,²⁴J. Adelman,¹³T. Affolder,¹⁰T. Akimoto,⁵⁵M. G. Albrow,¹⁷S. Amerio,⁴³D. Amidei,³⁵ A. Anastassov,⁵²K. Anikeev,¹⁷A. Annovi,¹⁹J. Antos,¹⁴M. Aoki,⁵⁵G. Apollinari,¹⁷T. Arisawa,⁵⁷A. Artikov,¹⁵ W. Ashmanskas,¹⁷A. Attal,³A. Aurisano,⁵³F. Azfar,⁴²P. Azzi-Bacchetta,⁴³P. Azzurri,⁴⁶N. Bacchetta,⁴³W. Badgett,¹⁷

A. Barbaro-Galtieri,²⁹V. E. Barnes,⁴⁸B. A. Barnett,²⁵S. Baroiant,⁷V. Bartsch,³¹G. Bauer,³³P.-H. Beauchemin,³⁴ F. Bedeschi,⁴⁶S. Behari,²⁵G. Bellettini,⁴⁶J. Bellinger,⁵⁹A. Belloni,³³D. Benjamin,¹⁶A. Beretvas,¹⁷J. Beringer,²⁹ T. Berry,³⁰A. Bhatti,⁵⁰M. Binkley,¹⁷D. Bisello,⁴³I. Bizjak,³¹R. E. Blair,²C. Blocker,⁶B. Blumenfeld,²⁵A. Bocci,¹⁶

A. Bodek,⁴⁹V. Boisvert,⁴⁹G. Bolla,⁴⁸A. Bolshov,³³D. Bortoletto,⁴⁸J. Boudreau,⁴⁷A. Boveia,¹⁰B. Brau,¹⁰ L. Brigliadori,⁵C. Bromberg,³⁶E. Brubaker,¹³J. Budagov,¹⁵H. S. Budd,⁴⁹S. Budd,²⁴K. Burkett,¹⁷G. Busetto,⁴³ P. Bussey,²¹A. Buzatu,³⁴K. L. Byrum,²S. Cabrera,^16,qM. Campanelli,²⁰M. Campbell,³⁵F. Canelli,¹⁷A. Canepa,⁴⁵ S. Carrillo,^18,iD. Carlsmith,⁵⁹R. Carosi,⁴⁶S. Carron,³⁴B. Casal,¹¹M. Casarsa,⁵⁴A. Castro,⁵P. Catastini,⁴⁶D. Cauz,⁵⁴ M. Cavalli-Sforza,³A. Cerri,²⁹L. Cerrito,^31,mS. H. Chang,²⁸Y. C. Chen,¹M. Chertok,⁷G. Chiarelli,⁴⁶G. Chlachidze,¹⁷

F. Chlebana,¹⁷I. Cho,²⁸K. Cho,²⁸D. Chokheli,¹⁵J. P. Chou,²²G. Choudalakis,³³S. H. Chuang,⁵²K. Chung,¹² W. H. Chung,⁵⁹Y. S. Chung,⁴⁹M. Cilijak,⁴⁶C. I. Ciobanu,²⁴M. A. Ciocci,⁴⁶A. Clark,²⁰D. Clark,⁶M. Coca,¹⁶ G. Compostella,⁴³M. E. Convery,⁵⁰J. Conway,⁷B. Cooper,³¹K. Copic,³⁵M. Cordelli,¹⁹G. Cortiana,⁴³F. Crescioli,⁴⁶ C. Cuenca Almenar,^7,qJ. Cuevas,^11,lR. Culbertson,¹⁷J. C. Cully,³⁵S. DaRonco,⁴³M. Datta,¹⁷S. D’Auria,²¹T. Davies,²¹

D. Dagenhart,¹⁷P. de Barbaro,⁴⁹S. De Cecco,⁵¹A. Deisher,²⁹G. De Lentdecker,^49,cG. De Lorenzo,³M. Dell’Orso,⁴⁶ F. Delli Paoli,⁴³L. Demortier,⁵⁰J. Deng,¹⁶M. Deninno,⁵D. De Pedis,⁵¹P. F. Derwent,¹⁷G. P. Di Giovanni,⁴⁴C. Dionisi,⁵¹

B. Di Ruzza,⁵⁴J. R. Dittmann,⁴M. D’Onofrio,³C. Do¨rr,²⁶S. Donati,⁴⁶P. Dong,⁸J. Donini,⁴³T. Dorigo,⁴³S. Dube,⁵² J. Efron,³⁹R. Erbacher,⁷D. Errede,²⁴S. Errede,²⁴R. Eusebi,¹⁷H. C. Fang,²⁹S. Farrington,³⁰I. Fedorko,⁴⁶W. T. Fedorko,¹³

R. G. Feild,⁶⁰M. Feindt,²⁶J. P. Fernandez,³²R. Field,¹⁸G. Flanagan,⁴⁸R. Forrest,⁷S. Forrester,⁷M. Franklin,²² J. C. Freeman,²⁹I. Furic,¹³M. Gallinaro,⁵⁰J. Galyardt,¹²J. E. Garcia,⁴⁶F. Garberson,¹⁰A. F. Garfinkel,⁴⁸C. Gay,⁶⁰ H. Gerberich,²⁴D. Gerdes,³⁵S. Giagu,⁵¹P. Giannetti,⁴⁶K. Gibson,⁴⁷J. L. Gimmell,⁴⁹C. Ginsburg,¹⁷N. Giokaris,^15,a M. Giordani,⁵⁴P. Giromini,¹⁹M. Giunta,⁴⁶G. Giurgiu,²⁵V. Glagolev,¹⁵D. Glenzinski,¹⁷M. Gold,³⁷N. Goldschmidt,¹⁸

J. Goldstein,^42,bA. Golossanov,¹⁷G. Gomez,¹¹G. Gomez-Ceballos,³³M. Goncharov,⁵³O. Gonza´lez,³²I. Gorelov,³⁷ A. T. Goshaw,¹⁶K. Goulianos,⁵⁰A. Gresele,⁴³S. Grinstein,²²C. Grosso-Pilcher,¹³R. C. Group,¹⁷U. Grundler,²⁴ J. Guimaraes da Costa,²²Z. Gunay-Unalan,³⁶C. Haber,²⁹K. Hahn,³³S. R. Hahn,¹⁷E. Halkiadakis,⁵²A. Hamilton,²⁰

B.-Y. Han,⁴⁹J. Y. Han,⁴⁹R. Handler,⁵⁹F. Happacher,¹⁹K. Hara,⁵⁵D. Hare,⁵²M. Hare,⁵⁶S. Harper,⁴²R. F. Harr,⁵⁸ R. M. Harris,¹⁷M. Hartz,⁴⁷K. Hatakeyama,⁵⁰J. Hauser,⁸C. Hays,⁴²M. Heck,²⁶A. Heijboer,⁴⁵B. Heinemann,²⁹ J. Heinrich,⁴⁵C. Henderson,³³M. Herndon,⁵⁹J. Heuser,²⁶D. Hidas,¹⁶C. S. Hill,^10,bD. Hirschbuehl,²⁶A. Hocker,¹⁷

A. Holloway,²²S. Hou,¹M. Houlden,³⁰S.-C. Hsu,⁹B. T. Huffman,⁴²R. E. Hughes,³⁹U. Husemann,⁶⁰J. Huston,³⁶ J. Incandela,¹⁰G. Introzzi,⁴⁶M. Iori,⁵¹A. Ivanov,⁷B. Iyutin,³³E. James,¹⁷D. Jang,⁵²B. Jayatilaka,¹⁶D. Jeans,⁵¹ E. J. Jeon,²⁸S. Jindariani,¹⁸W. Johnson,⁷M. Jones,⁴⁸K. K. Joo,²⁸S. Y. Jun,¹²J. E. Jung,²⁸T. R. Junk,²⁴T. Kamon,⁵³

P. E. Karchin,⁵⁸Y. Kato,⁴¹Y. Kemp,²⁶R. Kephart,¹⁷U. Kerzel,²⁶V. Khotilovich,⁵³B. Kilminster,³⁹D. H. Kim,²⁸ H. S. Kim,²⁸J. E. Kim,²⁸M. J. Kim,¹⁷S. B. Kim,²⁸S. H. Kim,⁵⁵Y. K. Kim,¹³N. Kimura,⁵⁵L. Kirsch,⁶S. Klimenko,¹⁸

M. Klute,³³B. Knuteson,³³B. R. Ko,¹⁶K. Kondo,⁵⁷D. J. Kong,²⁸J. Konigsberg,¹⁸A. Korytov,¹⁸A. V. Kotwal,¹⁶ A. C. Kraan,⁴⁵J. Kraus,²⁴M. Kreps,²⁶J. Kroll,⁴⁵N. Krumnack,⁴M. Kruse,¹⁶V. Krutelyov,¹⁰T. Kubo,⁵⁵S. E. Kuhlmann,²

T. Kuhr,²⁶N. P. Kulkarni,⁵⁸Y. Kusakabe,⁵⁷S. Kwang,¹³A. T. Laasanen,⁴⁸S. Lai,³⁴S. Lami,⁴⁶S. Lammel,¹⁷ M. Lancaster,³¹R. L. Lander,⁷K. Lannon,³⁹A. Lath,⁵²G. Latino,⁴⁶I. Lazzizzera,⁴³T. LeCompte,²J. Lee,⁴⁹J. Lee,²⁸

Y. J. Lee,²⁸S. W. Lee,^53,oR. Lefe`vre,²⁰N. Leonardo,³³S. Leone,⁴⁶S. Levy,¹³J. D. Lewis,¹⁷C. Lin,⁶⁰C. S. Lin,¹⁷ M. Lindgren,¹⁷E. Lipeles,⁹A. Lister,⁷D. O. Litvintsev,¹⁷T. Liu,¹⁷N. S. Lockyer,⁴⁵A. Loginov,⁶⁰M. Loreti,⁴³R.-S. Lu,¹

D. Lucchesi,⁴³P. Lujan,²⁹P. Lukens,¹⁷G. Lungu,¹⁸L. Lyons,⁴²J. Lys,²⁹R. Lysak,¹⁴E. Lytken,⁴⁸P. Mack,²⁶ D. MacQueen,³⁴R. Madrak,¹⁷K. Maeshima,¹⁷K. Makhoul,³³T. Maki,²³P. Maksimovic,²⁵S. Malde,⁴²S. Malik,³¹ G. Manca,³⁰A. Manousakis,^15,aF. Margaroli,⁵R. Marginean,¹⁷C. Marino,²⁶C. P. Marino,²⁴A. Martin,⁶⁰M. Martin,²⁵ V. Martin,^21,gM. Martı´nez,³R. Martı´nez-Balları´n,³²T. Maruyama,⁵⁵P. Mastrandrea,⁵¹T. Masubuchi,⁵⁵H. Matsunaga,⁵⁵ M. E. Mattson,⁵⁸R. Mazini,³⁴P. Mazzanti,⁵K. S. McFarland,⁴⁹P. McIntyre,⁵³R. McNulty,^30,fA. Mehta,³⁰P. Mehtala,²³ S. Menzemer,^11,hA. Menzione,⁴⁶P. Merkel,⁴⁸C. Mesropian,⁵⁰A. Messina,³⁶T. Miao,¹⁷N. Miladinovic,⁶J. Miles,³³ R. Miller,³⁶C. Mills,¹⁰M. Milnik,²⁶A. Mitra,¹G. Mitselmakher,¹⁸A. Miyamoto,²⁷S. Moed,²⁰N. Moggi,⁵B. Mohr,⁸

C. S. Moon,²⁸R. Moore,¹⁷M. Morello,⁴⁶P. Movilla Fernandez,²⁹J. Mu¨lmensta¨dt,²⁹A. Mukherjee,¹⁷Th. Muller,²⁶ R. Mumford,²⁵P. Murat,¹⁷M. Mussini,⁵J. Nachtman,¹⁷A. Nagano,⁵⁵J. Naganoma,⁵⁷K. Nakamura,⁵⁵I. Nakano,⁴⁰

A. Napier,⁵⁶V. Necula,¹⁶C. Neu,⁴⁵M. S. Neubauer,⁹J. Nielsen,^29,oL. Nodulman,²O. Norniella,³E. Nurse,³¹S. H. Oh,¹⁶ Y. D. Oh,²⁸I. Oksuzian,¹⁸T. Okusawa,⁴¹R. Oldeman,³⁰R. Orava,²³K. Osterberg,²³C. Pagliarone,⁴⁶E. Palencia,¹¹ V. Papadimitriou,¹⁷A. Papaikonomou,²⁶A. A. Paramonov,¹³B. Parks,³⁹S. Pashapour,³⁴J. Patrick,¹⁷G. Pauletta,⁵⁴ M. Paulini,¹²C. Paus,³³D. E. Pellett,⁷A. Penzo,⁵⁴T. J. Phillips,¹⁶G. Piacentino,⁴⁶J. Piedra,⁴⁴L. Pinera,¹⁸K. Pitts,²⁴

C. Plager,⁸L. Pondrom,⁵⁹X. Portell,³O. Poukhov,¹⁵N. Pounder,⁴²F. Prakoshyn,¹⁵A. Pronko,¹⁷J. Proudfoot,² F. Ptohos,^19,eG. Punzi,⁴⁶J. Pursley,²⁵J. Rademacker,^42,bA. Rahaman,⁴⁷V. Ramakrishnan,⁵⁹N. Ranjan,⁴⁸I. Redondo,³² B. Reisert,¹⁷V. Rekovic,³⁷P. Renton,⁴²M. Rescigno,⁵¹S. Richter,²⁶F. Rimondi,⁵L. Ristori,⁴⁶A. Robson,²¹T. Rodrigo,¹¹ E. Rogers,²⁴S. Rolli,⁵⁶R. Roser,¹⁷M. Rossi,⁵⁴R. Rossin,¹⁰P. Roy,³⁴A. Ruiz,¹¹J. Russ,¹²V. Rusu,¹³H. Saarikko,²³ A. Safonov,⁵³W. K. Sakumoto,⁴⁹G. Salamanna,⁵¹O. Salto´,³L. Santi,⁵⁴S. Sarkar,⁵¹L. Sartori,⁴⁶K. Sato,¹⁷P. Savard,³⁴

A. Savoy-Navarro,⁴⁴T. Scheidle,²⁶P. Schlabach,¹⁷E. E. Schmidt,¹⁷M. P. Schmidt,⁶⁰M. Schmitt,³⁸T. Schwarz,⁷ L. Scodellaro,¹¹A. L. Scott,¹⁰A. Scribano,⁴⁶F. Scuri,⁴⁶A. Sedov,⁴⁸S. Seidel,³⁷Y. Seiya,⁴¹A. Semenov,¹⁵ L. Sexton-Kennedy,¹⁷A. Sfyrla,²⁰S. Z. Shalhout,⁵⁸M. D. Shapiro,²⁹T. Shears,³⁰P. F. Shepard,⁴⁷D. Sherman,²² M. Shimojima,^55,kM. Shochet,¹³Y. Shon,⁵⁹I. Shreyber,²⁰A. Sidoti,⁴⁶P. Sinervo,³⁴A. Sisakyan,¹⁵A. J. Slaughter,¹⁷ J. Slaunwhite,³⁹K. Sliwa,⁵⁶J. R. Smith,⁷F. D. Snider,¹⁷R. Snihur,³⁴M. Soderberg,³⁵A. Soha,⁷S. Somalwar,⁵²V. Sorin,³⁶

J. Spalding,¹⁷F. Spinella,⁴⁶T. Spreitzer,³⁴P. Squillacioti,⁴⁶M. Stanitzki,⁶⁰A. Staveris-Polykalas,⁴⁶R. St. Denis,²¹ B. Stelzer,⁸O. Stelzer-Chilton,⁴²D. Stentz,³⁸J. Strologas,³⁷D. Stuart,¹⁰J. S. Suh,²⁸A. Sukhanov,¹⁸H. Sun,⁵⁶I. Suslov,¹⁵

T. Suzuki,⁵⁵A. Taffard,^24,pR. Takashima,⁴⁰Y. Takeuchi,⁵⁵R. Tanaka,⁴⁰M. Tecchio,³⁵P. K. Teng,¹K. Terashi,⁵⁰ J. Thom,^17,dA. S. Thompson,²¹E. Thomson,⁴⁵P. Tipton,⁶⁰V. Tiwari,¹²S. Tkaczyk,¹⁷D. Toback,⁵³S. Tokar,¹⁴ K. Tollefson,³⁶T. Tomura,⁵⁵D. Tonelli,⁴⁶S. Torre,¹⁹D. Torretta,¹⁷S. Tourneur,⁴⁴W. Trischuk,³⁴S. Tsuno,⁴⁰Y. Tu,⁴⁵ N. Turini,⁴⁶F. Ukegawa,⁵⁵S. Uozumi,⁵⁵S. Vallecorsa,²⁰N. van Remortel,²³A. Varganov,³⁵E. Vataga,³⁷F. Vazquez,^18,i

G. Velev,¹⁷C. Vellidis,^46,aG. Veramendi,²⁴V. Veszpremi,⁴⁸M. Vidal,³²R. Vidal,¹⁷I. Vila,¹¹R. Vilar,¹¹T. Vine,³¹ M. Vogel,³⁷I. Vollrath,³⁴I. Volobouev,^29,oG. Volpi,⁴⁶F. Wu¨rthwein,⁹P. Wagner,⁵³R. G. Wagner,²R. L. Wagner,¹⁷ J. Wagner,²⁶W. Wagner,²⁶R. Wallny,⁸S. M. Wang,¹A. Warburton,³⁴D. Waters,³¹M. Weinberger,⁵³W. C. Wester III,¹⁷

B. Whitehouse,⁵⁶D. Whiteson,^45,pA. B. Wicklund,²E. Wicklund,¹⁷G. Williams,³⁴H. H. Williams,⁴⁵P. Wilson,¹⁷ B. L. Winer,³⁹P. Wittich,^17,dS. Wolbers,¹⁷C. Wolfe,¹³T. Wright,³⁵X. Wu,²⁰S. M. Wynne,³⁰A. Yagil,⁹ K. Yamamoto,⁴¹J. Yamaoka,⁵²T. Yamashita,⁴⁰C. Yang,⁶⁰U. K. Yang,^13,jY. C. Yang,²⁸W. M. Yao,²⁹G. P. Yeh,¹⁷ J. Yoh,¹⁷K. Yorita,¹³T. Yoshida,⁴¹G. B. Yu,⁴⁹I. Yu,²⁸S. S. Yu,¹⁷J. C. Yun,¹⁷L. Zanello,⁵¹A. Zanetti,⁵⁴I. Zaw,²²

X. Zhang,²⁴J. Zhou,⁵²and S. Zucchelli⁵ (CDF Collaboration)

1Institute of Physics, Academia Sinica, Taipei, Taiwan 11529, Republic of China

2Argonne National Laboratory, Argonne, Illinois 60439, USA

3Institut de Fisica d’Altes Energies, Universitat Autonoma de Barcelona, E-08193, Bellaterra (Barcelona), Spain

4Baylor University, Waco, Texas 76798, USA

5Istituto Nazionale di Fisica Nucleare, University of Bologna, I-40127 Bologna, Italy

6Brandeis University, Waltham, Massachusetts 02254, USA

7University of California, Davis, Davis, California 95616, USA

8University of California, Los Angeles, Los Angeles, California 90024, USA

9University of California, San Diego, La Jolla, California 92093, USA

10University of California, Santa Barbara, Santa Barbara, California 93106, USA

11Instituto de Fisica de Cantabria, CSIC-University of Cantabria, 39005 Santander, Spain

12Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA

13Enrico Fermi Institute, University of Chicago, Chicago, Illinois 60637, USA

14Comenius University, 842 48 Bratislava, Slovakia and Institute of Experimental Physics, 040 01 Kosice, Slovakia

15Joint Institute for Nuclear Research, RU-141980 Dubna, Russia

16Duke University, Durham, North Carolina 27708, USA

17Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA

18University of Florida, Gainesville, Florida 32611, USA

19Laboratori Nazionali di Frascati, Istituto Nazionale di Fisica Nucleare, I-00044 Frascati, Italy

20University of Geneva, CH-1211 Geneva 4, Switzerland

21Glasgow University, Glasgow G12 8QQ, United Kingdom

22Harvard University, Cambridge, Massachusetts 02138, USA

23Division of High Energy Physics, Department of Physics, University of Helsinki and Helsinki Institute of Physics, FIN-00014, Helsinki, Finland

24University of Illinois, Urbana, Illinois 61801, USA

25The Johns Hopkins University, Baltimore, Maryland 21218, USA

26Institut fu¨r Experimentelle Kernphysik, Universita¨t Karlsruhe, 76128 Karlsruhe, Germany

27High Energy Accelerator Research Organization (KEK), Tsukuba, Ibaraki 305, Japan

28Center for High Energy Physics, Kyungpook National University, Taegu 702-701, Korea, Seoul National University, Seoul 151-742, Korea, and SungKyunKwan University, Suwon 440-746, Korea

29Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA

30University of Liverpool, Liverpool L69 7ZE, United Kingdom

31University College London, London WC1E 6BT, United Kingdom

32Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas, E-28040 Madrid, Spain

33Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

34Institute of Particle Physics, McGill University, Montre´al, Canada H3A 2T8 and University of Toronto, Toronto, Canada M5S 1A7

35University of Michigan, Ann Arbor, Michigan 48109, USA

36Michigan State University, East Lansing, Michigan 48824, USA

37University of New Mexico, Albuquerque, New Mexico 87131, USA

38Northwestern University, Evanston, Illinois 60208, USA

39The Ohio State University, Columbus, Ohio 43210, USA

40Okayama University, Okayama 700-8530, Japan

41Osaka City University, Osaka 588, Japan

42University of Oxford, Oxford OX1 3RH, United Kingdom

43University of Padova, Istituto Nazionale di Fisica Nucleare, Sezione di Padova-Trento, I-35131 Padova, Italy

44LPNHE, Universite Pierre et Marie Curie/IN2P3-CNRS, UMR7585, Paris, F-75252 France

45University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

46Istituto Nazionale di Fisica Nucleare Pisa, Universities of Pisa, Siena and Scuola Normale Superiore, I-56127 Pisa, Italy

47University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA

48Purdue University, West Lafayette, Indiana 47907, USA

49University of Rochester, Rochester, New York 14627, USA

50The Rockefeller University, New York, New York 10021, USA

51Istituto Nazionale di Fisica Nucleare, Sezione di Roma 1, University of Rome ‘‘La Sapienza,’’ I-00185 Roma, Italy

52Rutgers University, Piscataway, New Jersey 08855, USA

53Texas A&M University, College Station, Texas 77843, USA

54Istituto Nazionale di Fisica Nucleare, University of Trieste/Udine, Italy

55University of Tsukuba, Tsukuba, Ibaraki 305, Japan

56Tufts University, Medford, Massachusetts 02155, USA

57Waseda University, Tokyo 169, Japan

58Wayne State University, Detroit, Michigan 48201, USA

59University of Wisconsin, Madison, Wisconsin 53706, USA

60Yale University, New Haven, Connecticut 06520, USA (Received 25 June 2007; published 18 October 2007)

We present the results of a search for new particles that lead to aZboson plus jets inppcollisions at s

p 1:96 TeV using the Collider Detector at Fermilab (CDF II). A data sample with a luminosity of 1:06 fb¹ collected usingZboson decays toeeandis used. We describe a completely data-based method to predict the dominant background from standard modelZjet events. This method can be similarly applied to other analyses requiring background predictions in multijet environments, as shown when validating the method by predicting the background fromWjets inttproduction. No significant

pVisitor from: University of California, Irvine, Irvine, CA 92697, USA.

oVisitor from: Texas Tech University, Lubbock, TX 79409, USA.

nVisitor from: University of California Santa Cruz, Santa Cruz, CA 95064, USA.

mVisitor from: University of London, Queen Mary College, London, E1 4NS, England.

lVisitor from: University de Oviedo, E-33007 Oviedo, Spain.

kVisitor from: Nagasaki Institute of Applied Science, Nagasaki, Japan.

hVisitor from: University of Heidelberg, D-69120 Heidelberg, Germany.

qVisitor from: IFIC(CSIC-Universitat de Valencia), 46071 Valencia, Spain.

gVisitor from: University of Edinburgh, Edinburgh EH9 3JZ, United Kingdom.

fVisitor from: University College Dublin, Dublin 4, Ireland.

eVisitor from: University of Cyprus, Nicosia CY-1678, Cyprus.

dVisitor from: Cornell University, Ithaca, NY 14853, USA.

cVisitor from: University Libre de Bruxelles, B-1050 Brussels, Belgium.

bVisitor from: University of Bristol, Bristol BS8 1TL, United Kingdom.

iVisitor from: Universidad Iberoamericana, Mexico D.F., Mexico.

jVisitor from: University of Manchester, Manchester M13 9PL, England.

aVisitor from: University of Athens, 15784 Athens, Greece.

Z . . .

excess above the background prediction is observed, and a limit is set using a fourth generation quark model to quantify the acceptance. AssumingBRb⁰!bZ 100%and using a leading-order calculation of theb⁰cross section,b⁰quark masses below268 GeV=c²are excluded at 95% confidence level.

DOI:10.1103/PhysRevD.76.072006 PACS numbers: 13.85.Rm, 13.85.Qk, 14.70.Hp, 14.80.j

I. INTRODUCTION

This paper presents a search for new particles decaying to Z gauge bosons created in pp collisions at

ps 1:96 TeV with the CDF II detector at the Fermilab Tevatron, extending and complementing other work with such final states [1–4]. A variety of extensions to the standard model predict new particles with couplings toZ bosons [5–9]. We wish to discover or rule out these types of models, while maintaining model independence in the search. That is, while these theories offer guidance about the possible characteristics of physics beyond the standard model, they do not necessarily correspond to what actually exists in nature, and so the analysis is not tailored to specific models.

Of course, some assumptions are necessary in choosing how to discriminate between the standard model back- ground and new signals. We examine final states with Z bosons and additional jets. In particular, we focus on final states in which there are at least 3 jets, each with at least 30 GeV of transverse energy E_T. This assumption was motivated by studying the optimal kinematic selection of a specific model, the fourth generation model [5]. In the fourth generation model, an additional pair of heavy quarks is added to the standard model’s three. The production mechanisms of the new down-type quark (called the b⁰) would be identical to that of the top quark, with pair- production having the largest cross section. Depending on its mass, the direct tree-level decays of the b⁰ could be either kinematically forbidden or heavily Cabibbo- suppressed. These situations could give rise to a large branching ratio of b⁰!bZ via a loop diagram. While the selection was chosen as the optimal set of kinematic cuts using this model as a signal, this analysis constrains all models withZ3jet final states.

The dominant background for this final state is from standard modelZ production with jets from higher-order QCD processes. A leading-order calculation of this back- ground is insufficient. Use of higher-order calculations is complicated because it involves hard-scattering matrix elements in combination with soft nonperturbative QCD processes. Recent next-to-leading-order (NLO) predictions [10] have been used [11] with the aid of Monte Carlo simulations to account for the nonperturbative overlap.

Any such method requires validation with data. In this paper, we develop a different approach that uses the data as more than a validation tool, and uses it alone for the background estimation. In this approach, we extrapolate the jet transverse energy distributions from a low energy

control region of the data into the high energy signal region.

This paper is organized as follows. Section II contains a brief overview of the portions of the CDF II detector relevant to this measurement. Section III lists the trigger requirements and describes and motivates the signal sam- ple selections. Section IV lists the backgrounds. Section V describes, validates, and applies the method of predicting the dominant background. In Sec. VI the predictions for the remaining backgrounds are described. In Sec. VII we present the results of the search, and conclude in Sec. VIII.

II. THE CDF II DETECTOR

The CDF II detector is described in detail elsewhere [12]; here, only the portions required for this analysis are described. We first describe the coordinate system conven- tions. In the CDF coordinate system, the origin is the center of the detector, and thezaxis is along the beam axis, with positivezdefined as the proton beam direction. Thexaxis points radially outward from the Tevatron ring, leaving the y axis direction perpendicular to the earth’s surface with positive direction upward. Spherical coordinates are used where appropriate, in whichis the polar angle (zero in the positivezdirection),is the azimuthal angle (zero in the positive xdirection), and the pseudorapidity is defined by lntan=2. At hadron colliders, transverse energies and momenta are usually the appropriate physical quantities, defined by E_T Esin and p_T psin (whereEis a particle’s energy andpis the magnitude of a particle’s momentum).

A tracking system is situated directly outside the beam pipe and measures the trajectories and momenta of charged particles. The innermost part of the tracking system is the silicon detector, providing position measurements on up to 8 layers of sensors in the radial region 1:3< r <28 cm and the polar regionjj&2:5. Outside of this detector lies the central outer tracker (COT), an open-cell drift chamber providing measurements on up to 96 layers in the radial region 40< r <137 cm and the polar region jj&1.

Directly outside of the COT a solenoid provides a 1.4 T magnetic field, allowing particle momenta to be obtained from the trajectory measurements in this known field.

Surrounding the tracking system, segmented electro- magnetic (EM) and hadronic calorimeters measure particle energies. In the central region, the calorimeters are ar- ranged in a projective barrel geometry and cover the polar region jj<1:2. In the forward region, the calorimeters are arranged in a projective ‘‘end-plug’’ geometry and cover the polar region 1:2<jj<3:5. Two sets of drift

chambers, one directly outside the hadronic calorimeter and another outside additional steel shielding, measure muon trajectories in the region jj<0:6; another set of drift chambers similarly detects muons in the region0:6<

jj<1. Muon scintillators surround these drift chambers in the region jj<1 for trigger purposes. A luminosity measurement is provided by Cherenkov detectors in the region3:7<jj<4:7via a measurement of the average number ofpp collisions per crossing [13].

Collision events of interest are selected for analysis offline using a three level trigger system, with each level accepting events for processing at the next level. At level 1, custom hardware enables fast decisions using rudimentary tracking information and a simple counting of recon- structed objects. At level 2, trigger processors enable de- cisions based on partial event reconstruction. At level 3, a computer farm running fast event reconstruction software makes the final decision on event storage.

III. DATA SAMPLE AND EVENT SELECTION We first describe the baseline Z selection, and then describe the kinematic selection used to discriminate the potential signal from the standard model background. The kinematic selection is chosen and backgrounds are pre- dicteda priori, before looking in the signal region. While remaining as data-driven as possible throughout the analy- sis, Monte Carlo simulation is used in some studies, con- sistency checks, and for illustration purposes. In all cases, the Monte Carlo events are generated withPYTHIA[14] and the detector responses are modeled with aGEANTsimula- tion [15] as described in [16].

A. BaselineZselection

The data sample consists ofZ!eeandZ!can- didate events collected using single electron and muon triggers. The electron trigger requires at least one central electromagnetic energy cluster with E_T>18 GeV and a matching track with p_T>9 GeV=c. The muon trigger requires at least one central track with p_T>18 GeV=c with matching hits in the muon drift chambers. The aver- age integrated luminosity of these data samples is 1:06 fb¹ [17].

Zcandidate events are selected offline by requiring at least one pair of electron or muon candidates both with p_T>20 GeV=c and invariant mass in the range 81<

M_‘‘<101 GeV=c². The electron and muon identification variables are described in detail in Refs. [16,18]. The selection is described briefly here. To increase efficiency, only one of the lepton pair has stringent identification requirements (the ‘‘tight’’ candidate), while on the other lepton the identification requirements are relaxed (the

‘‘loose’’ candidate).

Loose electron candidates consist of well-isolated EM calorimeter clusters with low energy in the hadronic calo- rimeter; in the central part of the detector (jj<1:2) well-

measured tracks from the COT are required; in the forward parts of the detector (jj>1:2) no track is required, but the shower shape in the EM calorimeter is required to be consistent with that expected from electrons. Tight electron candidates have all the requirements of loose candidates, and are additionally required to be central (jj<1:2), to have a shower shape consistent with that expected from electrons, to have calorimeter position and energy mea- surements consistent with its matching track, and to have no nearby tracks consistent with that expected in electrons from photon conversions.

Loose muon candidates consist of well-measured tracks in the COT and well-isolated EM and hadronic calorimeter clusters with minimal energy deposits. Tight muon candi- dates have all the requirements of loose candidates, and are additionally required to have matching hits in the muon drift chambers.

Finally, all electron and muon pairs are required to be consistent with originating from the same zvertex and to have a time-of-flight difference (as measured by the COT) inconsistent with that expected for cosmic rays. They are also required to be separated inby an angle greater than 5 to remove two lepton candidates misreconstructed from a single lepton.

Using this selection, the distribution of M_‘‘ is plotted and compared to standard modelZMonte Carlo simulation in Fig.1.

2

) (GeV/c M

50 100 150

2

Events/GeV/c

1 10 10

10

10

FIG. 1 (color online). Distribution ofM‘‘ofZ!eeandZ! data (black points and errors) using the baselineZselection described in the text. Overlaid are standard model Z!eeand Z! Monte Carlo events, normalized to the number of events expected with the given luminosities using the expected cross section of 250 pb.

Z . . .

B. Kinematic selection

The analysis focuses on topologies with large numbers of highly energetic jets in the final state, for which the signal (from the decay of heavy objects) can be better separated from standard model Zjet production. Jets are clustered using the ‘‘MIDPOINT’’ clustering algorithm [19] with a cone size of 0.4 radians. Corrections are applied to extrapolate the jet energies back to the parton level using a generic jet response [20]. Jets are required to havejj<

2.

The following discriminators are used:

N_jet^X Number of jets in the event withE_T> XGeV;

J^X_T Scalar sum of E_T of jets in the event withE_T

> X GeV:

The thresholdsX as well as the cut values on these varia- bles are determined by optimization [21]. In the optimiza- tion we use the figure of merit S=1:5

B

p (whereSis the expected number of signal events andBis the expected number of background events) to quantify the sensitivity as a compromise between best discovery and best limit po- tential [22,23]. In the low background region (B1), maximizing this figure of merit is equivalent to maximiz- ing the signal efficiency. In the high background region (B1), this figure of merit has the same behavior as S=

B

p . For the optimization study, pp !b⁰b⁰ Monte Carlo simulations with a range of masses are used as the signalS. Standard modelZMonte Carlo simulations are used for the backgroundB.

In order to be sensitive to a range of masses, we must take into account the generic behavior of new signals: as mass increases the cross section decreases while the trans- verse energy spectra become harder. Therefore, to be opti- mally sensitive to higher mass signals, we cut at larger values of N_jet and J_T thus removing more of the back- ground to give sensitivity to the lower cross sections.

For the sake of simplicity, we desire that our selection only changes gradually with mass and uses the same E_T threshold on all jets. With a simple selection, the data- based background prediction method becomes easier. To confirm that this desire for simplicity does not consider- ably reduce the search sensitivity, and to understand what cut values and thresholds to use, we first establish a ‘‘tar- get’’ selection. The target selection is defined as the selec- tion with the highest sensitivity when placing cuts on the individual jet E_T’s and J_T. This is found by scanning through all possible cuts on J_T¹⁰ (that is, J_T is calculated with a 10 GeV threshold on the jets) and all possible E_T thresholds for up to 4 jets (ordered byE_T), and finding the point with the optimal sensitivity. In this scan, step sizes of 10 GeV are used for the jetE_Tthresholds, and a step size of 50 GeV is used forJ¹⁰_T . This scan is done independently for b⁰ masses in the range 100m_b⁰ 350 GeV=c² with a step size of50 GeV=c².

The optimal points found by this scan for ab⁰ mass of 150 GeV=c² are shown in column 2 of TableI. These cut values give the best possible sensitivity at this mass point when placing cuts on the individual jet E_T’s and J¹⁰_T . Again, we wish to choose a simple selection that gradually changes as a function of mass, and use the target sensitiv- ities at all mass points for comparison. Based on the optimal target points for b⁰ masses in the range 100 m_b⁰ 350 GeV=c², we choose the simpler requirements ofN_jet³⁰3andJ¹⁰_T > m_b⁰c². The sensitivity of the simple requirements is compared to the target sensitivity in col- umn 3 of TableIfor the150 GeV=c²mass point.

From the table it is apparent that, for m_b⁰ 150 GeV=c², the sensitivity of the simple cuts is only negligibly less than the target sensitivity. We find the same to be true for all mass points studied, except for the m_b⁰ 100 GeV=c² mass point. In that case, however, the sensitivity of the simple cuts is still adequate because of the larger cross sections for lower mass particles [24]. In addition, low masses near100 GeV=c²are less interesting as they are already more tightly excluded [25]. Thus, we conclude that the simpler selection of N_jet³⁰3andJ_T¹⁰>

m_b⁰c² is nearly optimal for the mass range of interest.

In the above, J_T was calculated using a 10 GeV E_T threshold on the jets. For the purposes of the background estimation, it is simpler to use the sameE_Tthreshold onJ_T as one uses on the N_jet variable. Therefore, a 30 GeV threshold is used when calculating J_T. This was found to give a small decrease in sensitivity in theb⁰model with the benefit of a gain in simplicity.

The kinematic jet selection was found to be optimal when using the fourth generation model as the signal.

When optimizing using the figure of meritS=1:5 pB

, the optimal point is independent of the normalization of the

TABLE I. Optimal point compared with the simple selection of N³⁰_jet3 and J¹⁰_T >150, for the m_b⁰150 GeV=c² mass point. Here, N_sig is the number of signal events expected in 1 fb¹ after the given selection usingb⁰ Monte Carlo simula- tions. N_bkg is the number of background events expected in 1 fb¹ after the given selection using standard model Z Monte Carlo simulations. In this optimization study,2:710⁵ standard model Z events were used; 1500 signal events were used (both counted before jet selection).

Variable Values from scan Values of simple selection

E^{jet 1}_T thresh.: 50 30

E^{jet 2}_T thresh.: 30 30

E^{jet 3}_T thresh.: 30 30

E^{jet 4}_T thresh.: 20 0

J_T¹⁰cut: 0 150

N_sig: 48.5 75.5

N_bkg: 2.60 13.8

S=1:5 pB

: 15.6 14.5

signal. That is, any model with a different cross section but the same kinematic distributions will give the same optimal point. In addition, the shape of the kinematic distributions are mostly determined by theb⁰mass. We therefore expect that this selection is nearly optimal for all models with heavy particles produced in pairs and decaying toZjet.

In general, this selection is sensitive to any model with highE_T jets in the final state. It may not be optimal for an arbitrary model, but designing a simple selection that is optimal for the entire class ofZhighE_Tjet models is not possible.

In this optimization, we assumed new signals would lead to final states consisting of a Zboson and many highE_T jets. Of course, some assumption about signal character- istics must be made in order to understand how to separate signal from background. These assumptions will naturally reduce the model independence of the search. There is a trade-off between the specificity of these assumptions and the sensitivity to a particular model. For example, in nearly all new physics models with Z boson final states, the transverse momentum spectrum of the Z is harder than for standard modelZproduction. This is because, in these models, the Z is usually a decay product of a massive particle. One would conclude that the Z transverse mo- mentum is a very model-independent variable, and there- fore well motivated. However, we find, in the b⁰ model sensitivity study, that the jet kinematic requirements have much higher sensitivity than theZtransverse momentum.

The cost of this sensitivity is a loss of generality: with this assumption we are no longer sensitive to Z final states without highE_T jets. The sensitivity of theb⁰ model can be further enhanced by requiring b jets using displaced vertices (because of theb⁰!bZdecay), again with a cost to generality. In our analysis, as a compromise between model independence and sensitivity, we choose to require additional jets in the event.

To summarize, after selecting Z!ee and Z! events, the kinematic selection is:

(i) N³⁰_jet3, and (ii) J³⁰_T > m_b⁰c².

That is, Zevents with N³⁰_jet 3 are selected, and the J_T³⁰ distribution is scanned for an excess. Step sizes of 50 GeV are used.

IV. BACKGROUNDS

In the signal region described above, there are potential backgrounds from the following sources:

(i) single-Zproduction in conjunction with jets, (ii) multijet events, where two jets fake leptons, (iii) cosmic rays coincident with multijet events,

(iv) WZjets, where theW decays to jets, (v) ZZjets, where one of theZ’s decays to jets, (vi) WWjets, where bothW’s decay to leptons, and (vii) ttjets, where bothW’s decay to leptons.

The dominant background is from standard model single-Zproduction in conjunction with jets. Since beyond leading-log order diagrams make potentially large contri- butions to events with N_jet³⁰3, calculation of this back- ground from theoretical first principles is extremely difficult, and therefore would require careful validation with data. Rather than using data as merely a validation tool we take a different approach, and instead measure the background directly from data, and with data alone. The following section is devoted to describing this prediction technique for the dominant background from Zjet. As this technique has not been applied previously, it is ex- plained thoroughly, with careful validation studies de- scribed. The remaining backgrounds are estimated in Sec. VI.

V. DATA-BASEDZJET BACKGROUND PREDICTION TECHNIQUE

Given the above selection, there are two tasks: the total number of background events with N_jet³⁰3must be pre- dicted, and the shape of the J³⁰_T distribution after this cut must be predicted. When combined, these two components give the full normalized J_T³⁰ distribution prediction. The background for events withN_jet³⁰3and anyJ³⁰_T cut can be obtained from this distribution. The method for predicting each of the two components is described separately in the following two sections.

In each of the prediction methods, fits to various jetE_T distributions are used. A parametrization that describes the shapes of these jet E_T distributions well is therefore re- quired. The parametrization used is

fE_T p₀e^ET=p1

E_T^p2 ; (1) where the p_i are fitted parameters. This parametrization was motivated by observations in Monte Carlo simulations, control regions of data, and phenomenological studies that:

at lowE_T, the jetE_Tshape follows a power law function; at high E_T, it follows an exponential decay function. The above parametrization satisfies these limiting behaviors.

With the above convention, the parameter p₁ has dimen- sions of energy, the parameter p₂ is dimensionless, and both parameters are positive. Further discussion and moti- vation for this parametrization is provided in [18].

A. Number of events withN³⁰_jet3

In order to predict the total number of events withN_jet³⁰ 3, we use the jet E_T distributions in the N_jet³⁰2control regions. Since jets are counted above an E_T threshold (in this case 30 GeV), the N_jet distribution is completely determined from the jet E_T distributions. To illustrate this, and to describe the method, standard model Z! Monte Carlo simulations are used. After validation with control samples, the method is applied to theZdata.

Z . . .

In Fig.2, the E_T distribution of the third highest jet is shown. By construction, a cut on N_jet³⁰2 separates this distribution into two regions. This distribution can be fit in the E_T<30 GeV region and extrapolated to the E_T >

30 GeVregion to get the expected number of background events withN_jet³⁰3.

We fit the parametrization from Eq. (1) to the jet E_T distribution of Fig.2, and show the results in Fig.3[26].

The fit matches well the broad features of the distribution above 30 GeV. The number of events withN_jet³⁰3is then predicted by integrating the fitted distribution from 30 GeV to infinity. The fit prediction obtained with this method (with its uncertainty from fit parameter error propagation described in Sec. V C) is116¹⁰₁₃events (with the number of generated Monte Carlo events having an equivalent lumi- nosity of 7 fb¹). The number of events observed in the simulated data with N³⁰_jet 3 is 152. In this case, the extrapolation predicts the background to within 31 16%. The level of consistency will be evaluated further in the validation studies with data in Sec. V D.

This method, using the jet E_T distributions to predict integrals of theN_jetdistribution, can clearly be extended to other analyses as well. For illustration purposes only we describe other examples here, still using standard model Z!Monte Carlo simulation. Consider predicting the total number of events withN_jet⁸⁰1(that is, we require at least one jet with anE_T threshold of 80 GeV). In this case, a fit to the highestE_Tjet distribution below 80 GeV can be extrapolated to above that threshold, as in Fig.4. (Note that the highestE_T distribution in this figure is harder than the third highest E_T jet distribution, as one expects when

ordering the jets by E_T). It is clear that the extrapolation describes the distribution reasonably well.

If we instead wish to predict the number of events with N⁴⁰_jet1, we must fit the same E_T distribution below

(GeV) highest jet E

3

0 20 40 60 80 100

Events/GeV

10

10

1 10 10

10

10

/dof: 14.0/12 χ2

FIG. 3 (color online). E_Tdistribution of the third highestE_Tjet in standard modelZ!Monte Carlo events. The distribution is fit to Eq. (1) in the range15< E_T<30 GeV, and extrapolated to theET>30 GeVregion. In this and following figures, when comparing binned histograms to unbinned fits, we place the x-value of each bin at the average of the entries in that bin.

(GeV) Highest jet E

0 100 200 300

Events/GeV

10

10

1 10 10

10

10

10

< 80 GeV region Fit in 20 < ET

< 40 GeV region Fit in 20 < ET

FIG. 4 (color online). E_T of the highest E_T jet in standard model Z! Monte Carlo events. The distribution is fit to Eq. (1) in the region20< E_T<80 GeV(dotted line), and again in the region20< ET<40 GeV(solid line).

(GeV) highest jet E

3

0 20 40 60 80 100

Events/GeV

10

10

1 10 10

10

10

10

≤ 2

30

Njet N³⁰_jet ≥3

FIG. 2 (color online). E_Tdistribution of the third highestE_Tjet in standard model Z! Monte Carlo simulations. Events with N_jet³⁰2 have E_T<30 GeV; events with N_jet³⁰3 have E_T>30 GeV.

40 GeV and extrapolate it to above that threshold, also shown in Fig.4. It is clear that the extrapolation does not describe the highE_Tportion of the distribution well. There is a large systematic uncertainty present in extrapolations that use such a small portion of the distribution that the shape cannot be reliably obtained. This can be mitigated by raising the E_T threshold, unless the shape of the jet E_T distribution at highE_T can be otherwise constrained. In the case examined in this analysis, we fit the third highestE_T jet (which has a softerE_T distribution than the highestE_T jet) in the regionE_T<30 GeV. We have checked that the data in this region constrains the shape sufficiently with validation studies using control samples of data and Monte Carlo simulations, described later in Sec. V D.

From the above, it is apparent that one can estimate the background for events with N_jet^X n by fitting the E_T distribution of the nth highest E_T jet in the region E_T <

Xand extrapolating the fit to the regionE_T> X, as long as the fit regionE_T< Xconstrains the shape sufficiently.

B.J_Tshape determination

We now describe the method used to determine the shape of the J³⁰_T distribution of events with N³⁰_jet3.

After finding the shape, it is then normalized to the number of events with N³⁰_jet3found by the above method. We again use standard modelZ!Monte Carlo events to explain the method, and later will apply it to data.

SinceJ_T³⁰ is simply the sum of the individual jet trans- verse energies above 30 GeV, if theE_T distributions of jets for events withN_jet³⁰3are known, theJ³⁰_T distribution can be predicted for these events. We extrapolate the shape of these jet E_T distributions from the jetE_T distributions of N_jet³⁰2events. In order to do such an extrapolation, we must understand the variation of the jetE_Tdistribution as a function ofN³⁰_jet.

TheE_T distributions of all jets in events withN³⁰_jet1 and 2, normalized to have equal area, is shown in Fig. 5 using Z!‘‘ data. The general shape is similar, though jets inN³⁰_jet2events have a slightly harder tail at highE_T. We model this by fitting to each jetE_T distribution (using Eq. (1)) and extrapolating the fit parameters to N³⁰_jet3 events. To avoid simultaneously extrapolating two fit pa- rameters we only extrapolate the exponential parameter (p₁), as this parameter governs the high E_T behavior in our parametrization. In order to extrapolate only this pa- rameter, we fit the N_jet³⁰1 E_T spectrum allowing both parameters to float freely, then fix the power law parameter (p₂) in the fit to the N_jet³⁰2 E_T spectrum. We then extrapolate thep₁ parameter of Eq. (1) linearly as a func- tion ofN_jet³⁰, from their fitted values atN_jet³⁰1andN³⁰_jet 2into the regionN_jet³⁰3.

Figures6 and7show the fits of the spectra for events with 1 and 2 jets. Figure8shows the linear extrapolation of

the exponential parameters. For illustration, the exponen- tial parameter obtained from a fit to theE_T distribution in N³⁰_jet3events (again fixing the power law parameter to that found in the N³⁰_jet1 events) is shown in the same figure. The extrapolation reasonably predicts the parameter for events withN_jet³⁰3[27].

(GeV)

T

E

50 100 150 200 250 300

Arbitrary normalization

10

10

10

10

10

10

1

= 1 bin

30

All jets in the Njet

= 2 bin

30

All jets in the Njet

FIG. 5 (color online). ETdistribution of jets inN³⁰_jet1events (open squares) and in N³⁰_jet2events (solid circles) in Z!‘‘

data. Events with higherN_jet³⁰have harderE_Tspectra.

(GeV) E

0 100 200 300 400 500

Arbitrary Normalization

10

10

10

10

10

10

/dof: 70.7/80 χ2

FIG. 6 (color online). E_Tdistribution of jets inN³⁰_jet1events in standard modelZ!Monte Carlo events. The distribution is fit to Eq. (1) in the rangeET>30.

Z . . .