Humans in hazardous environments take actions to reduce unnecessary risk, including limiting exposure to radioactive materials where ionising radiation can be a threat to human health. Robots can adopt the same approach of risk avoidance to minimise exposure to radiation, therefore limiting damage to electronics and materials. Reducing a robot's exposure to radiation results in longer operational lifetime and better return on investment for nuclear sector stakeholders. This work achieves radiation avoidance through the use of layered costmaps, to inform path planning algorithms of this additional risk. Interpolation of radiation observations into the configuration space of the robot is accomplished using an inverse distance weighting approach. This technique was successfully demonstrated using an unmanned ground vehicle running the Robot Operating System equipped with compatible gamma radiation sensors, both in simulation and in real-world mock inspection missions, where the vehicle was exposed to radioactive materials in Lancaster University's Neutron Laboratory. The addition of radiation avoidance functionality was shown to reduce total accumulated dose to background levels in real-world deployment and up to a factor of 10 in simulation.

This paper describes a novel autonomous ground vehicle that is designed for exploring unknown environments which contain sources of ionising radiation, such as might be found in a nuclear disaster site or a legacy nuclear facility. While exploring the environment, it is important that the robot avoids radiation hot spots to minimise breakdowns. Broken down robots present a real problem: they not only cause the mission to fail but they can block access routes for future missions. Until now, such robots have had no autonomous gamma radiation avoidance capabilities. New software algorithms are presented that allow radiation measurements to be converted into a format in which they can be integrated into the robot’s navigation system so that it can actively avoid receiving a high radiation dose during a mission. An unmanned ground vehicle was fitted with a gamma radiation detector and an autonomous navigation package that included the new radiation avoidance software. The full system was evaluated experimentally in a complex semi-structured environment that contained two radiation sources. In the experiment, the robot successfully identified both sources and avoided areas that were found to have high levels of radiation while navigating between user defined waypoints. This advancement in the state-of-the-art has the potential to deliver real benefit to the nuclear industry, in terms of both increased chance of mission success and reduction of the reliance on human operatives to perform tasks in dangerous radiation environments.

The utilisation of robots in hazardous nuclear environments has potential to reduce risk to humans. However, historical use has been largely limited to specific missions rather than broader industry-wide adoption. Testing and verification of robotics in realistic scenarios is key to gaining stakeholder confidence but hindered by limited access to facilities that contain radioactive materials. Simulations offer an alternative to testing with actual radioactive sources, provided they can readily describe the behaviour of robotic systems and ionising radiation within the same environment. This work presents a quick and easy way to generate simulated but realistic deployment scenarios and environments which include ionising radiation, developed to work within the popular robot operating system compatible Gazebo physics simulator. Generated environments can be evolved over time, randomly or user-defined, to simulate the effects of degradation, corrosion or to alter features of certain objects. Interaction of gamma radiation sources within the environment, as well as the response of simulated detectors attached to mobile robots, is verified against the MCNP6 Monte Carlo radiation transport code. The benefits these tools provide are highlighted by inclusion of three real-world nuclear sector environments, providing the robotics community with opportunities to assess the capabilities of robotic systems and autonomous functionalities.

In exploratory robotics for nuclear decommissioning, environmental understanding is key. Sites such as Fukushima Daiichi Power Station and Sellafield often use manually controlled or semi-autonomous vehicles for exploration and monitoring of assets. In many cases, robots have limited sensing capabilities such as a single camera to provide video to the operators. These limitations can cause issues, where a lack of data about the environment and limited understanding of depth within the image can lead to a mis-understanding of asset state or potential damage being caused to the robot or environment. This work aims to aid operators by using the limited sensors provided i.e. a single monocular camera, to allow estimates of the robot's surrounding environments to be generated insitu without having to off load large amounts of data for processing. This information can then be displayed as a mesh and manipulated in 3D to improve the operator awareness. Due to the target environment for operation being radioactive, speed is prioritised over accuracy, due to the damaging effects radiation can cause to electronics. In well lit environments images can be overlaid onto the meshes to improve the operators understanding and add detail to the mesh. From the results it has been found that 3D meshes of an environment/object can be generated in an acceptable time frame, less than 5 minutes. This differs from many current methods which require offline processing due to heavy computational requirement of Photogrammetry, or are far less informative giving data as raw point clouds, which can be hard to interpret. The proposed technique allows for lower resolution meshes good enough for avoiding collisions within an environment to be generated during a mission due to it's speed of generation, however there are still several issues which need to be solved before such a technique is ready for deployment.

In this paper we propose and demonstrate a novel void characterisation algorithm which is able to distinguish between internal and external voids that are present in point clouds of both manifold and non-manifold objects and 3D scenes. We demonstrate the capabilities of our algorithm using several point clouds representing both scenes and objects. Our algorithm is shown in both a descriptive overview format as well as pseudocode. We also compare a variety of different void detection algorithms and then present a novel refinement to the best performing of these algorithms. Our refinement allows for voids in point clouds to be detected more efficiently, with fewer false positives and with over an order of magnitude improvement in terms of run time. We show our run time performance and compare it to results obtained using alternative algorithms, when tested using popular single board computers. This comparison is important as our work is intended for online robotics applications, where hardware is typically of low computational power. The target application for this work is 3D scene reconstruction to aid in the decommissioning of nuclear facilities.

 Abstract — Currently, inspection of remote environments poses potential hazards to the human operator. Mixed reality presents the unique opportunity for the human operators & the piloted robots to co-exist within a virtual world that is then spatially mapped to the physical world. This can then act as a platform to conduct remote inspection, where the human operators could potentially possess heightened situational awareness and control

When using exploratory robotics for nuclear decommissioning, the generation of accurate and complete maps of the environment is critical. One format these maps can take is point clouds. However, when generating point clouds it is likely that voids, resulting from shadows created by features within the environment for example, will exist within them. Previous research studies have developed techniques which enable such voids to be interpolated. Unfortunately, for hazardous environments, this interpolation can be detrimental and have severe safety implications. This paper proposes a new algorithmic method for simplifying the detection of voids in point clouds. Once detected, the voids are validated, to make sure they exist within the scan-able part of the environment and if they do then they are marked for further investigation by the robot. This enables a more complete point cloud to be generated. To demonstrate the capabilities of the method, it was initially applied to a set of simplified scenes, where it was able to detect all the voids that were present, whilst also being compared to another algorithm. Following this the method was applied to a more realistic scenario. In this example, many, but not all of the voids were correctly identified. A discussion follows explaining why some voids were not detected and how future research aims to address this.

T. Aaltonen, S. Amerio, D. Amidei, A. Anastassov, A. Annovi, J. Antos, G. Apollinari, J. A. Appel, T. Arisawa, A. Artikov, J. Asaadi, W. Ashmanskas, B. Auerbach, A. Aurisano, F. Azfar, W. Badgett, T. Bae, A. Barbaro-Galtieri, V. E. Barnes, B. A. Barnett, P. Barria, P. Bartos, M. Bauce, F. Bedeschi, S. Behari, G. Bellettini, J. Bellinger, D. Benjamin, A. Beretvas, A. Bhatti, K. R. Bland, B. Blumenfeld, A. Bocci, A. Bodek, D. Bortoletto, J. Boudreau, A. Boveia, L. Brigliadori, C. Bromberg, E. Brucken, J. Budagov, H. S. Budd, K. Burkett, G. Busetto, P. Bussey, P. Butti, A. Buzatu, A. Calamba, S. Camarda, M. Campanelli, F. Canelli, B. Carls, D. Carlsmith, R. Carosi, S. Carrillo, B. Casal, M. Casarsa, A. Castro, P. Catastini, D. Cauz, V. Cavaliere, A. Cerri, L. Cerrito, Y. C. Chen, M. Chertok, G. Chiarelli, G. Chlachidze, K. Cho, D. Chokheli, A. Clark, C. Clarke, M. E. Convery, J. Conway, M. Corbo, M. Cordelli, C. A. Cox, D. J. Cox, M. Cremonesi, D. Cruz, J. Cuevas, R. Culbertson, N. d’Ascenzo, M. Datta, P. de Barbaro, L. Demortier, M. Deninno, M. D’Errico, F. Devoto, A. Di Canto, B. Di Ruzza, J. R. Dittmann, S. Donati, M. D’Onofrio, M. Dorigo, A. Driutti, K. Ebina, R. Edgar, R. Erbacher, S. Errede, B. Esham, S. Farrington, J. P. Fernández Ramos, R. Field, G. Flanagan, R. Forrest, M. Franklin, J. C. Freeman, H. Frisch, Y. Funakoshi, C. Galloni, A. F. Garfinkel, P. Garosi, H. Gerberich, E. Gerchtein, S. Giagu, V. Giakoumopoulou, K. Gibson, C. M. Ginsburg, N. Giokaris, P. Giromini, V. Glagolev, D. Glenzinski, M. Gold, D. Goldin, A. Golossanov, G. Gomez, G. Gomez-Ceballos, M. Goncharov, O. González López, I. Gorelov, A. T. Goshaw, K. Goulianos, E. Gramellini, C. Grosso-Pilcher, J. Guimaraes da Costa, S. R. Hahn, J. Y. Han, F. Happacher, K. Hara, M. Hare, R. F. Harr, T. Harrington-Taber, M. Hartz, K. Hatakeyama, C. Hays, J. Heinrich, M. Herndon, A. Hocker, Z. Hong, W. Hopkins, S. Hou, R. E. Hughes, U. Husemann, M. Hussein, J. Huston, G. Introzzi, M. Iori, A. Ivanov, E. James, D. Jang, B. Jayatilaka, E. J. Jeon, S. Jindariani, M. Jones, K. K. Joo, S. Y. Jun, T. R. Junk, M. Kambeitz, T. Kamon, P. E. Karchin, A. Kasmi, Y. Kato, W. Ketchum, J. Keung, B. Kilminster, D. H. Kim, H. S. Kim, J. E. Kim, M. J. Kim, S. H. Kim, S. B. Kim, Y. J. Kim, Y. K. Kim, N. Kimura, M. Kirby, K. Knoepfel, K. Kondo, D. J. Kong, J. Konigsberg, A. V. Kotwal, M. Kreps, J. Kroll, M. Kruse, T. Kuhr, M. Kurata, A. T. Laasanen, S. Lammel, M. Lancaster, K. Lannon, G. Latino, H. S. Lee, J. S. Lee, S. Leo, S. Leone, J. D. Lewis, A. Limosani, E. Lipeles, A. Lister, Q. Liu, T. Liu, S. Lockwitz, A. Loginov, D. Lucchesi, A. Lucà, J. Lueck, P. Lujan, P. Lukens, G. Lungu, J. Lys, R. Lysak, R. Madrak, P. Maestro, S. Malik, G. Manca, A. Manousakis-Katsikakis, L. Marchese, F. Margaroli, P. Marino, K. Matera, M. E. Mattson, A. Mazzacane, P. Mazzanti, R. McNulty, A. Mehta, P. Mehtala, C. Mesropian, T. Miao, D. Mietlicki, A. Mitra, H. Miyake, S. Moed, N. Moggi, C. S. Moon, R. Moore, M. J. Morello, A. Mukherjee, Th. Muller, P. Murat, M. Mussini, J. Nachtman, Y. Nagai, J. Naganoma, I. Nakano, A. Napier, J. Nett, T. Nigmanov, L. Nodulman, S. Y. Noh, O. Norniella, L. Oakes, S. H. Oh, Y. D. Oh, T. Okusawa, R. Orava, L. Ortolan, C. Pagliarone, E. Palencia, P. Palni, V. Papadimitriou, W. Parker, G. Pauletta, M. Paulini, C. Paus, T. J. Phillips, G. Piacentino, E. Pianori, J. Pilot, K. Pitts, C. Plager, L. Pondrom, S. Poprocki, K. Potamianos, A. Pranko, F. Prokoshin, F. Ptohos, G. Punzi, I. Redondo Fernández, P. Renton, M. Rescigno, F. Rimondi, L. Ristori, A. Robson, T. Rodriguez, S. Rolli, M. Ronzani, R. Roser, J. L. Rosner, F. Ruffini, A. Ruiz, J. Russ, V. Rusu, W. K. Sakumoto, Y. Sakurai, L. Santi, K. Sato, V. Saveliev, A. Savoy-Navarro, P. Schlabach, E. E. Schmidt, T. Schwarz, L. Scodellaro, F. Scuri, S. Seidel, Y. Seiya, A. Semenov, F. Sforza, S. Z. Shalhout, T. Shears, P. F. Shepard, M. Shimojima, M. Shochet, I. Shreyber-Tecker, A. Simonenko, K. Sliwa, J. R. Smith, F. D. Snider, H. Song, V. Sorin, R. St. Denis, M. Stancari, D. Stentz, J. Strologas, Y. Sudo, A. Sukhanov, I. Suslov, K. Takemasa, Y. Takeuchi, J. Tang, M. Tecchio, P. K. Teng, J. Thom, E. Thomson, V. Thukral, D. Toback, S. Tokar, K. Tollefson, T. Tomura, D. Tonelli, S. Torre, D. Torretta, P. Totaro, M. Trovato, F. Ukegawa, S. Uozumi, F. Vázquez, G. Velev, C. Vellidis, C. Vernieri, M. Vidal, R. Vilar, J. Vizán, M. Vogel, G. Volpi, P. Wagner, R. Wallny, S. M. Wang, D. Waters, W. C. Wester III, D. Whiteson, A. B. Wicklund, S. Wilbur, H. H. Williams, J. S. Wilson, P. Wilson, B. L. Winer, P. Wittich, S. Wolbers, H. Wolfe, T. Wright, X. Wu, Z. Wu, K. Yamamoto, D. Yamato, T. Yang, U. K. Yang, Y. C. Yang, W.-M. Yao, G. P. Yeh, K. Yi, J. Yoh, K. Yorita, T. Yoshida, G. B. Yu, I. Yu, A. M. Zanetti, Y. Zeng, C. Zhou, and S. Zucchelli

We describe a measurement of the ratio of the cross sections times branching fractions of the B + c meson in the decay mode B + c → J/ψ µ + ν to the B + meson in the decay mode B + → J/ψ K + in proton-antiproton collisions at center-of-mass energy √ s = 1 . 96 TeV. The measurement is based on the complete CDF RunII data set, which comes from an integrated luminosity of 8 . 7 fb − 1 . The ratio of the production cross sections times branching fractions for B + c and B + mesons with momentum transverse to the beam greater than 6 GeV /c and rapidity magnitude smaller than 0.6 is 0 . 211 ± 0 . 012 (stat) +0 . 021 − 0 . 020 (syst). Using the known B + → J/ψ K + branching fraction, the known B + production cross section, and a selection of the predicted B + c → J/ψ µ + ν branching fractions, the range for the total B + c production cross section is estimated.

T. Aaltonen, S. Amerio, D. Amidei, A. Anastassov, A. Annovi, J. Antos, G. Apollinari, J. A. Appel, T. Arisawa, A. Artikov, J. Asaadi, W. Ashmanskas, B. Auerbach, A. Aurisano, F. Azfar, W. Badgett, T. Bae, A. Barbaro-Galtieri, V. E. Barnes, B. A. Barnett, P. Barria, P. Bartos, M. Bauce, F. Bedeschi, S. Behari, G. Bellettini, J. Bellinger, D. Benjamin, A. Beretvas, A. Bhatti, K. R. Bland, B. Blumenfeld, A. Bocci, A. Bodek, D. Bortoletto, J. Boudreau, A. Boveia, L. Brigliadori, C. Bromberg, E. Brucken, J. Budagov, H. S. Budd, K. Burkett, G. Busetto, P. Bussey, P. Butti, A. Buzatu, A. Calamba, S. Camarda, M. Campanelli, F. Canelli, B. Carls, D. Carlsmith, R. Carosi, S. Carrillo, B. Casal, M. Casarsa, A. Castro, P. Catastini, D. Cauz, V. Cavaliere, A. Cerri, L. Cerrito, Y. C. Chen, M. Chertok, G. Chiarelli, G. Chlachidze, K. Cho, D. Chokheli, A. Clark, C. Clarke,M. E. Convery, J. Conway,M. Corbo, M. Cordelli, C. A. Cox, D. J. Cox, M. Cremonesi, D. Cruz, J. Cuevas, R. Culbertson, N. d’Ascenzo, M. Datta, P. de Barbaro, L. Demortier, M. Deninno, M. D’Errico, F. Devoto, A. Di Canto, B. Di Ruzza, J. R. Dittmann, S. Donati,M.D’Onofrio,M.Dorigo, A. Driutti, K. Ebina, R. Edgar, A. Elagin, R. Erbacher, S. Errede, B. Esham, S. Farrington, J. P. Fernández Ramos, R. Field, G. Flanagan, R. Forrest, M. Franklin, J. C. Freeman, H. Frisch, Y. Funakoshi, C. Galloni, A. F. Garfinkel, P. Garosi, H.Gerberich, E.Gerchtein, S. Giagu, V. Giakoumopoulou, K. Gibson, C.M.Ginsburg, N.Giokaris, P. Giromini, V. Glagolev, D. Glenzinski,M.Gold, D.Goldin, A.Golossanov, G.Gomez, G. Gomez-Ceballos,M.Goncharov, O. González López, I. Gorelov, A. T. Goshaw, K. Goulianos, E. Gramellini, C. Grosso-Pilcher, R. C. Group, J. Guimaraes daCosta, S. R.Hahn, J. Y. Han, F. Happacher, K. Hara,M.Hare, R. F. Harr, T. Harrington-Taber, K. Hatakeyama, C. Hays, J. Heinrich, M. Herndon, A. Hocker, Z. Hong, W. Hopkins, S. Hou, R. E. Hughes, U. Husemann, M. Hussein, J. Huston, G. Introzzi, M. Iori, A. Ivanov, E. James, D. Jang, B. Jayatilaka, E. J. Jeon, S. Jindariani, M. Jones, K. K. Joo, S. Y. Jun, T. R. Junk, M. Kambeitz, T. Kamon, P. E.Karchin,A.Kasmi,Y.Kato,W.Ketchum, J.Keung, B.Kilminster, D. H.Kim,H. S.Kim, J. E.Kim, M. J. Kim, S. H. Kim, S. B. Kim, Y. J. Kim, Y. K. Kim, N. Kimura, M. Kirby, K. Knoepfel, K. Kondo, D. J. Kong, J. Konigsberg, A. V. Kotwal, M. Kreps, J. Kroll, M. Kruse, T. Kuhr, M. Kurata, A. T. Laasanen, S. Lammel, M. Lancaster, K. Lannon, G. Latino, H. S. Lee, J. S. Lee, S. Leo, S. Leone, J. D. Lewis, A. Limosani, E. Lipeles, A. Lister, H. Liu, Q. Liu, T. Liu, S. Lockwitz, A. Loginov, D. Lucchesi, A. Lucà, J. Lueck, P. Lujan, P. Lukens, G. Lungu, J. Lys, R. Lysak, R. Madrak, P. Maestro, S. Malik, G. Manca, A. Manousakis-Katsikakis, L. Marchese, F. Margaroli, P. Marino, K. Matera, M. E. Mattson, A.Mazzacane, P.Mazzanti, R.McNulty, A.Mehta, P.Mehtala, C.Mesropian, T.Miao, D.Mietlicki, A.Mitra, H. Miyake, S. Moed, N. Moggi, C. S. Moon, R. Moore, M. J. Morello, A. Mukherjee, Th. Muller, P. Murat, M. Mussini, J. Nachtman, Y. Nagai, J. Naganoma, I. Nakano, A. Napier, J. Nett, C. Neu, T. Nigmanov, L. Nodulman, S. Y. Noh, O. Norniella, L. Oakes, S. H. Oh, Y. D. Oh, I. Oksuzian, T. Okusawa, R. Orava, L. Ortolan, C. Pagliarone, E. Palencia, P. Palni, V. Papadimitriou, W. Parker, G. Pauletta, M. Paulini, C. Paus, T. J. Phillips, G. Piacentino, E. Pianori, J. Pilot, K. Pitts, C. Plager, L. Pondrom, S. Poprocki, K. Potamianos, A. Pranko, F. Prokoshin, F. Ptohos, G. Punzi, I. Redondo Fernández, P. Renton, M. Rescigno, F. Rimondi, L. Ristori, A. Robson, T. Rodriguez, S. Rolli, M. Ronzani, R. Roser, J. L. Rosner, F. Ruffini, A. Ruiz, J. Russ, V. Rusu, W. K. Sakumoto, Y. Sakurai, L. Santi, K. Sato, V. Saveliev, A. Savoy-Navarro, P. Schlabach, E. E. Schmidt, T. Schwarz, L. Scodellaro, F. Scuri, S. Seidel, Y. Seiya, A. Semenov, F. Sforza, S. Z. Shalhout, T. Shears, P. F. Shepard, M. Shimojima, M. Shochet, I. Shreyber-Tecker, A. Simonenko, K. Sliwa, J. R. Smith, F. D. Snider, H. Song, V. Sorin, R. St. Denis, M. Stancari, D. Stentz, J. Strologas, Y. Sudo, A. Sukhanov, I. Suslov, K. Takemasa, Y. Takeuchi, J. Tang, M. Tecchio, P. K. Teng, J. Thom, E. Thomson, V. Thukral, D. Toback, S. Tokar, K. Tollefson, T. Tomura, D. Tonelli, S. Torre, D. Torretta, P. Totaro,M. Trovato, F. Ukegawa, S. Uozumi, F. Vázquez, G. Velev, C. Vellidis, C. Vernieri, M. Vidal, R. Vilar, J. Vizán, M. Vogel, G. Volpi, P. Wagner, R. Wallny, S. M. Wang, D. Waters, W. C. Wester III, D. Whiteson, A. B. Wicklund, S. Wilbur, H. H. Williams, J. S. Wilson, P. Wilson, B. L. Winer, P. Wittich, S. Wolbers, H. Wolfe, T. Wright, X. Wu, Z. Wu, K. Yamamoto, D. Yamato, T. Yang, U. K. Yang, Y. C. Yang, W.-M. Yao, G. P. Yeh, K. Yi, J. Yoh, K. Yorita, T. Yoshida, G. B. Yu, I. Yu, A. M. Zanetti, Y. Zeng, C. Zhou, and S. Zucchelli

T. Aaltonen, S. Amerio, D. Amidei, A. Anastassov, A. Annovi, J. Antos, G. Apollinari, J. A. Appel, T. Arisawa, A. Artikov, J. Asaadi, W. Ashmanskas, B. Auerbach, A. Aurisano, F. Azfar, W. Badgett, T. Bae, A. Barbaro-Galtieri, V. E. Barnes, B. A. Barnett, P. Barria, P. Bartos, M. Bauce, F. Bedeschi, S. Behari, G. Bellettini, J. Bellinger, D. Benjamin, A. Beretvas, A. Bhatti, K. R. Bland, B. Blumenfeld, A. Bocci, A. Bodek, D. Bortoletto, J. Boudreau, A. Boveia, L. Brigliadori, C. Bromberg, E. Brucken, J. Budagov, H. S. Budd, K. Burkett, G. Busetto, P. Bussey, P. Butti, A. Buzatu, A. Calamba, S. Camarda, M. Campanelli, F. Canelli, B. Carls, D. Carlsmith, R. Carosi, S. Carrillo, B. Casal, M. Casarsa, A. Castro, P. Catastini, D. Cauz, V. Cavaliere, A. Cerri, L. Cerrito, Y. C. Chen, M. Chertok, G. Chiarelli, G. Chlachidze, K. Cho, D. Chokheli, A. Clark, C. Clarke, M. E. Convery, J. Conway, M. Corbo, M. Cordelli, C. A. Cox, D. J. Cox, M. Cremonesi, D. Cruz, J. Cuevas, R. Culbertson, N. d’Ascenzo, M. Datta, P. de Barbaro, L. Demortier, M. Deninno, M. D’Errico, F. Devoto, A. Di Canto, B. Di Ruzza, J. R. Dittmann, S. Donati, M. D’Onofrio, M. Dorigo, A. Driutti, K. Ebina, R. Edgar, A. Elagin, R. Erbacher, S. Errede, B. Esham, S. Farrington, J. P. Fernández Ramos, R. Field, G. Flanagan, R. Forrest, M. Franklin, J. C. Freeman, H. Frisch, Y. Funakoshi, C. Galloni, A. F. Garfinkel, P. Garosi, H. Gerberich, E. Gerchtein, S. Giagu, V. Giakoumopoulou, K. Gibson, C. M. Ginsburg, N. Giokaris, P. Giromini, V. Glagolev, D. Glenzinski, M. Gold, D. Goldin, A. Golossanov, G. Gomez, G. Gomez-Ceballos, M. Goncharov, O. González López, I. Gorelov, A. T. Goshaw, K. Goulianos, E. Gramellini, C. Grosso-Pilcher, R. C. Group, J. Guimaraes da Costa, S. R. Hahn, J. Y. Han, F. Happacher, K. Hara, M. Hare, R. F. Harr, T. Harrington-Taber, K. Hatakeyama, C. Hays, J. Heinrich, M. Herndon, A. Hocker, Z. Hong, W. Hopkins, S. Hou, R. E. Hughes, U. Husemann, M. Hussein, J. Huston, G. Introzzi, M. Iori, A. Ivanov, E. James, D. Jang, B. Jayatilaka, E. J. Jeon, S. Jindariani, M. Jones, K. K. Joo, S. Y. Jun, T. R. Junk, M. Kambeitz, T. Kamon, P. E. Karchin, A. Kasmi, Y. Kato, W. Ketchum, J. Keung, B. Kilminster, D. H. Kim, H. S. Kim, J. E. Kim, M. J. Kim, S. H. Kim, S. B. Kim, Y. J. Kim, Y. K. Kim, N. Kimura, M. Kirby, K. Knoepfel, K. Kondo, D. J. Kong, J. Konigsberg, A. V. Kotwal, M. Kreps, J. Kroll, M. Kruse, T. Kuhr, M. Kurata, A. T. Laasanen, S. Lammel, M. Lancaster, K. Lannon, G. Latino, H. S. Lee, J. S. Lee, S. Leo, S. Leone, J. D. Lewis, A. Limosani, E. Lipeles, A. Lister, H. Liu, Q. Liu, T. Liu, S. Lockwitz, A. Loginov, D. Lucchesi, A. Lucà, J. Lueck, P. Lujan, P. Lukens, G. Lungu, J. Lys, R. Lysak, R. Madrak, P. Maestro, S. Malik, G. Manca, A. Manousakis-Katsikakis, L. Marchese, F. Margaroli, P. Marino, K. Matera, M. E. Mattson, A. Mazzacane, P. Mazzanti, R. McNulty, A. Mehta, P. Mehtala, C. Mesropian, T. Miao, D. Mietlicki, A. Mitra, H. Miyake, S. Moed, N. Moggi, C. S. Moon, R. Moore, M. J. Morello, A. Mukherjee, Th. Muller, P. Murat, M. Mussini, J. Nachtman, Y. Nagai, J. Naganoma, I. Nakano, A. Napier, J. Nett, C. Neu, T. Nigmanov, L. Nodulman, S. Y. Noh, O. Norniella, L. Oakes, S. H. Oh, Y. D. Oh, I. Oksuzian, T. Okusawa, R. Orava, L. Ortolan, C. Pagliarone, E. Palencia, P. Palni, V. Papadimitriou, W. Parker, G. Pauletta, M. Paulini, C. Paus, T. J. Phillips, E. Pianori, J. Pilot, K. Pitts, C. Plager, L. Pondrom, S. Poprocki, K. Potamianos, A. Pranko, F. Prokoshin, F. Ptohos, G. Punzi, I. Redondo Fernández, P. Renton, M. Rescigno, F. Rimondi, L. Ristori, A. Robson, T. Rodriguez, S. Rolli, M. Ronzani, R. Roser, J. L. Rosner, F. Ruffini, A. Ruiz, J. Russ, V. Rusu, W. K. Sakumoto, Y. Sakurai, L. Santi, K. Sato, V. Saveliev, A. Savoy-Navarro, P. Schlabach, E. E. Schmidt, T. Schwarz, L. Scodellaro, F. Scuri, S. Seidel, Y. Seiya, A. Semenov, F. Sforza, S. Z. Shalhout, T. Shears, P. F. Shepard, M. Shimojima, M. Shochet, I. Shreyber-Tecker, A. Simonenko, K. Sliwa, J. R. Smith, F. D. Snider, H. Song, V. Sorin, R. St. Denis, M. Stancari, D. Stentz, J. Strologas, Y. Sudo, A. Sukhanov, I. Suslov, K. Takemasa, Y. Takeuchi, J. Tang, M. Tecchio, P. K. Teng, J. Thom, E. Thomson, V. Thukral, D. Toback, S. Tokar, K. Tollefson, T. Tomura, D. Tonelli, S. Torre, D. Torretta, P. Totaro, M. Trovato, F. Ukegawa, S. Uozumi, G. Velev, C. Vellidis, C. Vernieri, M. Vidal, R. Vilar, J. Vizán, M. Vogel, G. Volpi, F. Vázquez, P. Wagner, R. Wallny, S. M. Wang, D. Waters, W. C. Wester III, D. Whiteson, A. B. Wicklund, S. Wilbur, H. H. Williams, J. S. Wilson, P. Wilson, B. L. Winer, P. Wittich, S. Wolbers, H. Wolfe, T. Wright, X. Wu, Z. Wu, K. Yamamoto, D. Yamato, T. Yang, U. K. Yang, Y. C. Yang, W.-M. Yao, G. P. Yeh, K. Yi, J. Yoh, K. Yorita, T. Yoshida, G. B. Yu, I. Yu, A. M. Zanetti, Y. Zeng, C. Zhou, and S. Zucchelli

Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Mass and lifetime measurements of bottom and charm baryons in p ¯ p collisions at ffiffi s p ¼ 1.96 TeV We report on mass and lifetime measurements of several ground state charmed and bottom baryons, using a data sample corresponding to 9.6 fb −1 from p ¯ p collisions at ffiffi ffi s p ¼ 1.96 TeV and recorded with the Collider Detector at Fermilab. Baryon candidates are reconstructed from data collected with an online event selection designed for the collection of long-lifetime heavy-flavor decay products and a second event selection designed to collect J=ψ → μ þ μ − candidates. First evidence for the process Ω − b → Ω 0 c π − is presented with a significance of 3.3σ. We measure the following baryon masses:

A study of event-shape observables in proton-antiproton collisions at $\sqrt{s}=1.96\text{ }\text{ }\mathrm{TeV}$ is presented. The data for this analysis were recorded by the CDF II Detector at the Tevatron Collider. The variables studied are the transverse thrust and thrust minor, both defined in the plane perpendicular to the beam direction. The observables are measured using energies from unclustered calorimeter cells. In addition to studies of the differential distributions, we present the dependence of event-shape mean values on the leading-jet transverse energy. Data are compared with pythia Tune A and to resummed parton-level predictions that were matched to fixed-order results at next-to-leading-order (NLO) accuracy ($\mathrm{NLO}+\mathrm{NLL}$). Predictions from pythia Tune A agree fairly well with the data. However, the underlying event contributes significantly to these observables, making it difficult to make direct comparisons to the $\mathrm{NLO}+\mathrm{NLL}$ predictions, which do not account for the underlying event. To overcome this difficulty, we introduce a new observable, a weighted difference of the mean values of the thrust and thrust minor, which is less sensitive to the underlying event, allowing for a comparison with $\mathrm{NLO}+\mathrm{NLL}$. Both pythia Tune A and the $\mathrm{NLO}+\mathrm{NLL}$ calculations agree well within the $20%$ theoretical uncertainty with the data for this observable, indicating that perturbative QCD successfully describes shapes of the hadronic final states.

This Letter describes the current most precise measurement of the W boson pair production cross section and most sensitive test of anomalous WW gamma and WWZ couplings in p (p) over bar collisions at a center-of-mass energy of 1.96 TeV. The WW candidates are reconstructed from decays containing two charged leptons and two neutrinos. Using data collected by the CDF II detector from 3: 6 fb(-1) of integrated luminosity, a total of 654 candidate events are observed with an expected background of 320 +/- 47 events. The measured cross section is sigma(p (p) over bar -> W+W- +X) = 12.1 +/- 0.9(stat)(-1.4)(+1.6)(syst) pb, which is in good agreement with the standard model prediction. The same data sample is used to place constraints on anomalous WW gamma and WWZ couplings.