Measurement of the t t production cross section in p p collisions at ffiffi s p ? 1:96 TeV using soft electron b-tagging T. Aaltonen,24 J. Adelman,14 T. Akimoto,56 B. A? lvarez Gonza?lez,12,u S. Amerio,44b,44a D. Amidei,35 A. Anastassov,39 A. Annovi,20 J. Antos,15 G. Apollinari,18 A. Apresyan,49 T. Arisawa,58 A. Artikov,16 W. Ashmanskas,18 A. Attal,4 A. Aurisano,54 F. Azfar,43 W. Badgett,18 A. Barbaro-Galtieri,29 V. E. Barnes,49 B.A. Barnett,26 P. Barria,47c,47a P. Bartos,15 V. Bartsch,31 G. Bauer,33 P.-H. Beauchemin,34 F. Bedeschi,47a D. Beecher,31 S. Behari,26 G. Bellettini,47b,47a J. Bellinger,60 D. Benjamin,17 A. Beretvas,18 J. Beringer,29 A. Bhatti,51 M. Binkley,18 D. Bisello,44b,44a I. Bizjak,31,z R. E. Blair,2 C. Blocker,7 B. Blumenfeld,26 A. Bocci,17 A. Bodek,50 V. Boisvert,50 G. Bolla,49 D. Bortoletto,49 J. Boudreau,48 A. Boveia,11 B. Brau,11,b A. Bridgeman,25 L. Brigliadori,6b,6a C. Bromberg,36 E. Brubaker,14 J. Budagov,16 H. S. Budd,50 S. Budd,25 S. Burke,18 K. Burkett,18 G. Busetto,44b,44a P. Bussey,22 A. Buzatu,34 K. L. Byrum,2 S. Cabrera,17,w C. Calancha,32 M. Campanelli,36 M. Campbell,35 F. Canelli,14,18 A. Canepa,46 B. Carls,25 D. Carlsmith,60 R. Carosi,47a S. Carrillo,19,o S. Carron,34 B. Casal,12 M. Casarsa,18 A. Castro,6b,6a P. Catastini,47c,47a D. Cauz,55b,55a V. Cavaliere,47c,47a M. Cavalli-Sforza,4 A. Cerri,29 L. Cerrito,31,q S. H. Chang,28 Y. C. Chen,1 M. Chertok,8 G. Chiarelli,47a G. Chlachidze,18 F. Chlebana,18 K. Cho,28 D. Chokheli,16 J. P. Chou,23 G. Choudalakis,33 S. H. Chuang,53 K. Chung,18,p W.H. Chung,60 Y. S. Chung,50 T. Chwalek,27 C. I. Ciobanu,45 M.A. Ciocci,47c,47a A. Clark,21 D. Clark,7 G. Compostella,44a M. E. Convery,18 J. Conway,8 M. Cordelli,20 G. Cortiana,44b,44a C.A. Cox,8 D. J. Cox,8 F. Crescioli,47b,47a C. Cuenca Almenar,8,w J. Cuevas,12,u R. Culbertson,18 J. C. Cully,35 D. Dagenhart,18 M. Datta,18 T. Davies,22 P. de Barbaro,50 S. De Cecco,52a A. Deisher,29 G. De Lorenzo,4 M. Dell?Orso,47b,47a C. Deluca,4 L. Demortier,51 J. Deng,17 M. Deninno,6a P. F. Derwent,18 A. Di Canto,47b,47a G. P. di Giovanni,45 C. Dionisi,52b,52a B. Di Ruzza,55b,55a J. R. Dittmann,5 M. D?Onofrio,4 S. Donati,47b,47a P. Dong,9 J. Donini,44a T. Dorigo,44a S. Dube,53 J. Efron,40 A. Elagin,54 R. Erbacher,8 D. Errede,25 S. Errede,25 R. Eusebi,18 H. C. Fang,29 S. Farrington,43 W. T. Fedorko,14 R. G. Feild,61 M. Feindt,27 J. P. Fernandez,32 C. Ferrazza,47d,47a R. Field,19 G. Flanagan,49 R. Forrest,8 M. J. Frank,5 M. Franklin,23 J. C. Freeman,18 I. Furic,19 M. Gallinaro,52a J. Galyardt,13 F. Garberson,11 J. E. Garcia,21 A. F. Garfinkel,49 P. Garosi,47c,47a K. Genser,18 H. Gerberich,25 D. Gerdes,35 A. Gessler,27 S. Giagu,52b,52a V. Giakoumopoulou,3 P. Giannetti,47a K. Gibson,48 J. L. Gimmell,50 C.M. Ginsburg,18 N. Giokaris,3 M. Giordani,55b,55a P. Giromini,20 M. Giunta,47a G. Giurgiu,26 V. Glagolev,16 D. Glenzinski,18 M. Gold,38 N. Goldschmidt,19 A. Golossanov,18 G. Gomez,12 G. Gomez-Ceballos,33 M. Goncharov,33 O. Gonza?lez,32 I. Gorelov,38 A. T. Goshaw,17 K. Goulianos,51 A. Gresele,44b,44a S. Grinstein,23 C. Grosso-Pilcher,14 R. C. Group,18 U. Grundler,25 J. Guimaraes da Costa,23 Z. Gunay-Unalan,36 C. Haber,29 K. Hahn,33 S. R. Hahn,18 E. Halkiadakis,53 B.-Y. Han,50 J. Y. Han,50 F. Happacher,20 K. Hara,56 D. Hare,53 M. Hare,57 S. Harper,43 R. F. Harr,59 R.M. Harris,18 M. Hartz,48 K. Hatakeyama,51 C. Hays,43 M. Heck,27 A. Heijboer,46 J. Heinrich,46 C. Henderson,33 M. Herndon,60 J. Heuser,27 S. Hewamanage,5 D. Hidas,17 C. S. Hill,11,d D. Hirschbuehl,27 A. Hocker,18 S. Hou,1 M. Houlden,30 S.-C. Hsu,29 B. T. Huffman,43 R. E. Hughes,40 U. Husemann,61 M. Hussein,36 J. Huston,36 J. Incandela,11 G. Introzzi,47a M. Iori,52b,52a A. Ivanov,8 E. James,18 D. Jang,13 B. Jayatilaka,17 E. J. Jeon,28 M.K. Jha,6a S. Jindariani,18 W. Johnson,8 M. Jones,49 K.K. Joo,28 S. Y. Jun,13 J. E. Jung,28 T. R. Junk,18 T. Kamon,54 D. Kar,19 P. E. Karchin,59 Y. Kato,42,n R. Kephart,18 W. Ketchum,14 J. Keung,46 V. Khotilovich,54 B. Kilminster,18 D.H. Kim,28 H. S. Kim,28 H.W. Kim,28 J. E. Kim,28 M. J. Kim,20 S. B. Kim,28 S. H. Kim,56 Y.K. Kim,14 N. Kimura,56 L. Kirsch,7 S. Klimenko,19 B. Knuteson,33 B. R. Ko,17 K. Kondo,58 D. J. Kong,28 J. Konigsberg,19 A. Korytov,19 A.V. Kotwal,17 M. Kreps,27 J. Kroll,46 D. Krop,14 N. Krumnack,5 M. Kruse,17 V. Krutelyov,11 T. Kubo,56 T. Kuhr,27 N. P. Kulkarni,59 M. Kurata,56 S. Kwang,14 A. T. Laasanen,49 S. Lami,47a S. Lammel,18 M. Lancaster,31 R. L. Lander,8 K. Lannon,40,t A. Lath,53 G. Latino,47c,47a I. Lazzizzera,44b,44a T. LeCompte,2 E. Lee,54 H. S. Lee,14 S.W. Lee,54,v S. Leone,47a J. D. Lewis,18 C.-S. Lin,29 J. Linacre,43 M. Lindgren,18 E. Lipeles,46 A. Lister,8 D. O. Litvintsev,18 C. Liu,48 T. Liu,18 N. S. Lockyer,46 A. Loginov,61 M. Loreti,44b,44a L. Lovas,15 D. Lucchesi,44b,44a C. Luci,52b,52a J. Lueck,27 P. Lujan,29 P. Lukens,18 G. Lungu,51 L. Lyons,43 J. Lys,29 R. Lysak,15 D. MacQueen,34 R. Madrak,18 K. Maeshima,18 K. Makhoul,33 T. Maki,24 P. Maksimovic,26 S. Malde,43 S. Malik,31 G. Manca,30,f A. Manousakis-Katsikakis,3 F. Margaroli,49 C. Marino,27 C. P. Marino,25 A. Martin,61 V. Martin,22,l M. Mart??nez,4 R. Mart??nez-Ballar??n,32 T. Maruyama,56 P. Mastrandrea,52a T. Masubuchi,56 M. Mathis,26 M. E. Mattson,59 P. Mazzanti,6a K. S. McFarland,50 P. McIntyre,54 R. McNulty,30,k A. Mehta,30 P. Mehtala,24 A. Menzione,47a P. Merkel,49 C. Mesropian,51 T. Miao,18 N. Miladinovic,7 R. Miller,36 C. Mills,23 M. Milnik,27 A. Mitra,1 G. Mitselmakher,19 H. Miyake,56 S. Moed,23 N. Moggi,6a M.N. Mondragon,18,o C. S. Moon,28 R. Moore,18 M. J. Morello,47a J. Morlock,27 P. Movilla Fernandez,18 J. Mu?lmensta?dt,29 A. Mukherjee,18 Th. Muller,27 R. Mumford,26 P. Murat,18 M. Mussini,6b,6a J. Nachtman,18,p Y. Nagai,56 PHYSICAL REVIEW D 81, 092002 (2010) 1550-7998=2010=81(9)=092002(18) 092002-1  2010 The American Physical Society A. Nagano,56 J. Naganoma,56 K. Nakamura,56 I. Nakano,41 A. Napier,57 V. Necula,17 J. Nett,60 C. Neu,46,x M. S. Neubauer,25 S. Neubauer,27 J. Nielsen,29,h L. Nodulman,2 M. Norman,10 O. Norniella,25 E. Nurse,31 L. Oakes,43 S. H. Oh,17 Y.D. Oh,28 I. Oksuzian,19 T. Okusawa,42 R. Orava,24 K. Osterberg,24 S. Pagan Griso,44b,44a C. Pagliarone,55a E. Palencia,18 V. Papadimitriou,18 A. Papaikonomou,27 A.A. Paramonov,14 B. Parks,40 S. Pashapour,34 J. Patrick,18 G. Pauletta,55b,55a M. Paulini,13 C. Paus,33 T. Peiffer,27 D. E. Pellett,8 A. Penzo,55a T. J. Phillips,17 G. Piacentino,47a E. Pianori,46 L. Pinera,19 K. Pitts,25 C. Plager,9 L. Pondrom,60 O. Poukhov,16,a N. Pounder,43 F. Prakoshyn,16 A. Pronko,18 J. Proudfoot,2 F. Ptohos,18,j E. Pueschel,13 G. Punzi,47b,47a J. Pursley,60 J. Rademacker,43,d A. Rahaman,48 V. Ramakrishnan,60 N. Ranjan,49 I. Redondo,32 P. Renton,43 M. Renz,27 M. Rescigno,52a S. Richter,27 F. Rimondi,6b,6a L. Ristori,47a A. Robson,22 T. Rodrigo,12 T. Rodriguez,46 E. Rogers,25 S. Rolli,57 R. Roser,18 M. Rossi,55a R. Rossin,11 P. Roy,34 A. Ruiz,12 J. Russ,13 V. Rusu,18 B. Rutherford,18 H. Saarikko,24 A. Safonov,54 W.K. Sakumoto,50 O. Salto?,4 L. Santi,55b,55a S. Sarkar,52b,52a L. Sartori,47a K. Sato,18 A. Savoy-Navarro,45 P. Schlabach,18 A. Schmidt,27 E. E. Schmidt,18 M.A. Schmidt,14 M. P. Schmidt,61,a M. Schmitt,39 T. Schwarz,8 L. Scodellaro,12 A. Scribano,47c,47a F. Scuri,47a A. Sedov,49 S. Seidel,38 Y. Seiya,42 A. Semenov,16 L. Sexton-Kennedy,18 F. Sforza,47b,47a A. Sfyrla,25 S. Z. Shalhout,59 T. Shears,30 P. F. Shepard,48 M. Shimojima,56,s S. Shiraishi,14 M. Shochet,14 Y. Shon,60 I. Shreyber,37 A. Simonenko,16 P. Sinervo,34 A. Sisakyan,16 A. J. Slaughter,18 J. Slaunwhite,40 K. Sliwa,57 J. R. Smith,8 F. D. Snider,18 R. Snihur,34 A. Soha,8 S. Somalwar,53 V. Sorin,36 T. Spreitzer,34 P. Squillacioti,47c,47a M. Stanitzki,61 R. St. Denis,22 B. Stelzer,34 O. Stelzer-Chilton,34 D. Stentz,39 J. Strologas,38 G. L. Strycker,35 J. S. Suh,28 A. Sukhanov,19 I. Suslov,16 T. Suzuki,56 A. Taffard,25,g R. Takashima,41 Y. Takeuchi,56 R. Tanaka,41 M. Tecchio,35 P. K. Teng,1 K. Terashi,51 J. Thom,18,i A. S. Thompson,22 G. A. Thompson,25 E. Thomson,46 P. Tipton,61 P. Ttito-Guzma?n,32 S. Tkaczyk,18 D. Toback,54 S. Tokar,15 K. Tollefson,36 T. Tomura,56 D. Tonelli,18 S. Torre,20 D. Torretta,18 P. Totaro,55b,55a S. Tourneur,45 M. Trovato,47d,47a S.-Y. Tsai,1 Y. Tu,46 N. Turini,47c,47a F. Ukegawa,56 S. Vallecorsa,21 N. van Remortel,24,c A. Varganov,35 E. Vataga,47d,47a F. Va?zquez,19,o G. Velev,18 C. Vellidis,3 M. Vidal,32 R. Vidal,18 I. Vila,12 R. Vilar,12 T. Vine,31 M. Vogel,38 I. Volobouev,29,v G. Volpi,47b,47a P. Wagner,46 R. G. Wagner,2 R. L. Wagner,18 W. Wagner,27,y J. Wagner-Kuhr,27 T. Wakisaka,42 R. Wallny,9 S.M. Wang,1 A. Warburton,34 D. Waters,31 M. Weinberger,54 J. Weinelt,27 W.C. Wester III,18 B. Whitehouse,57 D. Whiteson,46,g A. B. Wicklund,2 E. Wicklund,18 S. Wilbur,14 G. Williams,34 H. H. Williams,46 P. Wilson,18 B. L. Winer,40 P. Wittich,18,i S. Wolbers,18 C. Wolfe,14 T. Wright,35 X. Wu,21 F. Wu?rthwein,10 S. Xie,33 A. Yagil,10 K. Yamamoto,42 J. Yamaoka,17 U. K. Yang,14,r Y. C. Yang,28 W.M. Yao,29 G. P. Yeh,18 K. Yi,18,p J. Yoh,18 K. Yorita,58 T. Yoshida,42,m G. B. Yu,50 I. Yu,28 S. S. Yu,18 J. C. Yun,18 L. Zanello,52b,52a A. Zanetti,55a X. Zhang,25 Y. Zheng,9,e and S. Zucchelli6b,6a (CDF Collaboration) 1Institute of Physics, Academia Sinica, Taipei, Taiwan 11529, Republic of China 2Argonne National Laboratory, Argonne, Illinois 60439, USA 3University of Athens, 157 71 Athens, Greece 4Institut de Fisica d?Altes Energies, Universitat Autonoma de Barcelona, E-08193, Bellaterra (Barcelona), Spain 5Baylor University, Waco, Texas 76798, USA 6aIstituto Nazionale di Fisica Nucleare Bologna, I-40127 Bologna, Italy 6bUniversity of Bologna, I-40127 Bologna, Italy 7Brandeis University, Waltham, Massachusetts 02254, USA 8University of California, Davis, Davis, California 95616, USA 9University of California, Los Angeles, Los Angeles, California 90024, USA 10University of California, San Diego, La Jolla, California 92093, USA 11University of California, Santa Barbara, Santa Barbara, California 93106, USA 12Instituto de Fisica de Cantabria, CSIC?University of Cantabria, 39005 Santander, Spain 13Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA 14Enrico Fermi Institute, University of Chicago, Chicago, Illinois 60637, USA 15Comenius University, 842 48 Bratislava, Slovakia; Institute of Experimental Physics, 040 01 Kosice, Slovakia 16Joint Institute for Nuclear Research, RU-141980 Dubna, Russia 17Duke University, Durham, North Carolina 27708, USA 18Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA 19University of Florida, Gainesville, Florida 32611, USA 20Laboratori Nazionali di Frascati, Istituto Nazionale di Fisica Nucleare, I-00044 Frascati, Italy 21University of Geneva, CH-1211 Geneva 4, Switzerland T. AALTONEN et al. PHYSICAL REVIEW D 81, 092002 (2010) 092002-2 22Glasgow University, Glasgow G12 8QQ, United Kingdom 23Harvard University, Cambridge, Massachusetts 02138, USA 24Division of High Energy Physics, Department of Physics, University of Helsinki and Helsinki Institute of Physics, FIN-00014, Helsinki, Finland 25University of Illinois, Urbana, Illinois 61801, USA 26The Johns Hopkins University, Baltimore, Maryland 21218, USA 27Institut fu?r Experimentelle Kernphysik, Universita?t Karlsruhe, 76128 Karlsruhe, Germany 28Center for High Energy Physics: Kyungpook National University, Daegu 702-701, Korea; Seoul National University, Seoul 151-742, Korea; Sungkyunkwan University, Suwon 440-746, Korea; Korea Institute of Science and Technology Information, Daejeon 305-806, Korea; Chonnam National University, Gwangju 500-757, Korea; Chonbuk National University, Jeonju 561-756, 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, Que?bec, Canada H3A 2T8; Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6; University of Toronto, Toronto, Ontario, Canada M5S 1A7; and TRIUMF, Vancouver, British Columbia, Canada V6T 2A3 35University of Michigan, Ann Arbor, Michigan 48109, USA 36Michigan State University, East Lansing, Michigan 48824, USA 37Institution for Theoretical and Experimental Physics, ITEP, Moscow 117259, Russia 38University of New Mexico, Albuquerque, New Mexico 87131, USA 39Northwestern University, Evanston, Illinois 60208, USA 40The Ohio State University, Columbus, Ohio 43210, USA 41Okayama University, Okayama 700-8530, Japan 42Osaka City University, Osaka 588, Japan 43University of Oxford, Oxford OX1 3RH, United Kingdom 44aIstituto Nazionale di Fisica Nucleare, Sezione di Padova-Trento, I-35131 Padova, Italy 44bUniversity of Padova, I-35131 Padova, Italy 45LPNHE, Universite Pierre et Marie Curie/IN2P3-CNRS, UMR7585, Paris, F-75252 France 46University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA 47aIstituto Nazionale di Fisica Nucleare Pisa, I-56127 Pisa, Italy aDeceased. bVisitor from University of Massachusetts Amherst, Amherst, MA 01003, USA. cVisitor from Universiteit Antwerpen, B-2610 Antwerp, Belgium. dVisitor from University of Bristol, Bristol BS8 1TL, United Kingdom. eVisitor from Chinese Academy of Sciences, Beijing 100864, China. fVisitor from Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari, 09042 Monserrato (Cagliari), Italy. gVisitor from University of California Irvine, Irvine, CA 92697, USA. hVisitor from University of California Santa Cruz, Santa Cruz, CA 95064, USA. iVisitor from Cornell University, Ithaca, NY 14853, USA. jVisitor from University of Cyprus, Nicosia CY-1678, Cyprus. kVisitor from University College Dublin, Dublin 4, Ireland. lVisitor from University of Edinburgh, Edinburgh EH9 3JZ, United Kingdom. mVisitor from University of Fukui, Fukui City, Fukui Prefecture, Japan 910-0017. zOn leave from J. Stefan Institute, Ljubljana, Slovenia. yVisitor from Bergische Universita?t Wuppertal, 42097 Wuppertal, Germany. xVisitor from University of Virginia, Charlottesville, VA 22904, USA. wVisitor from IFIC (CSIC?Universitat de Valencia), 46071 Valencia, Spain. vVisitor from Texas Tech University, Lubbock, TX 79609, USA. uVisitor from University de Oviedo, E-33007 Oviedo, Spain. tVisitor from University of Notre Dame, Notre Dame, IN 46556, USA. sVisitor from Nagasaki Institute of Applied Science, Nagasaki, Japan. rVisitor from University of Manchester, Manchester M13 9PL, England. qVisitor from Queen Mary, University of London, London, E1 4NS, England. pVisitor from University of Iowa, Iowa City, IA 52242, USA. oVisitor from Universidad Iberoamericana, Mexico D.F., Mexico. nVisitor from Kinki University, Higashi-Osaka City, Japan 577-8502. MEASUREMENT OF THE tt PRODUCTION CROSS . . . PHYSICAL REVIEW D 81, 092002 (2010) 092002-3 47bUniversity of Pisa, I-56127 Pisa, Italy 47cUniversity of Siena, I-56127 Pisa, Italy 47dScuola Normale Superiore, I-56127 Pisa, Italy 48University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA 49Purdue University, West Lafayette, Indiana 47907, USA 50University of Rochester, Rochester, New York 14627, USA 51The Rockefeller University, New York, New York 10021, USA 52aIstituto Nazionale di Fisica Nucleare, Sezione di Roma 1, I-00185 Roma, Italy 52bSapienza Universita` di Roma, I-00185 Roma, Italy 53Rutgers University, Piscataway, New Jersey 08855, USA 54Texas A&M University, College Station, Texas 77843, USA 55aIstituto Nazionale di Fisica Nucleare Trieste/Udine, I-34100 Trieste, Italy 55bUniversity of Trieste/Udine, I-33100 Udine, Italy 56University of Tsukuba, Tsukuba, Ibaraki 305, Japan 57Tufts University, Medford, Massachusetts 02155, USA 58Waseda University, Tokyo 169, Japan 59Wayne State University, Detroit, Michigan 48201, USA 60University of Wisconsin, Madison, Wisconsin 53706, USA 61Yale University, New Haven, Connecticut 06520, USA (Received 22 February 2010; published 11 May 2010) We present a measurement of the top-quark pair-production cross section in p p collisions at ffiffiffi s p ? 1:96 TeV using a data sample corresponding to 1:7 fb1 of integrated luminosity collected with the Collider Detector at Fermilab. We reconstruct tt events in the lepton? jets channel, consisting of e? jets and ? jets final states. The dominant background is the production of W bosons in association with multiple jets. To suppress this background, we identify electrons from the semileptonic decay of heavy-flavor jets (??soft electron tags??). From a sample of 2196 candidate events, we obtain 120 tagged events with a background expectation of 51 3 events, corresponding to a cross section of tt ? 7:8 2:4?stat?  1:6?syst?  0:5?lumi? pb. We assume a top-quark mass of 175 GeV=c2. This is the first measurement of the tt cross section with soft electron tags in run II of the Tevatron. DOI: 10.1103/PhysRevD.81.092002 PACS numbers: 12.38.Qk, 13.20.He, 13.85.Lg, 14.65.Ha I. INTRODUCTION The top quark is the most massive fundamental particle observed to date, and has been studied by the CDF and D0 collaborations since its discovery in 1995 [1]. The tt pro- duction cross section has been measured in each of the three canonical final states: q q0bq q0 b [2], q q0b?  b [3?5], and ?b?  b [6] (? ? e, , and q ? u, d, c, s). In these measurements, different combinations of b-quark identifi- cation (??tagging??) and kinematic information [3] have been used to suppress backgrounds. Tagging of b quarks has been accomplished by identifying the long lifetime of the hadron with secondary vertex reconstruction or with displaced tracks [4] or through soft muons from semilep- tonic decay [5]. Along with measurements of the top-quark mass [7] and many other properties of the top quark, a consistent picture of the top quark as the third generation standard model (SM) isospin partner of the bottom quark emerges. The Fermilab Tevatron produces top quarks, typically in pairs, by colliding p p at ffiffiffi s p ? 1:96 TeV. The tt produc- tion cross section calculated at next-to-leading order is 6:7 0:8 pb [8] assuming mt ? 175 GeV=c2, where the uncertainty is dominated by the choice of renormalization and factorization scales. At the Tevatron, approximately 85% of tt production is via quark-antiquark annihilation and 15% is via gluon-gluon fusion. The measurement of the production cross section is important first as a test of perturbative QCD, but also as a platform from which to study other top-quark properties. Moreover, measuring the tt cross section in its various final states is an important consistency test of the SM and might highlight contribu- tions to a particular decay channel from new physics. In this paper, we present a measurement of the tt pro- duction cross section in the lepton plus 3 jets final state. The dominant background in this channel is the production of a W boson associated with several jets. To suppress this background, we use a soft electron tagger (SLTe) to iden- tify the semileptonic decay of heavy flavor (HF). Heavy flavor refers to the product of the fragmentation of a bottom or charm quark. Soft electron tagging is a challenging method of identi- fying b jets because the semileptonic branching fraction (BF) is approximately 20%?BF?b! eX? and BF?b! c! eX? each contribute approximately 10%?and be- cause electron identification is complicated by the pres- ence of a surrounding jet. The algorithm is able to distinguish electromagnetic showers from hadronic show- T. AALTONEN et al. PHYSICAL REVIEW D 81, 092002 (2010) 092002-4 ers by using a shower-maximum detector embedded in the electromagnetic calorimeter. This detector has a high enough resolution that it can determine the transverse shape and position of electron showers and yet be unaf- fected by nearby activity. Additionally, ! e?e conver- sions due to material interactions provide a significant background, which we suppress using a combination of geometric and kinematic requirements. Nevertheless, the soft electron technique is interesting because it is comple- mentary to other b-tagging techniques and because it is a useful technique for other analyses. This is the first measurement of the tt cross section with soft electron tags in run II of the Tevatron. A previous measurement at ffiffiffi s p ? 1:8 TeV combined secondary ver- tex, soft muon, and soft electron tagging [9]. We organize this paper as follows: Sec. II describes aspects of the CDF detector salient to this analysis. Section III describes the implementation of the SLTe. We discuss the SLTe tagging efficiency in tt events in Sec. IV. Section V describes the calculation of the background to tagged electrons in HF jets, including conversion electrons and hadrons. In Sec. VI, we tune the SLTe tagger in a b b control sample. This ensures the tagger?s validity in high- momentum b jets, such as those found in tt events. Section VII reports the cross section measurement, includ- ing the event selection and signal and background estima- tion. Finally, in Sec. VIII we present our results and conclusions. II. THE CDF DETECTOR CDF II is a multipurpose, azimuthally and forward- backward symmetric detector designed to study p p colli- sions at the Tevatron. An illustration of the detector is shown in Fig. 1. We use a cylindrical coordinate system where z points along the proton direction,  is the azimu- thal angle about the beam axis, and  is the polar angle to the proton beam direction. We define the pseudorapidity    ln tan?=2?. The tracking system consists of silicon microstrip de- tectors and an open-cell drift chamber immersed in a 1.4 T solenoidal magnetic field. The silicon microstrip detectors provide precise charged particle tracking in the radial range from 1.5?28 cm. The silicon detectors are divided into three different subcomponents, comprised of eight total layers. Layer00 (L00) [10] is a single-sided silicon detector mounted directly on the beam pipe. The silicon vertex detector (SVXII) [11] consists of five double-sided sensors with radial range up to 10.6 cm. The intermediate silicon layer (ISL) [12] is composed of two layers of FIG. 1. Illustration of the CDF II detector. MEASUREMENT OF THE tt PRODUCTION CROSS . . . PHYSICAL REVIEW D 81, 092002 (2010) 092002-5 double-sided silicon, extending coverage up to jj< 2:0. The drift chamber, referred to as the central outer tracker (COT) [13], consists of 96 layers of sense wires grouped in eight alternating superlayers of axial and stereo wires, covering a radial range from 40 to 140 cm. The recon- structed trajectories of COT tracks are extrapolated into the silicon detectors, and the track is refit using the additional hits in the silicon detectors. In combination, the COT and silicon detectors provide excellent tracking up to jj  1:1. The transverse momentum (pT) resolution, ?pT?=pT , is approximately 0:07%pT ?GeV=c1 when hits from the SVXII and ISL are included. Beyond the solenoid lie the electromagnetic and had- ronic calorimeters, with coverage up to jj  3:6. The calorimeters have a projective geometry with a segmenta- tion of   0:1 and   15 in the central (jj  1:1) region. The central electromagnetic calorimeter (CEM) [14] consists of >18 radiation lengths (X0) of lead- scintillator sandwich and contains wire and strip chambers embedded at the expected shower maximum ( 6X0). The wire and strip chambers are collectively referred to as the central shower-maximum (CES) chambers and provide measurements of the transverse electromagnetic shower shape along the r and z di- rections with a resolution of 1 and 2 mm, respectively. The central hadronic calorimeter (CHA) [15] consists of 4:7 interaction lengths of alternating lead-scintillator layers at normal incidence. Measured in units of GeV, the CEM has an energy resolution ?E?=E ? 13:5%= ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiE sin??p 2% and the CHA has an energy resolution ?E?=E ? 50%= ffiffiffiffiEp . Muon chambers [16] consist of layers of drift tubes surrounding the calorimeter. The central muon detector (CMU) is cylindrical and covers a pseudorapidity range jj< 0:63. The central muon upgrade (CMP) is a box- shaped set of drift chambers located beyond the CMU and separated by more than three interaction lengths of steel. Muons which produce hits in both the CMU and CMP are called CMUP. The central muon extension (CMX) extends the muon coverage up to jj  1. Gaseous Cherenkov luminosity counters (CLC) [17] provide the luminosity measurement with a 6% relative uncertainty. CDF uses a three-level trigger system to select events to be recorded to tape. The first two levels perform a limited set of reconstruction with dedicated hardware, and the third level is a software trigger performing speed-optimized event reconstruction algorithms. The triggers used in this analysis include electron, muon, and jet triggers at differ- ent transverse energy thresholds. The electron triggers require the coincidence of a track with an electromagnetic cluster in the central calorimeter. The muon triggers re- quire a track that points to hits in the muon chambers. The jet triggers require calorimeter clusters with uncorrected ET above a specified threshold. III. SOFT ELECTRON TAGGING The SLTe algorithm uses the COT and silicon trackers, central calorimeter and, in particular, the central shower- maximum chambers to identify electrons embedded in jets from semileptonic decays of HF quarks. The tagging algo- rithm is ??track based???as opposed to ??jet based???in that we consider every track in the event that meets certain criteria as a candidate for tagging. Such tracks are required to be well measured by the COT and to extrapolate to the CES. This requirement forces the track to have jj less than 1.2. We require that the track pT is greater than 2 GeV=c. We consider only tracks that originate close to the primary vertex: jd0j< 0:3 cm, jz0j< 60 cm, and jz0  zvtxj< 5 cm, where d0 is the impact parameter, which is the distance of closest approach in the transverse plane, with respect to the beam line. The z position of the track at closest approach to the beam line is z0, and zvtx is the reconstructed z position of the primary vertex. Tracks must also pass a jet-matching requirement, which is that they are within R  ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ?2p  0:4 from the axis of a jet with transverse energy ET greater than 20 GeV. Jets are clustered with a fixed-cone algorithm with a cone of size R  0:4. Jet energies are corrected for detector response, multiple interactions, and uninstrumented regions of the detector [18]. Finally, tracks must also pass a conversion filter described in Sec. VA. Although we have not explic- itly required tracks to have silicon hits, the conversion filter insists that tracks with a high number of ??missing?? silicon hits must be discarded. We consider tracks which meet all of the above criteria as SLTe candidates. Candidate tracks are passed through the SLTe algorithm which uses information from both the calorimeter and CES detectors. The algorithm is designed to identify low-pT electrons [19] embedded in high-ET jets while still main- taining a high identification efficiency for high-pT elec- trons. This is particularly important for tagging tt events, although the SLTe algorithm is not specific to this final state. Figure 2 shows the pT shape of candidate SLTe electrons in the CDF II detector from a bottom quark, charm quark, and photon conversions in PYTHIA [20] tt Monte Carlo (MC) simulated events. Even in tt events, the electron spectrum from b jets peaks at low pT but extends more than a decade in scale. Electrons from charm decay in tt events are principally due to cascade decays, but some direct charm production occurs through the hadronic decay of the W boson. The SLTe candidate tracks are extrapolated to the front face of the calorimeter to seed an electromagnetic cluster in the CEM. The two calorimeter towers adjacent in  space closest to the extrapolated point are used in the cluster. A candidate SLTe must have an electromagnetic shower that satisfies 0:6< EEM=p < 2:5 and EHad=EEM < 0:2, where EEM and EHad are the total electromagnetic and hadronic energies in the cluster, respectively, and p is the momentum of the electron track. The EEM=p requirement T. AALTONEN et al. PHYSICAL REVIEW D 81, 092002 (2010) 092002-6 selects electromagnetic showers which have approxi- mately the same energy as the track (as expected from electrons), while the EHad=EEM requirement suppresses late-developing (typically hadronic) showers. These re- quirements were tuned in simulated tt events and are looser than for typical high-ET electrons because the presence of photons and hadrons from the nearby jet distorts the energy deposition. Figure 3 shows the calorimeter variables for candidate SLTe tracks in PYTHIA tt simulation. Next, the SLTe algorithm uses the track extrapolation to seed a wire cluster and strip cluster in the CES. We limit the number of strips and wires in the clusters to seven each in order to minimize the effects of the surrounding environ- ment. At least two wires (strips) with energy above a 80 (120) MeV threshold must be present, or the track is not tagged. This requirement suppresses low-pT hadrons that have a late-developing shower in the CEM. Two discriminant quantities determined from the CES are used to distinguish electrons from hadrons. One is a 2 comparison between the transverse shower profile of the SLTe candidate and the profile measured with test-beam electrons. The other is the distance , measured in cm, between the extrapolated track and the position of the cluster energy centroid. Each type of discriminant is de- termined for the wire and strip chambers separately. We construct a likelihood-ratio discriminant by using the 2 and  distributions from pure samples of electrons and hadrons as templates. The electron sample is selected by triggering on an ET > 8 GeV electron from a photon conversion (! e?e) and using the partner electron. For this sample, the conversion filter requirement is inverted, and the jet-matching requirement is ignored. To prevent a bias from overlapping electromagnetic showers, photon conversions in which both electrons share a tower are not considered. The hadron sample is selected through events that pass a 50 GeV jet trigger and identifying generic tracks in jets away from the trigger jet. In both samples, the purity is over 98%. The distributions for the CES wire chamber and strip chamber discriminants from each sample are shown in Fig. 4. The relative difference in shapes between the wire and strip distributions is due to the different energy thresh- olds used, and the slightly different resolution due to the differing technology. The likelihood ratio is formed by binning the electron and hadron templates in a normalized four-dimensional histogram to preserve the correlations between the four variables, 2wire, 2strip,wire, andstrip, creating probability distribution functions for both signal and background. We use them to derive a likelihood ratio according to the [GeV/c]T Track peSLT 5 10 15 20 25 30 35 40 45 50 Normalized Unit s 0 0.1 0.2 0.3 0.4 0.5 Bottom Electron Charm Electron Conversion Electron Eventst Distribution of Electrons in tTp FIG. 2. Transverse momentum distribution of candidate SLTe tracks in jets in PYTHIA ttMC simulated events. Distributions for electrons from a bottom quark, charm quark, and photon con- versions are normalized to unity to emphasize the relative difference in the shapes. /pEME 0 1 2 3 4 5 6 7 8 9 10 Normalized Unit s 0 0.02 0.04 0.06 0.08 0.1 0.12 Eventst tracks in teCandidate SLT (a) Bottom Electrons Charm Electrons EM/EHadE 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Normalized Unit s 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 Eventst tracks in teCandidate SLT (b) Bottom Electrons Charm Electrons FIG. 3. (a) EEM=p and (b) EHad=EEM for candidate SLTe tracks from HF decay in PYTHIA ttMC simulated events. Selection criteria for the distributions are shown with arrows. MEASUREMENT OF THE tt PRODUCTION CROSS . . . PHYSICAL REVIEW D 81, 092002 (2010) 092002-7 formula L  Si Si ? Bi ; (1) where Si and Bi are the values of the probability distribu- tion functions in the ith bin of signal and background templates, respectively. We tag a candidate track if L> 0:55. Two other operating points (> 0:65 and>0:75) were also studied for this analysis, but the former point was found to give the best expected combined statistical and systematic uncertainty on the tt cross section. Table I summarizes the requirements for a candidate SLTe track to be tagged. # of Wires Above Threshold 1 2 3 4 5 6 7 N or m al iz ed U ni ts 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 (a) Electrons Hadrons wire 2? 0 5 10 15 20 25 30 N o rm a liz e d Un i ts 0 0.05 0.1 0.15 0.2 0.25 (b) O ve rfl ow Electrons Hadrons [cm]wire? -6 -4 -2 0 2 4 6 N ormalized Unit s 0 0.05 0.1 0.15 0.2 0.25 0.3 (c) Electrons Hadrons # of Strips Above Threshold 1 2 3 4 5 6 7 Normalized Unit s 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 (d) Electrons Hadrons strip 2? 0 5 10 15 20 25 30 N o rm a liz e d Un i ts 0 0.05 0.1 0.15 0.2 0.25 (e) O ve rfl ow Electrons Hadrons [cm] strip? -6 -4 -2 0 2 4 6 Normalized Unit s 0 0.05 0.1 0.15 0.2 0.25 0.3 (f) Electrons Hadrons FIG. 4. (a) Number of wires above threshold, (b) 2wire, (c) wire, (d) number of strips above threshold, (e) 2strip, and (f) strip for SLTe tracks from a sample of conversion electrons and from a sample of hadrons in jet-triggered events. The last bin of the 2 distribution and the first and last bins of the  distribution are the integral of the underflow/overflow. Arrows indicate the location of the wire and strip requirement for tagging. CES variables are combined to form a likelihood-ratio discriminant. TABLE I. Summary of requirements for tagging a candidate SLTe track. 0:6 0:55 Likelihood Requirement 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Tagging Efficienc y 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Electrons Hadrons FIG. 5. Tagging efficiency for electrons from photon conver- sions (where each leg occupies different calorimeter towers) and hadrons in events triggered on a 50 GeV jet as a function of the likelihood-ratio requirement. T. AALTONEN et al. PHYSICAL REVIEW D 81, 092002 (2010) 092002-8 We measure the tagging efficiency?that is, the number of tracks that are tagged divided by the number of all candidate tracks?with the combination of calorimeter, wire/strip, and L requirements in various samples. Figure 5 shows this tagging efficiency for the electron sample ( 60% at L> 0:55) and the hadron sample ( 1:1% atL> 0:55) as a function of the likelihood-ratio requirement. Note that because the hadron sample has not been corrected for the small contamination by electrons, the hadron tagging efficiency should only be considered an upper bound. This correction is discussed later in Sec. VB. Also note that value of the likelihood ratio does not extend to 1.0. This is an artifact of the four variables chosen for the likelihood. Hadrons occupy the entire phase space of pos- sible values for 2wire=strip and wire=strip, so that the back- ground probability distribution function is never zero. IV. SLTe TAGGING EFFICIENCY IN JETS An important feature of the SLTe algorithm is the tag- ging efficiency dependence on the environment. In the previous section we described the per-track tagging effi- ciency for a sample of isolated conversion electrons where each leg is incident on a different calorimeter tower. However, the tagging efficiency for electrons from semi- leptonic b decay with the same kinematic characteristics as the conversion electrons is markedly lower. This is due to the nearby jet which distorts the electromagnetic shower detected in the calorimeter. In general, the calorimeter variables EEM=p and EHad=EEM are strongly affected by the jet, whereas the CES variables?that is, the 2 and  variables as well as the number of wires and strips in the CES cluster?have a much weaker dependence. For the SLTe algorithm, we introduce the isolation variable ISLT defined as the scalar sum of the pT of tracks which point to the calorimeter cluster divided by the can- didate track pT: clstpT=pT . This variable is useful at quantifying the degree to which the local environment should affect the electron?s electromagnetic shower, and hence the identification variables. An isolated SLTe track has ISLT identically equal to 1.0, whereas for a nonisolated track, ISLT > 1:0. In order to measure the SLTe tagging efficiency of soft electrons in jets, we rely on a combination of MC simula- tion and data-driven techniques. We study the calorimeter and the CES discriminants, which both enter the SLTe algorithm, separately. Although the calorimeter variables have a strong dependence on the local environment, they are well modeled in the MC simulation. However, the CES variables, on the whole, are poorly modeled in the simula- tion due to the presence of early overlapping hadronic showers. We study the modeling of the SLTe calorimeter-based discriminants in a sample of conversion electrons recon- structed in jets. This sample is constructed by identifying an electron and its conversion partner while both are close to a jet (R  0:4). We select such conversions in data triggered on a 50 GeV jet and a kinematically comparable dijet MC simulation sample. We use the missing silicon layer variable, described in Sec. VA, to enhance the con- version electron content in the sample. This is done by requiring that the track associated with the conversion (GeV/c)TTrack p 2 4 6 8 10 12 14 16 18 20 Efficienc y 0.5 0.6 0.7 0.8 0.9 1 1.1 Isolated Conversion Electrons Jet Data Jet MC (a) (GeV/c)TTrack p 2 4 6 8 10 12 14 16 18 20 Efficienc y 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 NonIsolated Conversion Electrons Jet Data Jet MC (b) FIG. 6. Efficiency of the calorimeter requirements on an untagged conversion electron as a function of the track pT , for both isolated (a) and nonisolated (b) tracks. Error bars reflect statistical uncertainties from both data and MC. We use the overall agreement to derive a 2.5% relative systematic uncertainty on the calorimeter requirements of the SLTe tagger. MEASUREMENT OF THE tt PRODUCTION CROSS . . . PHYSICAL REVIEW D 81, 092002 (2010) 092002-9 partner is expected to have, but does not have, hits in at least three silicon layers. The conversion partner is used as a probe to compare the efficiency of the combined calo- rimeter requirements in both data and simulated samples as a function of pT and ISLT. We see very good agreement in the general trend between both samples, as shown in Fig. 6, from which we derive a 2.5% relative systematic uncer- tainty (integrating over all bins) to cover the difference between data and simulation. The comparison between kinematically and environmentally similar samples is im- portant to validate the behavior of the simulation modeling. To account for the mismodeling of the CES-based dis- criminants, we measure the tagging efficiency of candidate SLTe tracks directly in data and apply it to candidate SLTe tracks in the simulation that have already passed the calorimeter-based requirements. The efficiency is parame- trized as a three-dimensional matrix in pT , , and ISLT to account for the correlations between the three variables. This matrix is constructed out of the pure conversion electron sample used to create the likelihood-ratio tem- plates. The validity of the tag matrix is then verified in a sample of electrons from Z boson decays and in a b b sample, as described in Sec. VI. A 3% relative systematic uncertainty?derived from the agreement within the con- version sample and with the Z! e?e sample?is applied to the tag-matrix prediction. Applying the matrix as a weight on each candidate SLTe track identified in the simulated events, we find that the tagging efficiency for electrons from HF jets in tt events is approximately 40% per electron track (see Sec. VI). This is calculated by identifying candidate SLTe tracks in tt events matched to electrons from HF jets in the simulation. For those electrons which pass the calorimeter requirements, the tag matrix determines the expected tagging probability. V. SLTe TAGGING BACKGROUNDS The two principal backgrounds to SLTe tagging are real electrons from photon conversions and misidentified elec- trons from charged hadrons (e.g. , K, p). Although the tagging probability is very low for hadrons, the high multi- plicity of such tracks makes their contribution non- negligible. Conversion electrons are much more abundant than electrons from HF jets. In tt events, 3 times as many candidate tracks are due to conversion electrons than to electrons from semileptonic decay of HF. Their removal is essential to effective b-tagging. Additionally, there is a small contribution from Dalitz decays of 0, , and J=c . In this section we discuss the estimation of the conversion electron and hadronic backgrounds. A. Conversions The primary procedure for conversion electron rejection relies on identifying the partner leg. We identify an SLTe tagged track as a conversion if, when combined with another nearby track in the event, the pair has the geomet- ric characteristics of a photon conversion. In particular, the cot?? between the tracks as well as the distance between the tracks when they are parallel in the r plane must be small. However, for low-pT conversion electrons in jets, this requirement fails to identify the partner leg more than 40% of the time. The primary reason for this is that the track reconstruction algorithms begin to fail at very low pT 500 MeV=c. The asymmetric energy sharing be- tween conversion legs exacerbates this effect. To recover conversion electrons when the partner leg is not found, we use the fact that conversions are produced through interactions in the material. We extrapolate the candidate track?s helix through the silicon detectors and identify silicon detector channels where no hit is found. If a track is missing hits on each side of more than three double-sided silicon layers, then it is identified as a con- version (at most six missing layers are possible [21]). Figure 7 shows the reconstructed radius of conversion, Rconv, versus the number of missing silicon layers for conversion electrons with both legs tagged by the SLTe in an inclusive sample of ET > 8 GeV electrons. Although high Rconv values are suppressed because of the impact parameter requirement, there is a clear correlation between missing silicon layers and the Rconv. For SLTe candidates, we combine the standard partner-track-finding algorithm with the missing silicon layer algorithm so that, if a tag fails either, we reject it as a conversion electron. We measure the conversion ID efficiency in data by decomposing the algorithm into a partner-track-finding component and a missing silicon layer component. We use the missing silicon layer templates to measure the partner-track-finding component efficiency, and we use a sample of conversions with both legs SLTe tagged to (cm)convR 0 5 10 15 20 25 30 35 40 45 50 # Missing SI Layer s 0 1 2 3 4 5 6 Conversions from Inclusive Electron Data set FIG. 7. Number of missing silicon layers versus the recon- structed radius of conversion for conversion electrons found in an inclusive (ET > 8 GeV) electron sample. Tracks tagged by the SLTe algorithm are rejected as conversions if they have more than three missing silicon layers. T. AALTONEN et al. PHYSICAL REVIEW D 81, 092002 (2010) 092002-10 measure the missing silicon layer component efficiency. We combine the efficiencies, accounting for their correlation. We use an in situ process of building templates for the missing silicon layer variable for conversions and prompt tracks directly within the sample of interest to fit for the total conversion content before and after rejection. The in situ nature of the template construction is important because conversion identification depends strongly on kinematics and geometry that can vary across different samples. The conversion template is constructed from conversions where both legs are tagged by the SLTe, and the prompt track template is constructed from tracks where the SLTe requirements have been inverted, resulting in a nearly 100% pure hadronic sample. A fit for the conversion component of SLTe tags in events triggered on a 20 GeV jet is shown in Fig. 8. In the fit, only those electrons with hits in six expected silicon layers are considered. Those tracks with fewer than six expected layers are used as a consistency check, and a systematic uncertainty is assigned to the geometric bias incurred from this requirement. The dearth of tracks with four or five missing layers is an artifact of the CDF track reconstruction algorithm, which requires that at least three silicon hits must be added to any track or none will be added. The goodness of fit is limited by systematic biases in the template construction which contribute the dominant systematic uncertainties to the efficiency measurement. Such biases include correlations between track finding and missing silicon layers, modeling of prompt electrons (including HF decay) by prompt hadrons, geometric de- pendencies, and sample contamination. We find that the conversion identification efficiency is overestimated in MC simulation relative to data. We char- acterize the difference by a multiplicative scale factor (SF), defined as the ratio of efficiencies measured in data and simulation. Because the conversion identification effi- ciency depends strongly on the underlying photon energy spectrum, it is important for the SF measurement to com- pare energetically similar samples. Therefore, we measure the SF in events triggered by a jet with ET > 20, 50, 70, and 100 GeV and compare to MC simulated dijet events which pass the same requirements. We measure a conver- sion efficiency SF of 0:93 0:01?stat?  0:02?syst?. The dominant uncertainties are systematic effects related to the accuracy of the template models. We find that the SF behaves consistently as a constant correction across a variety of different event and track variables in multiple data sets. Figure 9 shows the SF as a function of track pT in a sample of events triggered by an ET > 20 GeV jet. The gray band shows the value of the SF with statistical and systematic uncertainties for combined SF across jet 20, 50, 70, and 100 data sets. We also measure a conversion ??misidentification effi- ciency???defined as the efficiency to misidentify a non- conversion track as a conversion?multiplicative SF of 1:0 0:3 between data and simulation. This is done by measuring the efficiency to identify prompt tracks as con- versions. The large systematic uncertainty accounts for the variation found across different kinematic variables, jet triggers, and particle types (such as the difference between a HF electron and a pion from Ks decay). In tt events, the complete algorithm is approximately 70% efficient at re- jecting candidate SLTe tracks that are conversions. Only 7% of nonconversions are misidentified as conversions. Since the misidentification efficiency is an order of mag- nitude lower than the efficiency, the total contribution of systematic uncertainties from each is comparable. Missing Silicon Layers 0 1 2 3 4 5 6 Tags/Missing Laye r 0 2000 4000 6000 8000 10 000 12 000 14 000 16 000 18 000 20 000 Tags/Missing Laye r Jet 20 Data set Electron Tags Conversions Prompts FIG. 8. Fit for the conversion and prompt component of SLTe tags before conversion removal in events triggered on an ET > 20 GeV jet. The goodness of fit is limited by systematic biases in the template construction and is accounted for in the final SF measurement. (GeV/c)TTrack p 2 3 4 5 6 7 8 9 10 11 12 Efficiency/Scale Facto r 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 Efficiency: Jet20 Data Efficiency: Jet20 MC Efficiency SF FIG. 9 (color online). Conversion identification scale factor measurement in events triggered by an ET > 20 GeV jet. Shown also is the efficiency measured in data and the efficiency measured in MC simulation. The consistency across pT , among other variables, demonstrates the validity of the SF approach. MEASUREMENT OF THE tt PRODUCTION CROSS . . . PHYSICAL REVIEW D 81, 092002 (2010) 092002-11 B. Hadrons We measure the tagging efficiency of hadrons in MC by defining a three-dimensional fake matrix out of tracks in jet-triggered events. The matrix parametrizes the probabil- ity that the CES discriminants (L plus the number of wires and strips) can tag a hadron. We remove jets where a large fraction of energy is deposited by a single track, in order to reduce the contamination of hard electrons that are also reconstructed as jets. We find that the use of the track pT , , and ISLT is sufficient to describe the dependence of the tagging efficiency on other variables as well. This is dem- onstrated in Fig. 10, which shows the measured and pre- dicted tags in events triggered on a jet with ET > 100 GeV as a function of the ET of the jet closest in R to the SLTe track. We also cross-check the fake-matrix prediction in a distinct sample of tracks in jets triggered on a high-ET photon. We find that the agreement is good within 5%. Before tagging, the tracks in the jet samples are almost purely hadronic; however, after tagging, we must correct for the electron contamination when we estimate the total efficiency to tag a hadronic track. Three classes of elec- trons are present in the sample: conversion electrons, HF electrons, and other sources (primarily Dalitz decay of 0). The conversion electron contamination is estimated by measuring the efficiency and misidentification efficiency of the conversion filter in the jet samples. Using this information in combination with the number of tracks before and after conversion removal determines the re- maining conversion content. The HF electron contamina- tion is estimated using correlations between the SLTe tags and b tags from a secondary vertex algorithm, SECVTX [22]. We use SECVTX to enhance the HF content of the jet sample. Using MC simulation to estimate the expected size of this enhancement, we can extrapolate back to the original, pre-SECVTX tag HF component. The remaining contribution of electrons from other sources is small and estimated with the MC simulation. We find that 35% 3% of the tags in the jet-triggered sample are electrons. This estimate is verified by measur- ing the SLTe tagging efficiency for charged pions from Ks decay. By subtracting the electron contamination from the fake-matrix prediction, we find that, on average, 0.5% of hadronic tracks in tt events produce a fake SLTe tag. VI. TUNING IN b b SAMPLE As a validation of the measured efficiency of the tagger, we measure the jet tagging efficiency in a highly enriched sample of b b events. Events are selected through an 8 GeV electron or muon trigger, and we require that both the jet close to the lepton (R  0:4) and the recoiling (away) jet have a SECVTX tag. We measure the per-jet efficiency to find at least one SLTe tag in the away jet. This efficiency is measured to be 4:4 0:1?stat??%? in simulation and 4:3 0:1?stat??%? in data. The efficiency is calculated in simulation by taking all of the candidate tracks in the jet that pass the calorimeter requirements and using either the tag matrix for electrons or the fake matrix for hadrons to determine a tagging probability. If a track is identified as a conversion, then the tagging probability is rescaled according to the conver- sion efficiency or misidentification efficiency SF. We tune the tag matrix with a multiplicative factor of 0:98 0:03 to get the simulation to agree with data, where the systematic uncertainty is assigned to cover a jet-ET dependence in the difference. The difference in the prediction and measure- (GeV)TJet E 20 40 60 80 100 120 140 160 180 200 Tag s 0 5000 10 000 15 000 20 000 25 000 30 000 35 000 40 000 Fractional Differenc e -0.4 -0.2 0 0.2 0.4Fake-Matrix Prediction Jet 100 Data (Pred-Meas)/Pred 5% Systematic? FIG. 10 (color online). Predicted and measured tags in events triggered on an ET > 100 GeV jet as a function of the ET of the jet closest to the candidate SLTe track. On the right axis is the relative fractional difference between the measurement and prediction. (GeV/c)T Track peSLT 2 4 6 8 10 12 14 16 18 20 Tags/1.0 GeV/ c 0 200 400 600 800 1000 1200 Tags/1.0 GeV/ c Overflo w SLT Tags HF Electrons Conversion Electrons Fake Electrons 8 GeV Lepton Data set EventseSLT FIG. 11 (color online). Predicted and measured tags as a function of the SLTe track pT in a b b enhanced sample con- structed from inclusive electron and muon triggered events. Shown are contributions from fake tags, conversion electron tags, and HF electron tags. Simulation and data statistical uncertainties are combined in quadrature and shown together on the data points only. T. AALTONEN et al. PHYSICAL REVIEW D 81, 092002 (2010) 092002-12 ment is due to isolation effects of the jet environment not already accounted for by the ISLT parametrization, specifi- cally the presence of neutral hadrons. Figure 11 shows the predicted and measured tags in the combined 8 GeV elec- tron and muon trigger samples as a function of the pT of the SLTe tag after the tuning. Statistical uncertainties from the data and simulation are added in quadrature and shown on the data points. By combining the tag matrix, fake matrix, conversion identification and misidentification efficiency SFs, and the correction for the jet environment, we estimate the tagging efficiency of data from simulation. Figure 12 shows the efficiency to tag a HF electron and a hadron in simulated tt events as a function of the track pT and the jet ET . While the tagging efficiency for electrons is steady as a function of the track pT , it decreases as a function of the jet ET because of the decreasing isolation at high ET . VII. CROSS-SECTION MEASUREMENT The tt production cross section is determined with the equation  ? N  B ttAtt RLdt ; (2) where N is the number of tagged events, B is the expected background, tt and Att are the signal efficiency and acceptance, respectively, and RLdt is the integrated lumi- nosity. In this section, we describe the measurement of each of these quantities. A. Event selection and expectation We select tt events in the lepton? jets decay channel through an inclusive lepton trigger which requires an elec- tron (muon) with ET > 18 GeV (pT > 18 GeV=c). After triggering, we further require that events contain an iso- lated electron (muon) with ET > 20 GeV (pT > 20 GeV=c) in the central region (jj< 1:1). We refer to this lepton as the primary lepton, to distinguish it from the soft lepton tag. The isolation of the primary lepton is defined as the transverse energy in the calorimeter sur- rounding the lepton in a cone of R  0:4?but not in- cluding the lepton ET itself?divided by the electron (muon) ET (pT). The lepton is considered isolated if the isolation is less than 0.1. Note that this isolation definition is different than the isolation variable ISLT which is used with the SLTe algorithm. We reject cosmic ray muons, conversion electrons, and Z bosons. Only one primary lepton is allowed to be recon- structed in the lepton? jets sample, and the flavor of that lepton must be consistent with the trigger path. More de- tails regarding this event selection can be found in Ref. [5]. An inclusive W boson sample is constructed by requiring high missing transverse energy, E6 T > 30 GeV. We sup- press background events by requiring HT > 250 GeV when three or more jets are present. We define HT as the scalar sum of the transverse energy of the primary lepton, jets, and E6 T . In total, using events collected from February 2002 through March 2007 corresponding to an integrated lumi- nosity of RLdt ? 1:7 0:1 fb1, we find 2196 ??pretag?? events with 3 jets after the event selection described. We apply the SLTe algorithm to this sample and find 120 ??tag?? events with  3 jets with at least one SLTe, of which five have two SLTe tags. Out of 120 events, 48 have a SECVTX tag present, in agreement with the expected 45 such double tags. We use PYTHIA MC simulation with mt ? 175 GeV=c2 to simulate top-quark pair production. By default, all MC simulated samples are generated with the CTEQ5L [23] parton distribution functions (PDF), and the program EVTGEN [24] is used to decay the particle species. We (GeV/c)T Track peSLT 2 4 6 8 10 12 14 16 18 HF Electron Efficienc y 0 0.1 0.2 0.3 0.4 0.5 0.6 Tagging EfficiencyeSLT H a dr o n Ef fic ie n cy 0 0.005 0.01 HF Electrons Hadrons (a) (GeV)TJet E 20 30 40 50 60 70 80 90 100 110 120 HF Electron Efficienc y 0 0.1 0.2 0.3 0.4 0.5 0.6 Tagging EfficiencyeSLT Hadron Efficienc y 0 0.005 0.01HF Electrons Hadrons (b) FIG. 12 (color online). Predicted efficiency to tag an electron from semileptonic decay of HF and a hadron candidate SLTe track in tt events as a function of the track pT (a) and corrected jet ET (b). The left axis indicates the tagging efficiency for the electrons and the right axis indicates the tagging efficiency for the hadrons. MEASUREMENT OF THE tt PRODUCTION CROSS . . . PHYSICAL REVIEW D 81, 092002 (2010) 092002-13 measure Att by counting the number of events that pass the lepton? jets event selection described above divided by the total number of events generated. We do not restrict the decay channel at the generator level, so it is possible for some signal from other decay channels [25] to be recon- structed and categorized as lepton? jets. We then correct the acceptance with various scale factors to account for differences between simulation modeling and data. These scale factors result from differences in modeling of the lepton identification and isolation components, as well as corrections for requirements imposed on data but not the simulation, including the trigger efficiency, the position of the primary vertex along z, and the quality of the lepton track. The total acceptance for tt events after corrections is 6.2%, comparable with the acceptance of other analyses in this final state [3?5]. A breakdown of the corrected accep- tance by jet multiplicity and W lepton type is shown in Table II. Scaling the acceptance by the tt production cross section (assumed here to be 6.7 pb) and integrated lumi- nosity yields a total pretag event expectation of 716:7 44:4 events, where the dominant uncertainties result from the uncertainty on the luminosity and the acceptance corrections. Finally, we measure the efficiency to find at least one SLTe tag in events that pass the event selection by applying the calorimeter requirements, tag matrix, fake matrix, and conversion efficiency scale factors to candidate tracks. Assuming tt ? 6:7 pb, and RLdt ? 1:7 fb1, we expect 59:2 5:0 events after tagging in the  3 jet region. This corresponds to a per-event tagging efficiency of tt ? 8:3%. B. Background estimation and sample composition We consider three categories of background in the iden- tification of tt events. The first category, whose contribu- tion is derived from MC simulation, includes the production of WW, WZ, ZZ (where one Z can be pro- duced off shell), single top-quark production, Z in associa- tion with jets, and Drell-Yan in association with jets. These backgrounds have a small uncertainty on the production cross section or contribute sufficiently little to the total background that a large uncertainty has little effect. For diboson production, we use PYTHIA generated samples scaled by their respective theoretical cross sections to estimate their contribution to the pretag and tag samples. The estimate for single top-quark production uses a com- bination of MADEVENT [26] for generation and PYTHIA for showering, and is calculated separately for s- and t-channel processes, again using the theoretical cross sections. Z? jets and Drell-Yan? jets use an ALPGEN [27] and PYTHIA combination, where ALPGEN is used for the generation and PYTHIA is used for the showering. The cross section is scaled to match the measured Z? jets cross section with an additional 1:2 0:2 correction to match the measured jet multiplicity spectrum. Table III lists the cross sections used for each process. TABLE II. Corrected tt acceptance in the lepton? jets decay channel. We have required HT > 250 GeV for events with  3 jets and E6 T > 30 GeV. Combined statistical and systematic uncertainties are shown. Corrected tt acceptance (%) Lepton 1 jet 2 jets 3 jets 4 jets  5 jets CEM 0:163 0:003 0:862 0:011 1:403 0:017 1:493 0:018 0:519 0:007 CMUP 0:089 0:002 0:477 0:009 0:788 0:015 0:826 0:015 0:284 0:006 CMX 0:042 0:001 0:220 0:005 0:353 0:008 0:381 0:008 0:130 0:003 Total 0:295 0:005 1:559 0:024 2:543 0:039 2:700 0:041 0:932 0:015 TABLE III. Cross sections and generators used for the MC-simulation-derived backgrounds. The production of single top, Z? jets, and Drell-Yan? jets is constrained to decay (semi)leptonically at generator level. The cross sections for these processes are multiplied by the leptonic branching fraction. The decay of the diboson simulation, however, remains uncon- strained, and the full production cross section is quoted. Process Cross section BF (pb) Generator WW 12:4 0:25 [28] PYTHIA WZ 3:96 0:06 [28] PYTHIA ZZ 2:12 0:15 [28] PYTHIA Single top (s channel) 0:29 0:02 [29] MADEVENT? PYTHIA Single top (t channel) 0:66 0:03 [29] MADEVENT? PYTHIA Z? jets 308 51 [30] ALPGEN? PYTHIA Drell-Yan? jets 2882 480 [30] ALPGEN? PYTHIA T. AALTONEN et al. PHYSICAL REVIEW D 81, 092002 (2010) 092002-14 The second category consists of background from multi- jet production, called QCD. We estimate the QCD contri- bution by releasing the E6 T requirement and fitting the total E6 T distribution to templates for the backgrounds and sig- nal. To model the QCD E6 T spectrum, we use two samples: a PYTHIA b b dijet sample, and a data sample with an ET > 20 GeV electron candidate that fails at least two electron ID requirements. This sample is principally composed of multijet events with a similar topology to those that fake a high-ET electron. We fit for the fraction of QCD events in the sample by fixing the tt and MC simulation-driven background normalizations, and varying the W ? jets and QCD template normalizations separately. The total QCD contribution has virtually no dependence on the assumed tt cross section. We also include a 15% systematic uncer- tainty due to the real electron contamination in the elec- tronlike sample. Table IV shows the measured fits for the fraction of pretag events with E6 T > 30 GeV that are due to pretag and tag QCD events, FQCDpre and FQCDtag , respectively. The result of the fit in the pretag region for  3 tags is shown in Fig. 13. The third category and largest background is the pro- duction of W bosons in association with multiple jets. We use a combination of simulation and data-driven tech- niques to measure this background. We use ALPGEN as the generator of the W ? multijet data sets and PYTHIA for fragmentation and showering. The W ? jet normalization is determined by assuming that all pretag data events, not already accounted for by tt or by the first two background categories, must be W ? jets. The tag estimate is derived from the pretag estimate by assuming that the tagging efficiency measured in MC simulation for separate HF categories is accurate and only the relative amount of HF needs adjustment. The equations below elucidate this procedure: NpreW ? N pre data  N pre MC  N pre QCD  N pre tt ; (3) NtagW?b b ? N pre W ? 2bF2b ? 1bF1b?; (4) NtagW?c c ? N pre W ? 2cF2c ? 1cF1c?; (5) NtagW?LF ? N pre W 0b;0c?1 F2b  F1b  F2c  F1c?; (6) where Ntag and Npre are the number of tag and pretag events for various signal and background components, and LF refers to light flavor. The tagging efficiencies are measured in separate HF categories, where the sub- script designates the number of reconstructed jets in an event identified as a b or c jet with information from the generator. For bookkeeping purposes, the presence of a b jet supersedes the presence of a c jet. The HF fractions F designate the fraction of W ? jet events for each HF category. While both the HF efficiencies and HF fractions are measured in MC simulation, the fractions are calibrated by a single, multiplicative K factor, K ? 1:0 0:4, de- rived from a data/MC comparison of multijet events with HF enhanced by a SECVTX tag. The systematic uncertainty is dominated by the contribution from varying theQ2 of the samples and the agreement of the K factor across jet multi- plicities. Phase-space overlap of jets simulated by ALPGEN TABLE IV. Summary of the fraction of the pretag sample due to pretag and tag QCD events for different jet multiplicities. 1 jet 2 jets  3 jets FQCDpre (%) 3:7 6:0 4:6 0:6 9:2 1:5 FQCDtag (%) 0:045 0:011 0:10 0:02 0:28 0:14 (GeV)TE 0 20 40 60 80 100 120 Events/6.0 Ge V 0 50 100 150 200 250 300 350 400 450 Events/6.0 Ge V QCD Template Fit -1 Ldt=1.7 fb? 3 Jets)?Data (Pretag: tt QCD Template W+Jets Z+Jets Drell-Yan Single Top WW/WZ/ZZ FIG. 13 (color online). QCD fit for pretag events with 3 jets. W ? jet and QCD templates are allowed to float. TABLE V. Heavy-flavor fractions multiplied by the K factor for W ? jet events. Uncertainties are dominated by the agree- ment of the K factor across jet bins and the Q2 scale. All numbers are shown in units of %. Fraction 1 jet 2 jets 3 jets  4 jets F1b 0:8 0:3 1:6 0:6 3:0 1:1 3:7 1:4 F2b 1:0 0:4 2:2 0:8 3:5 1:3 F1c 5:8 1:6 9:1 2:6 10:2 3:3 12:1 3:9 F2c 1:5 0:6 3:4 1:3 6:3 2:3 TABLE VI. SLTe tagging efficiency for different classes of HF inW ? jet events. Uncertainties shown include all SLTe tagging systematic uncertainties. All numbers are shown in units of %. 1 jet 2 jets 3 jets  4 jets 0b;0c 0:92 0:06 1:89 0:11 3:01 0:17 4:24 0:24 1b 3:33 0:16 4:39 0:22 5:43 0:29 6:80 0:36 2b 6:72 0:33 7:26 0:37 9:55 0:45 1c 1:61 0:09 2:50 0:14 3:46 0:20 4:78 0:28 2c 3:11 0:17 4:17 0:23 5:58 0:30 MEASUREMENT OF THE tt PRODUCTION CROSS . . . PHYSICAL REVIEW D 81, 092002 (2010) 092002-15 and PYTHIA is accounted for by allowing ALPGEN to simu- late those HF jets well separated in  space and allowing PYTHIA to simulate the rest [31]. Tables V and VI show the measured values for the HF fractions and efficiencies, respectively. C. Measurement and uncertainties Although the W ? jets background depends explicitly on the assumed value of tt [see Eq. (3)], we can solve algebraically for the cross section, resulting in a central value of 7:8 2:4 pb, where the statistical uncertainty is determined through error propagation and is verified with pseudoexperiments. The final sample composition is shown in Table VII and is shown graphically in Fig. 14. The table shows the tt expectation for the measured cross section along with the background estimates corrected for the signal contribution. The observed number of pretag events and the expected number of pretag tt events are also presented. The combined systematic uncertainties due to the lumi- nosity, acceptance, background cross sections, SLTe tag- ging, K factor, and QCD fit are given in the table. Note that some of the background contributions?in particular, the W ? jets components?are negatively correlated with each other, and this is reflected in the systematic uncertainties presented. Figure 15 show the SLTe tag pT distribution and the event HT distribution in the  3 jet region. In the previous sections, we have described systematic uncertainties related to the SLTe tagger and the back- ground estimations. The tagger uncertainties derive from the calorimeter variable modeling, the tag- and fake-matrix predictions, the conversion (mis)identification scale fac- tors, and the jet environment correction from the b-jet tuning. Each of the tagger uncertainties are uncorrelated because they have been derived in separate samples with distinct measurement techniques. The background uncer- tainties are derived from the theoretical or experimental production cross sections, the W ? jet HF K factor, the QCD fit, and the acceptance modeling. Here we discuss the uncertainties arising from the jet energy scale (JES) [18] and the modeling of the tt signal. The systematic uncer- tainties are summarized in Table VIII. TABLE VII. Sample composition of lepton? jet events with  1 SLTe tag corrected for the measured signal contribution. Uncertainties include effects from luminosity, acceptance corrections, cross section uncertainties, SLTe tagger modeling, K factor, and the QCD fit. Process 1 jet 2 jets 3 jets 4 jets  5 jets Pretag 120 599 19 695 1358 645 193 Pretag tt ( ? 7:84 pb) 39:82 2:11 211:2 11:2 345:4 18:3 366:6 19:4 126:67 6:71 WW 12:87 1:27 12:36 1:14 1:53 0:14 0:64 0:06 0:25 0:02 WZ 1:37 0:13 3:04 0:26 0:41 0:04 0:21 0:02 0:06 0:01 ZZ 0:16 0:02 0:17 0:02 0:05 0:01 0:02 0:00 0:01 0:00 Single top (s) 0:55 0:06 2:31 0:23 0:46 0:05 0:17 0:02 0:05 0:01 Single top (t) 1:88 0:17 2:67 0:25 0:36 0:03 0:09 0:01 0:01 0:00 Z? jets 46:3 10:1 19:52 4:02 2:44 0:44 1:09 0:20 0:28 0:05 Drell-Yan? jets 10:01 2:27 6:32 1:42 1:11 0:25 0:33 0:07 0:09 0:02 QCD 53:9 14:1 20:20 4:65 3:75 1:92 1:78 0:91 0:53 0:27 W ? b b 28:2 10:9 22:74 8:70 2:43 0:94 1:04 0:43 0:23 0:10 W ? c c, W ? c 104:2 30:2 47:1 14:6 3:80 1:31 1:66 0:62 0:36 0:15 W ? light-flavor 960:7 90:8 281:0 22:9 18:56 2:10 5:60 1:14 1:22 0:32 Total W ? jets 1093 101 350:8 24:0 24:78 2:05 8:30 1:38 1:81 0:43 Backgrounds 1220:0 94:8 417:4 25:5 34:89 2:36 12:64 1:32 3:09 0:41 tt ( ? 7:84 pb) 1:41 0:10 13:25 0:96 26:27 1:94 30:70 2:16 12:41 0:86 Tags 1312 427 56 45 19 Number of Jets 1 2 3 4 5? Event s 0 200 400 600 800 1000 1200 1400 Event s Data =7.8 pb) tt ? (tt QCD W+LF cW+c bW+b Z+Jets Drell-Yan Single Top WW/WZ/ZZ -1 Ldt=1.7 fb? Number of Jets 3 4 5?0 10 20 30 40 50 60 70 FIG. 14 (color online). Jet multiplicity of SLTe tagged events in the lepton? jets data set. The embedded plot is the  3 jet subsample. Hashed areas represent the combined systematic uncertainties, while the data show only the statistical uncertainty. T. AALTONEN et al. PHYSICAL REVIEW D 81, 092002 (2010) 092002-16 The effect of the JES uncertainty is calculated by adjust- ing the jet energy corrections that are applied to the MC simulation by1 and remeasuring the cross section. The central value for the cross section is 7.2 pb with ?1 JES and 8.5 pb with 1 JES, so we assign a 8:6% relative systematic uncertainty due to the JES. We also determine the uncertainty from initial state radiation (ISR) and final state radiation (FSR) by remea- suring the acceptance with the PYTHIA MC simulation tuned with more or less ISR and FSR. We take the mean deviation as a systematic uncertainty. Uncertainties related to top-quark kinematic modeling and the jet fragmentation model are considered by replac- ing PYTHIA with HERWIG [32] as the event generator for the tt sample. The result is a 2.2% relative difference in the tt acceptance, which we take as a systematic uncertainty. The uncertainty from PDFs is considered from three sources. The first source is the difference in tt acceptance when the CTEQ5L PDF set is reweighted within its own uncertainties. The second source is the difference between the CTEQ5L and an MRST98 [33] set. The third source is calculated by varying S within the same PDF set. The final PDF uncertainty is calculated by taking the larger of the first two uncertainties and combining it in quadrature with the S uncertainty. This results in a 0.9% uncertainty on the cross section. The final result is tt ? 7:8 2:4?stat?  1:6?syst?  0:5?lumi? pb; (7) where we separate the luminosity uncertainty from the other systematic uncertainties. Although we have assumed for this analysis a top-quark mass of 175 GeV=c2, the world average for the top-quark mass is now approxi- mately 172:4 GeV=c2. This moves the theoretical value of the cross section to approximately 7.2 pb. The system- atic uncertainty on the tt cross section due to the error on the top-quark mass is small, and leaves the result unchanged. VIII. CONCLUSIONS We have performed the first measurement of the tt production cross section with SLTe tags in run II of the Tevatron. This measurement, tt ? 7:8 2:4?stat?  1:6?syst?  0:5?lumi? pb, is consistent with the theoretical value [8] tt ? 6:7 0:8 pb (mt ? 175 GeV=c2), as well as the current CDF average [34] tt ? 7:02 0:63 pb. While statistically limited, this measurement demonstrates the consistency of the top-quark production cross section in the lepton? jets final state with soft electron b-tagging. This measurement also provides an experimental basis for investigating other high-pT physics measurements with the soft electron tagging technique. (GeV/c)T Track peSLT 5 10 15 20 25 30 35 40 Tags/2.0 GeV/ c 0 10 20 30 40 50 60 GeV/c Tags/2.0 GeV/ c -1 Ldt=1.7 fb? Data=7.8)tt? (ttQCD W+LF cW+c bW+b Z+Jets Drell-Yan Single Top WW/WZ/ZZ (a) GeVTEvent H 250 300 350 400 450 500 550 600 650 700 Events/25 Ge V 0 5 10 15 20 25 30 ( eV) Events/25 Ge V -1 Ldt=1.7 fb? Data=7.8)tt? (ttQCD W+LF cW+c bW+b Z+Jets Drell-Yan Single Top WW/WZ/ZZ (b) FIG. 15 (color online). (a) pT distribution of SLTe tags in lepton? jet events with  3 jets. (b) HT distribution of SLTe tagged events with  3 jets. TABLE VIII. Summary of systematic uncertainties. Source Relative uncertainty on tt (%) Jet energy scale 8.4 QCD fit 5.0 K factor 3.0 HERWIG/PYTHIA 2.2 Acceptance corrections 1.6 Background cross section 0.6 PDFs 0.9 FSR 0.6 ISR 0.5 Conversion ID efficiency SFs 10.7 Fake matrix 7.8 Calorimeter modeling 7.7 Tag matrix 6.8 Jet environment correction 5.4 Total tagger uncertainty 17.6 Total 20.6 MEASUREMENT OF THE tt PRODUCTION CROSS . . . PHYSICAL REVIEW D 81, 092002 (2010) 092002-17 ACKNOWLEDGMENTS We thank the Fermilab staff and the technical staffs of the participating institutions for their vital contributions. 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