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13 results on '"Bürkle, A"'

1. Modeling of Surface Damage at the Si/SiO$_2$-interface of Irradiated MOS-capacitors

Author

Akchurin, N., Altopp, G., Burkle, B., Frey, W. D., Heintz, U., Hinton, N., Hoeferkamp, M., Kazhykarim, Y., Kuryatkov, V., Mengke, T., Peltola, T., Seidel, S., Spencer, E., Tripathi, M., and Voelker, J.

Subjects
Physics - Instrumentation and Detectors
Abstract

Surface damage caused by ionizing radiation in SiO$_2$ passivated silicon particle detectors consists mainly of the accumulation of a positively charged layer along with trapped-oxide-charge and interface traps inside the oxide and close to the Si/SiO$_2$-interface. High density positive interface net charge can be detrimental to the operation of a multi-channel $n$-on-$p$ sensor since the inversion layer generated under the Si/SiO$_2$-interface can cause loss of position resolution by creating a conduction channel between the electrodes. In the investigation of the radiation-induced accumulation of oxide charge and interface traps, a capacitance-voltage characterization study of n/$\gamma$- and $\gamma$-irradiated Metal-Oxide-Semiconductor (MOS) capacitors showed that close agreement between measurement and simulation were possible when oxide charge density was complemented by both acceptor- and donor-type deep interface traps with densities comparable to the oxide charges. Corresponding inter-strip resistance simulations of a $n$-on-$p$ sensor with the tuned oxide charge density and interface traps show close agreement with experimental results. The beneficial impact of radiation-induced accumulation of deep interface traps on inter-electrode isolation may be considered in the optimization of the processing parameters of isolation implants on $n$-on-$p$ sensors for the extreme radiation environments., Comment: Corresponding author: T. Peltola. 24 pages, 17 figures, 6 tables

Published
2023
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2. Response of a CMS HGCAL silicon-pad electromagnetic calorimeter prototype to 20-300 GeV positrons

Author

Acar, B., Adamov, G., Adloff, C., Afanasiev, S., Akchurin, N., Akgün, B., Khan, F. Alam, Alhusseini, M., Alison, J., Alpana, A., Altopp, G., Alyari, M., An, S., Anagul, S., Andreev, I., Aspell, P., Atakisi, I. O., Bach, O., Baden, A., Bakas, G., Bakshi, A., Bannerjee, S., Bargassa, P., Barney, D., Beaudette, F., Beaujean, F., Becheva, E., Becker, A., Behera, P., Belloni, A., Bergauer, T., Besancon, M., Bhattacharya, S., Bhowmik, D., Bilki, B., Bloch, P., Bodek, A., Bonanomi, M., Bonnemaison, A., Bonomally, S., Borg, J., Bouyjou, F., Bower, N., Braga, D., Brashear, J., Brondolin, E., Bryant, P., Perraguin, A. Buchot, Bueghly, J., Burkle, B., Butler-Nalin, A., Bychkova, O., Callier, S., Calvet, D., Cao, X., Cappati, A., Caraway, B., Caregari, S., Cauchois, A., Ceard, L., Cekmecelioglu, Y. C., Cerci, S., Cerminara, G., Chadeeva, M., Charitonidis, N., Chatterjee, R., Chen, Y. M., Chen, Z., Cheng, H. J., Cheng, K. y., Chernichenko, S., Cheung, H., Chien, C. H., Choudhury, S., Čoko, D., Collura, G., Couderc, F., Danilov, M., Dannheim, D., Daoud, W., Dauncey, P., David, A., Davies, G., Davignon, O., Day, E., DeBarbaro, P., De Guio, F., de La Taille, C., De Silva, M., Debbins, P., Defranchis, M. M., Delagnes, E., Berrio, J. M. Deltoro, Derylo, G., de Almeida, P. G. Dias, Diaz, D., Dinaucourt, P., Dittmann, J., Dragicevic, M., Dugad, S., Dulucq, F., Dumanoglu, I., Dutta, V., Dutta, S., Dünser, M., Eckdahl, J., Edberg, T. K., Berni, M. El, Elias, F., Eno, S. C., Ershov, Yu., Everaerts, P., Extier, S., Fahim, F., Fallon, C., Fedi, G., Alves, B. A. Fontana Santos, Frahm, E., Franzoni, G., Freeman, J., French, T., Gandhi, P., Ganjour, S., Gao, X., Garcia-Bellido, A., Gastaldi, F., Gecse, Z., Geerebaert, Y., Gerwig, H., Gevin, O., Ghosh, S., Gilbert, A., Gilbert, W., Gill, K., Gingu, C., Gninenko, S., Golunov, A., Golutvin, I., Gonzalez, T., Gorbounov, N., Gouskos, L., Gray, A. B., Gu, Y., Guilloux, F., Guler, Y., Gülmez, E., Guo, J., Guler, E. Gurpinar, Hammer, M., Hassanshahi, H. M., Hatakeyama, K., Heering, A., Hegde, V., Heintz, U., Hinton, N., Hirschauer, J., Hoff, J., Hou, W. -S., Hou, X., Hua, H., Incandela, J., Irshad, A., Isik, C., Jain, S., Jheng, H. R., Joshi, U., Kachanov, V., Kalinin, A., Kalipoliti, L., Kaminskiy, A., Kapoor, A., Kara, O., Karneyeu, A., Kaya, M., Kaya, O., Topaksu, A. Kayis, Khukhunaishvili, A., Kiesler, J., Kilpatrick, M., Kim, S., Koetz, K., Kolberg, T., Köseyan, O. K., Kristić, A., Krohn, M., Krüger, K., Kulagin, N., Kulis, S., Kunori, S., Kuo, C. M., Kuryatkov, V., Kyre, S., Lai, Y., Lamichhane, K., Landsberg, G., Lange, C., Langford, J., Lee, M. Y., Levin, A., Li, A., Li, B., Li, J. H., Li, Y. y., Liao, H., Lincoln, D., Linssen, L., Lipton, R., Liu, Y., Lobanov, A., Lu, R. -S., Lupi, M., Lysova, I., Magnan, A. -M., Magniette, F., Mahjoub, A., Maier, A. A., Malakhov, A., Mallios, S., Mandjavize, I., Mannelli, M., Mans, J., Marchioro, A., Martelli, A., Martinez, G., Masterson, P., Meng, B., Mengke, T., Mestvirishvili, A., Mirza, I., Moccia, S., Mohanty, G. B., Monti, F., Morrissey, I., Murthy, S., Musić, J., Musienko, Y., Nabili, S., Nagar, A., Nguyen, M., Nikitenko, A., Noonan, D., Noy, M., Nurdan, K., Ochando, C., Odegard, B., Odell, N., Okawa, H., Onel, Y., Ortez, W., Ozegović, J., Ozkorucuklu, S., Paganis, E., Pagenkopf, D., Palladino, V., Pandey, S., Pantaleo, F., Papageorgakis, C., Papakrivopoulos, I., Parshook, J., Pastika, N., Paulini, M., Paulitsch, P., Peltola, T., Gomes, R. Pereira, Perkins, H., Petiot, P., Pierre-Emile, T., Pitters, F., Popova, E., Prosper, H., Prvan, M., Puljak, I., Qu, H., Quast, T., Quinn, R., Quinnan, M., Garcia, M. T. Ramos, Rao, K. K., Rapacz, K., Raux, L., Reichenbach, G., Reinecke, M., Revering, M., Roberts, A., Romanteau, T., Rose, A., Rovere, M., Roy, A., Rubinov, P., Rusack, R., Rusinov, V., Ryjov, V., Sahin, O. M., Salerno, R., Rodriguez, A. M. Sanchez, Saradhy, R., Sarkar, T., Sarkisla, M. A., Sauvan, J. B., Schmidt, I., Schmitt, M., Scott, E., Seez, C., Sefkow, F., Sharma, S., Shein, I., Shenai, A., Shukla, R., Sicking, E., Sieberer, P., Silva, P., Simsek, A. E., Sirois, Y., Smirnov, V., Sozbilir, U., Spencer, E., Steen, A., Strait, J., Strobbe, N., Su, J. W., Sukhov, E., Sun, L., Cerci, D. Sunar, Syal, C., Tali, B., Tan, C. L., Tao, J., Tastan, I., Tatli, T., Thaus, R., Tekten, S., Thienpont, D., Tiras, E., Titov, M., Tlisov, D., Tok, U. G., Troska, J., Tsai, L. -S., Tsamalaidze, Z., Tsipolitis, G., Tsirou, A., Tyurin, N., Undleeb, S., Urbanski, D., Ustinov, V., Uzunian, A., Van de Klundert, M., Varela, J., Velasco, M., Viazlo, O., Pinto, M. Vicente Barreto, Virdee, P. Vichoudis T., de Oliveira, R. Vizinho, Voelker, J., Voirin, E., Vojinovic, M., Wade, A., Wang, C., Wang, F., Wang, X., Wang, Z., Wayne, M., Webb, S. N., Whitbeck, A., White, D., Wickwire, R., Wilson, J. S., Winter, D., Wu, H. y., Wu, L., Nursanto, M. Wulansatiti, Yeh, C. H, Yohay, R., Yu, D., Yu, G. B., Yu, S. S., Yuan, C., Yumiceva, F., Yusuff, I., Zacharopoulou, A., Zamiatin, N., Zarubin, A., Zenz, S., Zghiche, A., Zhang, H., Zhang, J., Zhang, Y., and Zhang, Z.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

The Compact Muon Solenoid Collaboration is designing a new high-granularity endcap calorimeter, HGCAL, to be installed later this decade. As part of this development work, a prototype system was built, with an electromagnetic section consisting of 14 double-sided structures, providing 28 sampling layers. Each sampling layer has an hexagonal module, where a multipad large-area silicon sensor is glued between an electronics circuit board and a metal baseplate. The sensor pads of approximately 1 cm$^2$ are wire-bonded to the circuit board and are readout by custom integrated circuits. The prototype was extensively tested with beams at CERN's Super Proton Synchrotron in 2018. Based on the data collected with beams of positrons, with energies ranging from 20 to 300 GeV, measurements of the energy resolution and linearity, the position and angular resolutions, and the shower shapes are presented and compared to a detailed Geant4 simulation.

Published
2021
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3. End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data

Author

Andrews, Michael, Burkle, Bjorn, Chen, Yi-fan, DiCroce, Davide, Gleyzer, Sergei, Heintz, Ulrich, Narain, Meenakshi, Paulini, Manfred, Pervan, Nikolas, Shafi, Yusef, Sun, Wei, Usai, Emanuele, and Yang, Kun

Subjects
Physics - Data Analysis, Statistics and Probability, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, High Energy Physics - Experiment
Abstract

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. In this study, we use low-level detector information from the simulated CMS Open Data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves an AUC score of 0.975$\pm$0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier AUC score increases to 0.9824$\pm$0.0013, serving as the first performance benchmark for these CMS Open Data samples. We additionally provide a timing performance comparison of different processor unit architectures for training the network., Comment: 9 pages, 3 figures, 4 tables; v3: unpublished

Published
2021
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4. Construction and commissioning of CMS CE prototype silicon modules

Author

Acar, B., Adamov, G., Adloff, C., Afanasiev, S., Akchurin, N., Akgün, B., Alhusseini, M., Alison, J., Altopp, G., Alyari, M., An, S., Anagul, S., Andreev, I., Andrews, M., Aspell, P., Atakisi, I. A., Bach, O., Baden, A., Bakas, G., Bakshi, A., Bargassa, P., Barney, D., Becheva, E., Behera, P., Belloni, A., Bergauer, T., Besancon, M., Bhattacharya, S., Bhowmik, D., Bloch, P., Bodek, A., Bonanomi, M., Bonnemaison, A., Bonomally, S., Borg, J., Bouyjou, F., Braga, D., Brashear, J., Brondolin, E., Bryant, P., Bueghly, J., Bilki, B., Burkle, B., Butler-Nalin, A., Callier, S., Calvet, D., Cao, X., Caraway, B., Caregari, S., Ceard, L., Cekmecelioglu, Y. C., Cerminara, G., Charitonidis, N., Chatterjee, R., Chen, Y. M., Chen, Z., Cheng, K. y., Chernichenko, S., Cheung, H., Chien, C. H., Choudhury, S., Čoko, D., Collura, G., Couderc, F., Dumanoglu, I., Dannheim, D., Dauncey, P., David, A., Davies, G., Day, E., DeBarbaro, P., De Guio, F., de La Taille, C., De Silva, M., Debbins, P., Delagnes, E., Deltoro, J. M., Derylo, G., de Almeida, P. G. Dias, Diaz, D., Dinaucourt, P., Dittmann, J., Dragicevic, M., Dugad, S., Dutta, V., Dutta, S., Eckdahl, J., Edberg, T. K., Berni, M. El, Eno, S. C., Ershov, Yu., Everaerts, P., Extier, S., Fahim, F., Fallon, C., Alves, B. A. Fontana Santos, Frahm, E., Franzoni, G., Freeman, J., French, T., Guler, E. Gurpinar, Guler, Y., Gagnan, M., Gandhi, P., Ganjour, S., Garcia-Bellido, A., Gecse, Z., Geerebaert, Y., Gerwig, H., Gevin, O., Gilbert, W., Gilbert, A., Gill, K., Gingu, C., Gninenko, S., Golunov, A., Golutvin, I., Gonzalez, T., Gorbounov, N., Gouskos, L., Gu, Y., Guilloux, F., Gülmez, E., Hammer, M., Harilal, A., Hatakeyama, K., Heering, A., Hegde, V., Heintz, U., Hinger, V., Hinton, N., Hirschauer, J., Hoff, J., Hou, W. S., Isik, C., Incandela, J., Jain, S., Jheng, H. R., Joshi, U., Kara, O., Kachanov, V., Kalinin, A., Kameshwar, R., Kaminskiy, A., Karneyeu, A., Kaya, O., Kaya, M., Khukhunaishvili, A., Kim, S., Koetz, K., Kolberg, T., Kristić, A., Krohn, M., Krüger, K., Kulagin, N., Kulis, S., Kunori, S., Kuo, C. M., Kuryatkov, V., Kyre, S., Köseyan, O. K., Lai, Y., Lamichhane, K., Landsberg, G., Langford, J., Lee, M. Y., Levin, A., Li, A., Li, B., Li, J. -H., Liao, H., Lincoln, D., Linssen, L., Lipton, R., Liu, Y., Lobanov, A., Lu, R. S., Lysova, I., Magnan, A. M., Magniette, F., Maier, A. A., Malakhov, A., Mandjavize, I., Mannelli, M., Mans, J., Marchioro, A., Martelli, A., Masterson, P., Meng, B., Mengke, T., Mestvirishvili, A., Mirza, I., Moccia, S., Morrissey, I., Mudholkar, T., Musić, J., Musienko, I., Nabili, S., Nagar, A., Nikitenko, A., Noonan, D., Noy, M., Nurdan, K., Ochando, C., Odegard, B., Odell, N., Onel, Y., Ortez, W., Ozegović, J., Rodriguez, L. Pacheco, Paganis, E., Pagenkopf, D., Palladino, V., Pandey, S., Pantaleo, F., Papageorgakis, C., Papakrivopoulos, I., Parshook, J., Pastika, N., Paulini, M., Paulitsch, P., Peltola, T., Gomes, R. Pereira, Perkins, H., Petiot, P., Pitters, F., Prosper, H., Prvan, M., Puljak, I., Quast, T., Quinn, R., Quinnan, M., Rapacz, K., Raux, L., Reichenbach, G., Reinecke, M., Revering, M., Rodriguez, A., Romanteau, T., Rose, A., Rovere, M., Roy, A., Rubinov, P., Rusack, R., Simsek, A. E., Sozbilir, U., Sahin, O. M., Sanchez, A., Saradhy, R., Sarkar, T., Sarkisla, M. A., Sauvan, J. B., Schmidt, I., Schmitt, M., Scott, E., Seez, C., Sefkow, F., Sharma, S., Shein, I., Shenai, A., Shukla, R., Sicking, E., Sieberer, P., Sirois, Y., Smirnov, V., Spencer, E., Steen, A., Strait, J., Strebler, T., Strobbe, N., Su, J. W., Sukhov, E., Sun, L., Sun, M., Syal, C., Tali, B., Tok, U. G., Topaksu, A. Kayis, Tan, C. L., Tastan, I., Tatli, T., Thaus, R., Tekten, S., Thienpont, D., Pierre-Emile, T., Tiras, E., Titov, M., Tlisov, D., Troska, J., Tsamalaidze, Z., Tsipolitis, G., Tsirou, A., Tyurin, N., Undleeb, S., Urbanski, D., Ustinov, V., Uzunian, A., van de Klundert, M., Varela, J., Velasco, M., Pinto, M. Vicente Barreto, da Silva, P. M., Virdee, T., de Oliveira, R. Vizinho, Voelker, J., Voirin, E., Wang, Z., Wang, X., Wang, F., Wayne, M., Webb, S. N., Weinberg, M., Whitbeck, A., White, D., Wickwire, R., Wilson, J. S., Wu, H. Y., Wu, L., Yeh, C. H, Yohay, R., Yu, G. B., Yu, S. S., Yu, D., Yumiceva, F., Zacharopoulou, A., Zamiatin, N., Zarubin, A., Zenz, S., Zhang, H., and Zhang, J.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic (CE-E) and hadronic (CE-H) compartments. The calorimeter will be built with $\sim$30,000 hexagonal silicon modules. Prototype modules have been constructed with 6-inch hexagonal silicon sensors with cell areas of 1.1~$cm^2$, and the SKIROC2-CMS readout ASIC. Beam tests of different sampling configurations were conducted with the prototype modules at DESY and CERN in 2017 and 2018. This paper describes the construction and commissioning of the CE calorimeter prototype, the silicon modules used in the construction, their basic performance, and the methods used for their calibration., Comment: 35 pages, submitted to JINST

Published
2020

5. The DAQ system of the 12,000 Channel CMS High Granularity Calorimeter Prototype

Author

Acar, B., Adamov, G., Adloff, C., Afanasiev, S., Akchurin, N., Akgün, B., Alhusseini, M., Alison, J., Altopp, G., Alyari, M., An, S., Anagul, S., Andreev, I., Andrews, M., Aspell, P., Atakisi, I. A., Bach, O., Baden, A., Bakas, G., Bakshi, A., Bargassa, P., Barney, D., Becheva, E., Behera, P., Belloni, A., Bergauer, T., Besancon, M., Bhattacharya, S., Bhowmik, D., Bloch, P., Bodek, A., Bonanomi, M., Bonnemaison, A., Bonomally, S., Borg, J., Bouyjou, F., Braga, D., Brashear, J., Brondolin, E., Bryant, P., Bueghly, J., Bilki, B., Burkle, B., Butler-Nalin, A., Callier, S., Calvet, D., Cao, X., Caraway, B., Caregari, S., Ceard, L., Cekmecelioglu, Y. C., Cerminara, G., Charitonidis, N., Chatterjee, R., Chen, Y. M., Chen, Z., Cheng, K. y., Chernichenko, S., Cheung, H., Chien, C. H., Choudhury, S., Čoko, D., Collura, G., Couderc, F., Dumanoglu, I., Dannheim, D., Dauncey, P., David, A., Davies, G., Day, E., DeBarbaro, P., De Guio, F., de La Taille, C., De Silva, M., Debbins, P., Delagnes, E., Deltoro, J. M., Derylo, G., de Almeida, P. G. Dias, Diaz, D., Dinaucourt, P., Dittmann, J., Dragicevic, M., Dugad, S., Dutta, V., Dutta, S., Eckdahl, J., Edberg, T. K., Berni, M. El, Eno, S. C., Ershov, Yu., Everaerts, P., Extier, S., Fahim, F., Fallon, C., Alves, B. A. Fontana Santos, Frahm, E., Franzoni, G., Freeman, J., French, T., Guler, E. Gurpinar, Guler, Y., Gagnan, M., Gandhi, P., Ganjour, S., Garcia-Bellido, A., Gecse, Z., Geerebaert, Y., Gerwig, H., Gevin, O., Gilbert, W., Gilbert, A., Gill, K., Gingu, C., Gninenko, S., Golunov, A., Golutvin, I., Gonzalez, T., Gorbounov, N., Gouskos, L., Gu, Y., Guilloux, F., Gülmez, E., Hammer, M., Harilal, A., Hatakeyama, K., Heering, A., Hegde, V., Heintz, U., Hinger, V., Hinton, N., Hirschauer, J., Hoff, J., Hou, W. S., Isik, C., Incandela, J., Jain, S., Jheng, H. R., Joshi, U., Kara, O., Kachanov, V., Kalinin, A., Kameshwar, R., Kaminskiy, A., Karneyeu, A., Kaya, O., Kaya, M., Khukhunaishvili, A., Kim, S., Koetz, K., Kolberg, T., Kristić, A., Krohn, M., Krüger, K., Kulagin, N., Kulis, S., Kunori, S., Kuo, C. M., Kuryatkov, V., Kyre, S., Köseyan, O. K., Lai, Y., Lamichhane, K., Landsberg, G., Langford, J., Lee, M. Y., Levin, A., Li, A., Li, B., Li, J. -H., Liao, H., Lincoln, D., Linssen, L., Lipton, R., Liu, Y., Lobanov, A., Lu, R. S., Lysova, I., Magnan, A. M., Magniette, F., Maier, A. A., Malakhov, A., Mandjavize, I., Mannelli, M., Mans, J., Marchioro, A., Martelli, A., Masterson, P., Meng, B., Mengke, T., Mestvirishvili, A., Mirza, I., Moccia, S., Morrissey, I., Mudholkar, T., Musić, J., Musienko, I., Nabili, S., Nagar, A., Nikitenko, A., Noonan, D., Noy, M., Nurdan, K., Ochando, C., Odegard, B., Odell, N., Onel, Y., Ortez, W., Ozegović, J., Rodriguez, L. Pacheco, Paganis, E., Pagenkopf, D., Palladino, V., Pandey, S., Pantaleo, F., Papageorgakis, C., Papakrivopoulos, I., Parshook, J., Pastika, N., Paulini, M., Paulitsch, P., Peltola, T., Gomes, R. Pereira, Perkins, H., Petiot, P., Pitters, F., Prosper, H., Prvan, M., Puljak, I., Quast, T., Quinn, R., Quinnan, M., Rapacz, K., Raux, L., Reichenbach, G., Reinecke, M., Revering, M., Rodriguez, A., Romanteau, T., Rose, A., Rovere, M., Roy, A., Rubinov, P., Rusack, R., Simsek, A. E., Sozbilir, U., Sahin, O. M., Sanchez, A., Saradhy, R., Sarkar, T., Sarkisla, M. A., Sauvan, J. B., Schmidt, I., Schmitt, M., Scott, E., Seez, C., Sefkow, F., Sharma, S., Shein, I., Shenai, A., Shukla, R., Sicking, E., Sieberer, P., Sirois, Y., Smirnov, V., Spencer, E., Steen, A., Strait, J., Strebler, T., Strobbe, N., Su, J. W., Sukhov, E., Sun, L., Sun, M., Syal, C., Tali, B., Tok, U. G., Topaksu, A. Kayis, Tan, C. L., Tastan, I., Tatli, T., Thaus, R., Tekten, S., Thienpont, D., Pierre-Emile, T., Tiras, E., Titov, M., Tlisov, D., Troska, J., Tsamalaidze, Z., Tsipolitis, G., Tsirou, A., Tyurin, N., Undleeb, S., Urbanski, D., Ustinov, V., Uzunian, A., van de Klundert, M., Varela, J., Velasco, M., Pinto, M. Vicente Barreto, da Silva, P. M., Virdee, T., de Oliveira, R. Vizinho, Voelker, J., Voirin, E., Wang, Z., Wang, X., Wang, F., Wayne, M., Webb, S. N., Weinberg, M., Whitbeck, A., White, D., Wickwire, R., Wilson, J. S., Wu, H. Y., Wu, L., Yeh, C. H, Yohay, R., Yu, G. B., Yu, S. S., Yu, D., Yumiceva, F., Zacharopoulou, A., Zamiatin, N., Zarubin, A., Zenz, S., Zhang, H., and Zhang, J.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. One part of this upgrade is the replacement of the current endcap calorimeters with a high granularity sampling calorimeter equipped with silicon sensors, designed to manage the high collision rates. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors. In the most recent of these tests, conducted in late 2018 at the CERN SPS, the performance of a prototype calorimeter equipped with ${\approx}12,000\rm{~channels}$ of silicon sensors was studied with beams of high-energy electrons, pions and muons. This paper describes the custom-built scalable data acquisition system that was built with readily available FPGA mezzanines and low-cost Raspberry PI computers.

Published
2020
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6. Charge Collection and Electrical Characterization of Neutron Irradiated Silicon Pad Detectors for the CMS High Granularity Calorimeter

Author

Akchurin, N., Almeida, P., Altopp, G., Alyari, M., Bergauer, T., Brondolin, E., Burkle, B., Frey, W. D., Gecse, Z., Heintz, U., Hinton, N., Kuryatkov, V., Lipton, R., Mannelli, M., Mengke, T., Paulitsch, P., Peltola, T., Pitters, F., Sicking, E., Spencer, E., Tripathi, M., Pinto, M. Vicente Barreto, Voelker, J., Wang, Z., and Yohay, R.

Subjects
Physics - Instrumentation and Detectors
Abstract

The replacement of the existing endcap calorimeter in the Compact Muon Solenoid (CMS) detector for the high-luminosity LHC (HL-LHC), scheduled for 2027, will be a high granularity calorimeter. It will provide detailed position, energy, and timing information on electromagnetic and hadronic showers in the immense pileup of the HL-LHC. The High Granularity Calorimeter (HGCAL) will use 120-, 200-, and 300-$\mu\textrm{m}$ thick silicon (Si) pad sensors as the main active material and will sustain 1-MeV neutron equivalent fluences up to about $10^{16}~\textrm{n}_\textrm{eq}\textrm{cm}^{-2}$. In order to address the performance degradation of the Si detectors caused by the intense radiation environment, irradiation campaigns of test diode samples from 8-inch and 6-inch wafers were performed in two reactors. Characterization of the electrical and charge collection properties after irradiation involved both bulk polarities for the three sensor thicknesses. Since the Si sensors will be operated at -30 $^\circ$C to reduce increasing bulk leakage current with fluence, the charge collection investigation of 30 irradiated samples was carried out with the infrared-TCT setup at -30 $^\circ$C. TCAD simulation results at the lower fluences are in close agreement with the experimental results and provide predictions of sensor performance for the lower fluence regions not covered by the experimental study. All investigated sensors display 60$\%$ or higher charge collection efficiency at their respective highest lifetime fluences when operated at 800 V, and display above 90$\%$ at the lowest fluence, at 600 V. The collected charge close to the fluence of $10^{16}~\textrm{n}_\textrm{eq}\textrm{cm}^{-2}$ exceeds 1 fC at voltages beyond 800 V., Comment: 36 pages, 34 figures

Published
2020
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7. End-to-end particle and event identification at the Large Hadron Collider with CMS Open Data

Author

Alison, John, An, Sitong, Andrews, Michael, Bryant, Patrick, Burkle, Bjorn, Gleyzer, Sergei, Heintz, Ulrich, Narain, Meenakshi, Paulini, Manfred, Poczos, Barnabas, and Usai, Emanuele

Subjects
High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

From particle identification to the discovery of the Higgs boson, deep learning algorithms have become an increasingly important tool for data analysis at the Large Hadron Collider (LHC). We present an innovative end-to-end deep learning approach for jet identification at the Compact Muon Solenoid (CMS) experiment at the LHC. The method combines deep neural networks with low-level detector information, such as calorimeter energy deposits and tracking information, to build a discriminator to identify different particle species. Using two physics examples as references: electron vs. photon discrimination and quark vs. gluon discrimination, we demonstrate the performance of the end-to-end approach on simulated events with full detector geometry as available in the CMS Open Data. We also offer insights into the importance of the information extracted from various sub-detectors and describe how end-to-end techniques can be extended to event-level classification using information from the whole CMS detector., Comment: Talk presented at the 2019 Meeting of the Division of Particles and Fields of the American Physical Society (DPF2019), July 29 - August 2, 2019, Northeastern University, Boston, C1907293

Published
2019

8. End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data

Author

Andrews, Michael, Alison, John, An, Sitong, Bryant, Patrick, Burkle, Bjorn, Gleyzer, Sergei, Narain, Meenakshi, Paulini, Manfred, Poczos, Barnabas, and Usai, Emanuele

Subjects
High Energy Physics - Experiment, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Data Analysis, Statistics and Probability
Abstract

We describe the construction of end-to-end jet image classifiers based on simulated low-level detector data to discriminate quark- vs. gluon-initiated jets with high-fidelity simulated CMS Open Data. We highlight the importance of precise spatial information and demonstrate competitive performance to existing state-of-the-art jet classifiers. We further generalize the end-to-end approach to event-level classification of quark vs. gluon di-jet QCD events. We compare the fully end-to-end approach to using hand-engineered features and demonstrate that the end-to-end algorithm is robust against the effects of underlying event and pile-up., Comment: 10 pages, 5 figures, 7 tables; v2: published version

Published
2019
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9. Machine Learning in High Energy Physics Community White Paper

Author

Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Anderson, John, Andrews, Michael, Espinosa, Juan Pedro Araque, Aurisano, Adam, Basara, Laurent, Bevan, Adrian, Bhimji, Wahid, Bonacorsi, Daniele, Burkle, Bjorn, Calafiura, Paolo, Campanelli, Mario, Capps, Louis, Carminati, Federico, Carrazza, Stefano, Chen, Yi-fan, Childers, Taylor, Coadou, Yann, Coniavitis, Elias, Cranmer, Kyle, David, Claire, Davis, Douglas, De Simone, Andrea, Duarte, Javier, Erdmann, Martin, Eschle, Jonas, Farbin, Amir, Feickert, Matthew, Castro, Nuno Filipe, Fitzpatrick, Conor, Floris, Michele, Forti, Alessandra, Garra-Tico, Jordi, Gemmler, Jochen, Girone, Maria, Glaysher, Paul, Gleyzer, Sergei, Gligorov, Vladimir, Golling, Tobias, Graw, Jonas, Gray, Lindsey, Greenwood, Dick, Hacker, Thomas, Harvey, John, Hegner, Benedikt, Heinrich, Lukas, Heintz, Ulrich, Hooberman, Ben, Junggeburth, Johannes, Kagan, Michael, Kane, Meghan, Kanishchev, Konstantin, Karpiński, Przemysław, Kassabov, Zahari, Kaul, Gaurav, Kcira, Dorian, Keck, Thomas, Klimentov, Alexei, Kowalkowski, Jim, Kreczko, Luke, Kurepin, Alexander, Kutschke, Rob, Kuznetsov, Valentin, Köhler, Nicolas, Lakomov, Igor, Lannon, Kevin, Lassnig, Mario, Limosani, Antonio, Louppe, Gilles, Mangu, Aashrita, Mato, Pere, Meenakshi, Narain, Meinhard, Helge, Menasce, Dario, Moneta, Lorenzo, Moortgat, Seth, Neubauer, Mark, Newman, Harvey, Otten, Sydney, Pabst, Hans, Paganini, Michela, Paulini, Manfred, Perdue, Gabriel, Perez, Uzziel, Picazio, Attilio, Pivarski, Jim, Prosper, Harrison, Psihas, Fernanda, Radovic, Alexander, Reece, Ryan, Rinkevicius, Aurelius, Rodrigues, Eduardo, Rorie, Jamal, Rousseau, David, Sauers, Aaron, Schramm, Steven, Schwartzman, Ariel, Severini, Horst, Seyfert, Paul, Siroky, Filip, Skazytkin, Konstantin, Sokoloff, Mike, Stewart, Graeme, Stienen, Bob, Stockdale, Ian, Strong, Giles, Sun, Wei, Thais, Savannah, Tomko, Karen, Upfal, Eli, Usai, Emanuele, Ustyuzhanin, Andrey, Vala, Martin, Vasel, Justin, Vallecorsa, Sofia, Verzetti, Mauro, Vilasís-Cardona, Xavier, Vlimant, Jean-Roch, Vukotic, Ilija, Wang, Sean-Jiun, Watts, Gordon, Williams, Michael, Wu, Wenjing, Wunsch, Stefan, Yang, Kun, and Zapata, Omar

Subjects
Physics - Computational Physics, Computer Science - Machine Learning, High Energy Physics - Experiment, Statistics - Machine Learning
Abstract

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit., Comment: Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm

Published
2018

10. Revealing biases in the sampling of ecological interaction networks

Author

de Aguiar, Marcus A. M., Newman, Erica A., Pires, Mathias M., Yeakel, Justin D., Hembry, David H., Burkle, Laura, Gravel, Dominique, Guimaraes Jr, Paulo R., O'Donnell, Jimmy, Poisot, Timothee, and Fortin, Marie-Josee

Subjects
Quantitative Biology - Populations and Evolution
Abstract

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases in both the interactors (the nodes of the network) and interactions (the links between nodes). These issues may affect the accuracy of empirically constructed ecological networks. We explore the properties of sampled ecological networks by simulating large-scale ecological networks with predetermined topologies, and sampling them with different mathematical procedures. Several types of modular networks were generated, intended to represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different sampling designs that may be encountered in field experiments. The observed networks generated by each sampling process were analyzed with respect to number and size of components. We show that the sampling effort needed to estimate underlying network properties depends both on the sampling design and on network topology. Networks with random or scale-free modules require more complete sampling compared to networks whose modules are nested or bipartite. Overall, the structure of nested modules was the easiest to detect, regardless of sampling design. Sampling according to species degree was consistently found to be the most accurate strategy to estimate network structure. Conversely, sampling according to module results in an accurate view of certain modules, but fails to provide a global picture of the underlying network. We recommend that these findings are incorporated into the design of projects aiming to characterize large networks of species interactions in the field, to reduce sampling biases. The software scripts developed to construct and sample networks are provided for further explorations of network structure and comparisons to real interaction data., Comment: 35 pages, 4 figures

Published
2017
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11. Reaction Mechanisms in Petroleum: From Experimentation to Upgrading and Geological Conditions

Author

Lannuzel, Frédéric, Burklé-Vitzthum, Valérie, Bounaceur, Roda, Marquaire, Paul-Marie, and Michels, Raymond

Subjects
Physics - Chemical Physics
Abstract

Among the numerous questions that arise concerning the exploitation of petroleum from unconventional reservoirs, lie the questions of the composition of hydrocarbons present in deep seated HP-HT reservoirs or produced during in-situ upgrading steps of heavy oils and oil shales. Our research shows that experimental hydrocarbon cracking results obtained in the laboratory cannot be extrapolated to geological reservoir conditions in a simple manner. Our demonstration is based on two examples: 1) the role of the hydrocarbon mixture composition on reaction kinetics (the "mixing effect") and the effects of pressure (both in relationship to temperature and time). The extrapolation of experimental data to geological conditions requires investigation of the free-radical reaction mechanisms through a computed kinetic model. We propose a model that takes into account 52 reactants as of today, and which can be continuously improved by addition of new reactants as research proceeds. This model is complete and detailed enough to be simulated in large ranges of temperature (150-500\degree C) and pressures (1-1500 bar). It is thus adapted to predict the hydrocarbons evolution from upgrading conditions to geological reservoirs., Comment: 8th World Congress of Chemical Engineering, Montr\'eal : Canada (2009)

Published
2009

12. Cluster-based density-functional approach to quantum transport through molecular and atomic contacts

Author

Pauly, F., Viljas, J. K., Huniar, U., Hafner, M., Wohlthat, S., Burkle, M., Cuevas, J. C., and Schon, G.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract

We present a cluster-based density-functional approach to model charge transport through molecular and atomic contacts. The electronic structure of the contacts is determined in the framework of density functional theory, and the parameters needed to describe transport are extracted from finite clusters. A similar procedure, restricted to nearest-neighbor interactions in the electrodes, has been presented by Damle et al. [Chem. Phys. 281, 171 (2002)]. Here, we show how to systematically improve the description of the electrodes by extracting bulk parameters from sufficiently large metal clusters. In this way we avoid problems arising from the use of nonorthogonal basis functions. For demonstration we apply our method to electron transport through Au contacts with various atomic-chain configurations and to a single-atom contact of Al., Comment: 18 pages, 13 figures

Published
2008
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