Author: "Hazra A" - Searchworks@Jio Institute Digital Library Search Results

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26,671 results on '"Hazra A"'

51. Search for a $\mu^+\mu^-$ resonance in four-muon final states at Belle II

Author: Belle II Collaboration, Adachi, I., Adamczyk, K., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bilka, T., Bilokin, S., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bozek, A., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Cremaldi, L. M., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dorigo, M., Dort, K., Dreyer, S., Dubey, S., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Gudkova, K., Halder, S., Han, Y., Hara, T., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inguglia, G., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Joo, K. K., Junkerkalefeld, H., Kalita, D., Kandra, J., Kang, K. H., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Lautenbach, K., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, Y., Li, Y. B., Libby, J., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Milesi, M., Miller, C., Mirra, M., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Oskin, P., Otani, F., Pakhlova, G., Panta, A., Pardi, S., Parham, K., Park, H., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schwanda, C., Schwickardi, M., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Sutcliffe, W., Svidras, H., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yusa, Y., Zani, L., Zhang, B., Zhilich, V., Zhou, Q. D., Zhou, X. Y., and Zhukova, V. I.
Subjects: High Energy Physics - Experiment
Abstract: We report on a search for a resonance $X$ decaying to a pair of muons in $e^{+}e^{-}\rightarrow \mu^+ \mu^- X$ events in the 0.212-9.000 GeV/$c^{2}$ mass range, using 178 fb$^{-1}$ of data collected by the BelleII experiment at the SuperKEKB collider at a center of mass energy of 10.58 GeV. The analysis probes two different models of $X$ beyond the standard model: a $Z^{\prime}$ vector boson in the $L_{\mu}-L_{\tau}$ model and a muonphilic scalar. We observe no evidence for a signal and set exclusion limits at the 90$\%$ confidence level on the products of cross section and branching fraction for these processes, ranging from 0.046 fb to 0.97 fb for the $L_{\mu}-L_{\tau}$ model and from 0.055 fb to 1.3 fb for the muonphilic scalar model. For masses below 6 GeV/$c^{2}$, the corresponding constraints on the couplings of these processes to the standard model range from 0.0008 to 0.039 for the $L_{\mu}-L_{\tau}$ model and from 0.0018 to 0.040 for the muonphilic scalar model. These are the first constraints on the muonphilic scalar from a dedicated search.
Published: 2024
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52. Measurement of $CP$ asymmetries in $B^0 \rightarrow K^0_S K^0_S K^0_S$ decays at Belle II

Author: Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Bauer, M., Baur, A., Beaubien, A., Becherer, F., Becker, J., Behera, P. K., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Chekelian, V., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cremaldi, L. M., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., De Yta-Hernandez, A., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Garmash, A., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Granderath, S., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Halder, S., Han, Y., Hara, K., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Hershenhorn, A., Higuchi, T., Hill, E. C., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Ito, S., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kakuno, H., Kaleta, M., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Konno, T., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kulii, Y., Kuhr, T., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Lautenbach, K., Leboucher, R., Diberder, F. R. Le, Leitl, P., Levit, D., Li, C., Li, L. K., Li, Y., Libby, J., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lozar, A., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuda, T., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Milesi, M., Miller, C., Mirra, M., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakamura, K. R., Nakao, M., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nisar, N. K., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Paladino, A., Panta, A., Paoloni, E., Pardi, S., Parham, K., Park, H., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Pham, F., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rad, N., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Rizzuto, L. B., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sahoo, D., Sanders, D. A., Sandilya, S., Sangal, A., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stottler, Z. S., Stroili, R., Strube, J., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tanigawa, H., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Toutounji, N., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uematsu, Y., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varner, G. S., Varvell, K. E., Veronesi, M., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Welsch, M., Wessel, C., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yusa, Y., Zani, L., Zhang, Y., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhou, X. Y., and Zhukova, V. I.
Subjects: High Energy Physics - Experiment
Abstract: We report a measurement of decay-time dependent charge-parity ($CP$) asymmetries in $B^0 \rightarrow K^0_S K^0_S K^0_S$ decays. We use $387 \times 10^6 B\bar{B}$ pairs collected at the $\Upsilon(4S)$ resonance with the Belle II detector at the SuperKEKB asymmetric-energy electron-positron collider. We reconstruct 220 signal events and extract the $CP$-violating parameters $S$ and $C$ from a fit to the distribution of the decay-time difference between the two $B$ mesons. The resulting confidence region is consistent with previous measurements in $B^0 \rightarrow K^0_S K^0_S K^0_S$ and $B^0 \rightarrow (c\bar{c})K^0$ decays, and with predictions based on the standard model., Comment: 10 pages, 5 figures
Published: 2024

53. Realization of a Spin Glass in a two-dimensional van der Waals material

Author: Pal, Banabir, Gopi, Ajesh K., Guan, Yicheng, Chakraborty, Anirban, Tiwari, Kajal, Mathew, Anagha, Srivastava, Abhay K., Zhang, Wenjie, Hazra, Binoy K., Meyerheim, Holger, and Parkin, Stuart S. P.
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Recent advances in van der Waals (vdW) materials have sparked renewed interest in the impact of dimensionality on magnetic phase transitions. While ordered magnetic phases have been demonstrated to survive in the two-dimensional (2D) limit, the quest for a spin-glass with quenched magnetic disorder in lower dimensions has proven elusive. Here we show evidence of a spin-glass emerging from randomly distributed Fe atoms in Fe3GeTe2, the first time such a state has been reported in a vdW material. AC magnetic susceptibility displays a strong frequency dependence indicative of slow spin dynamics. Additional distinctive phenomena, including ageing, chaos, and memory effects, further substantiate the existence of a glassy state. Remarkably, we find that this state persists even in single-unit-cell thick Fe3GeTe2, thereby confirming the existence of a 2D spin-glass. The formation of spin-glass states via intercalation in vdW systems allows for highly tunable spin-glass states that are otherwise difficult to realize.
Published: 2024

54. New graph-neural-network flavor tagger for Belle II and measurement of $\sin2\phi_1$ in $B^0 \to J/\psi K^0_\text{S}$ decays

Author: Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Bilokin, S., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Doležal, Z., Dong, T. V., Dorigo, M., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dujany, G., Ecker, P., Eliachevitch, M., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Han, Y., Hara, K., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Joo, K. K., Junkerkalefeld, H., Kakuno, H., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Levit, D., Li, C., Li, L. K., Li, Y., Li, Y. B., Libby, J., Lin, Y. -R., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakamura, K. R., Nakao, M., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Panta, A., Pardi, S., Parham, K., Park, H., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Sangal, A., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schwanda, C., Schwickardi, M., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Sutcliffe, W., Svidras, H., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uematsu, Y., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yusa, Y., Zhang, B., Zhilich, V., Zhou, Q. D., Zhou, X. Y., Zhukova, V. I., and Žlebčík, R.
Subjects: High Energy Physics - Experiment
Abstract: We present GFlaT, a new algorithm that uses a graph-neural-network to determine the flavor of neutral $B$ mesons produced in $\Upsilon(4S)$ decays. It improves previous algorithms by using the information from all charged final-state particles and the relations between them. We evaluate its performance using $B$ decays to flavor-specific hadronic final states reconstructed in a 362 $\text{fb}^{-1}$ sample of electron-positron collisions collected at the $\Upsilon(4S)$ resonance with the Belle II detector at the SuperKEKB collider. We achieve an effective tagging efficiency of $(37.40 \pm 0.43 \pm 0.36) \%$, where the first uncertainty is statistical and the second systematic, which is $18\%$ better than the previous Belle II algorithm. Demonstrating the algorithm, we use $B^{0}\to J/\psi K^0_\text{S}$ decays to measure the mixing-induced and direct $CP$ violation parameters, $S = (0.724 \pm 0.035 \pm 0.009)$ and $C = (-0.035 \pm 0.026 \pm 0.029)$., Comment: 15 pages, 9 figures
Published: 2024
Full Text: View/download PDF

55. Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems

Author: Tripura, Tapas, Panda, Satyam, Hazra, Budhaditya, and Chakraborty, Souvik
Subjects: Mathematics - Dynamical Systems
Abstract: Exploring the intersection of deterministic and stochastic dynamics, this paper delves into Lagrangian discovery for conservative and non-conservative systems under stochastic excitation. Traditional Lagrangian frameworks, adept at capturing deterministic behavior, are extended to incorporate stochastic excitation. The study critically evaluates recent computational methodologies for learning Lagrangians from observed data, highlighting the limitations in interpretability and the exclusion of stochastic excitation. To address these gaps, an automated data-driven framework is proposed for the simultaneous yet uncoupled discovery of Lagrange densities and the volatility function of stochastic excitation by leveraging the sparse regression. This novel framework offers several advantages over existing approaches. Firstly, it provides an interpretable description of the underlying Lagrange density, allowing for a deeper understanding of system dynamics under stochastic excitations. Secondly, it identifies the interpretable form of the generalized stochastic force, addressing the limitations of existing deterministic approaches. Additionally, the framework demonstrates robustness and versatility through numerical case studies encompassing both stochastic differential equations (SDEs) and stochastic partial differential equations (SPDEs), with results showing almost exact approximations to true system behavior and minimal relative error in derived equations of motion.
Published: 2024

56. DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem

Author: Banerjee, Somnath, Dutta, Avik, Agrawal, Aaditya, Hazra, Rima, and Mukherjee, Animesh
Subjects: Computer Science - Computation and Language
Abstract: With the AI revolution in place, the trend for building automated systems to support professionals in different domains such as the open source software systems, healthcare systems, banking systems, transportation systems and many others have become increasingly prominent. A crucial requirement in the automation of support tools for such systems is the early identification of named entities, which serves as a foundation for developing specialized functionalities. However, due to the specific nature of each domain, different technical terminologies and specialized languages, expert annotation of available data becomes expensive and challenging. In light of these challenges, this paper proposes a novel named entity recognition (NER) technique specifically tailored for the open-source software systems. Our approach aims to address the scarcity of annotated software data by employing a comprehensive two-step distantly supervised annotation process. This process strategically leverages language heuristics, unique lookup tables, external knowledge sources, and an active learning approach. By harnessing these powerful techniques, we not only enhance model performance but also effectively mitigate the limitations associated with cost and the scarcity of expert annotators. It is noteworthy that our model significantly outperforms the state-of-the-art LLMs by a substantial margin. We also show the effectiveness of NER in the downstream task of relation extraction., Comment: Accepted at ECML-PKDD 2024 (Long Paper)
Published: 2024

57. How (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries

Author: Banerjee, Somnath, Layek, Sayan, Hazra, Rima, and Mukherjee, Animesh
Subjects: Computer Science - Computation and Language, Computer Science - Cryptography and Security
Abstract: In this study, we tackle a growing concern around the safety and ethical use of large language models (LLMs). Despite their potential, these models can be tricked into producing harmful or unethical content through various sophisticated methods, including 'jailbreaking' techniques and targeted manipulation. Our work zeroes in on a specific issue: to what extent LLMs can be led astray by asking them to generate responses that are instruction-centric such as a pseudocode, a program or a software snippet as opposed to vanilla text. To investigate this question, we introduce TechHazardQA, a dataset containing complex queries which should be answered in both text and instruction-centric formats (e.g., pseudocodes), aimed at identifying triggers for unethical responses. We query a series of LLMs -- Llama-2-13b, Llama-2-7b, Mistral-V2 and Mistral 8X7B -- and ask them to generate both text and instruction-centric responses. For evaluation we report the harmfulness score metric as well as judgements from GPT-4 and humans. Overall, we observe that asking LLMs to produce instruction-centric responses enhances the unethical response generation by ~2-38% across the models. As an additional objective, we investigate the impact of model editing using the ROME technique, which further increases the propensity for generating undesirable content. In particular, asking edited LLMs to generate instruction-centric responses further increases the unethical response generation by ~3-16% across the different models., Comment: Under review. {https://huggingface.co/datasets/SoftMINER-Group/TechHazardQA}
Published: 2024

58. Data-driven Discovery with Large Generative Models

Author: Majumder, Bodhisattwa Prasad, Surana, Harshit, Agarwal, Dhruv, Hazra, Sanchaita, Sabharwal, Ashish, and Clark, Peter
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially. This position paper urges the Machine Learning (ML) community to exploit the capabilities of large generative models (LGMs) to develop automated systems for end-to-end data-driven discovery -- a paradigm encompassing the search and verification of hypotheses purely from a set of provided datasets, without the need for additional data collection or physical experiments. We first outline several desiderata for an ideal data-driven discovery system. Then, through DATAVOYAGER, a proof-of-concept utilizing GPT-4, we demonstrate how LGMs fulfill several of these desiderata -- a feat previously unattainable -- while also highlighting important limitations in the current system that open up opportunities for novel ML research. We contend that achieving accurate, reliable, and robust end-to-end discovery systems solely through the current capabilities of LGMs is challenging. We instead advocate for fail-proof tool integration, along with active user moderation through feedback mechanisms, to foster data-driven scientific discoveries with efficiency and reproducibility.
Published: 2024

59. Measurement of $CP$ asymmetries in $B^0\to\eta'K^0_s$ decays at Belle II

Author: Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bilka, T., Bilokin, S., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dorigo, M., Dort, K., Dreyer, S., Dubey, S., Dujany, G., Ecker, P., Eliachevitch, M., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Halder, S., Han, Y., Hara, K., Hara, T., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Joo, K. K., Junkerkalefeld, H., Kakuno, H., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, J., Kumar, M., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Levit, D., Li, C., Li, L. K., Li, Y., Li, Y. B., Libby, J., Liu, M., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Panta, A., Pardi, S., Parham, K., Park, H., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Sutcliffe, W., Svidras, H., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uematsu, Y., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yusa, Y., Zhang, B., Zhang, Y., Zhilich, V., Zhou, Q. D., Zhou, X. Y., and Zhukova, V. I.
Subjects: High Energy Physics - Experiment
Abstract: We describe a measurement of charge-parity ($CP$) violation asymmetries in $B^0\to\eta'K^0_S$ decays using Belle II data. We consider $\eta'\to\eta(\to\gamma\gamma)\pi^+\pi^-$ and $\eta'\to\rho(\to\pi^+\pi^-)\gamma$ decays. The data were collected at the SuperKEKB asymmetric-energy $e^+e^-$ collider between the years 2019 and 2022, and contain $(387\pm 6) \times 10^6$ bottom-antibottom meson pairs. We reconstruct $829\pm35$ signal decays and extract the $CP$ violating parameters from a fit to the distribution of the proper-decay-time difference between the two $B$ mesons. The measured direct and mixing-induced $CP$ asymmetries are $\text{C}_{\eta'K^0_S} = -0.19 \pm 0.08 \pm 0.03 $ and $\text{S}_{\eta'K^0_S} = +0.67 \pm 0.10 \pm 0.04 $, respectively, where the first uncertainties are statistical and the second are systematic. These results are in agreement with current world averages and standard model predictions.
Published: 2024

60. Search for a heavy neutral lepton that mixes predominantly with the tau neutrino

Author: Belle Collaboration, Nayak, M., Dey, S., Soffer, A., Adachi, I., Aihara, H., Said, S. Al, Asner, D. M., Atmacan, H., Ayad, R., Babu, V., Banerjee, Sw., Bauer, M., Behera, P., Belous, K., Bessner, M., Bhardwaj, V., Bhuyan, B., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Campajola, M., Červenkov, D., Chang, M. -C., Cheon, B. G., Cho, H. E., Cho, K., Choi, Y., Choudhury, S., Das, S., De Nardo, G., De Pietro, G., Dhamija, R., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dubey, S., Ecker, P., Epifanov, D., Ferber, T., Fogel, O., Fulsom, B. G., Gaur, V., Giri, A., Goldenzweig, P., Graziani, E., Gudkova, K., Hadjivasiliou, C., Halder, S., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hedges, M. T., Herrmann, D., Hou, W. -S., Hsu, C. -L., Inami, K., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jia, S., Jin, Y., Kawasaki, T., Kiesling, C., Kim, C. H., Kim, D. Y., Kinoshita, K., Kodyš, P., Korobov, A., Korpar, S., Križan, P., Krokovny, P., Kuhr, T., Kumar, D., Kumar, R., Kuzmin, A., Kwon, Y. -J., Lai, Y. -T., Lam, T., Lange, J. S., Laurenza, M., Lee, S. C., Li, L. K., Li, Y. B., Gioi, L. Li, Libby, J., Lieret, K., Lin, Y. -R., Liventsev, D., Luo, T., Ma, Y., Masuda, M., Maurya, S. K., Meier, F., Merola, M., Miyabayashi, K., Mizuk, R., Mohanty, G. B., Mussa, R., Nakamura, I., Nakao, M., Natkaniec, Z., Natochii, A., Nayak, L., Nishida, S., Ogawa, S., Ono, H., Oskin, P., Pakhlov, P., Pakhlova, G., Pardi, S., Park, H., Park, J., Park, S. -H., Passeri, A., Patra, S., Paul, S., Pestotnik, R., Piilonen, L. E., Podobnik, T., Prencipe, E., Prim, M. T., Röhrken, M., Rostomyan, A., Rout, N., Russo, G., Sandilya, S., Santelj, L., Savinov, V., Schnell, G., Schwanda, C., Seino, Y., Senyo, K., Sevior, M. E., Shan, W., Sharma, C., Shen, C. P., Shiu, J. -G., Singh, J. B., Sokolov, A., Solovieva, E., Starič, M., Sumihama, M., Takizawa, M., Tanida, K., Tenchini, F., Unno, Y., Uno, S., Ushiroda, Y., Vahsen, S. E., Wang, M. -Z., Watanuki, S., Won, E., Yabsley, B. D., Yan, W., Yin, J. H., Yuan, L., Zhang, Z. P., Zhilich, V., and Zhukova, V.
Subjects: High Energy Physics - Experiment
Abstract: We report a search for a heavy neutral lepton (HNL) that mixes predominantly with $\nu_\tau$. The search utilizes data collected with the Belle detector at the KEKB asymmetric energy $e^+ e^-$ collider. The data sample was collected at and just below the center-of-mass energies of the $\Upsilon(4S)$ and $\Upsilon(5S)$ resonances and has an integrated luminosity of $915~\textrm{fb}^{-1}$, corresponding to $(836\pm 12)\times 10^6$ $e^+e^\to\tau^+\tau^-$ events. We search for production of the HNL (denoted $N$) in the decay $\tau^-\to \pi^- N$ followed by its decay via $N \to \mu^+\mu^- \nu_\tau$. The search focuses on the parameter-space region in which the HNL is long lived, so that the $\mu^+\mu^-$ originate from a common vertex that is significantly displaced from the collision point of the KEKB beams. Consistent with the expected background yield, one event is observed in the data sample after application of all the event-selection criteria. We report limits on the mixing parameter of the HNL with the $\tau$ neutrino as a function of the HNL mass., Comment: 10 pages, 5 figures
Published: 2024
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61. Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context

Author: Banerjee, Somnath, Sahoo, Amruit, Layek, Sayan, Dutta, Avik, Hazra, Rima, and Mukherjee, Animesh
Subjects: Computer Science - Computation and Language
Abstract: In the continuously advancing AI landscape, crafting context-rich and meaningful responses via Large Language Models (LLMs) is essential. Researchers are becoming more aware of the challenges that LLMs with fewer parameters encounter when trying to provide suitable answers to open-ended questions. To address these hurdles, the integration of cutting-edge strategies, augmentation of rich external domain knowledge to LLMs, offers significant improvements. This paper introduces a novel framework that combines graph-driven context retrieval in conjunction to knowledge graphs based enhancement, honing the proficiency of LLMs, especially in domain specific community question answering platforms like AskUbuntu, Unix, and ServerFault. We conduct experiments on various LLMs with different parameter sizes to evaluate their ability to ground knowledge and determine factual accuracy in answers to open-ended questions. Our methodology GraphContextGen consistently outperforms dominant text-based retrieval systems, demonstrating its robustness and adaptability to a larger number of use cases. This advancement highlights the importance of pairing context rich data retrieval with LLMs, offering a renewed approach to knowledge sourcing and generation in AI systems. We also show that, due to rich contextual data retrieval, the crucial entities, along with the generated answer, remain factually coherent with the gold answer.
Published: 2024

62. Study of $\Upsilon(10753)$ decays to $\pi^{+}\pi^{-}\Upsilon(nS)$ final states at Belle II

Author: Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Bozek, A., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dorigo, M., Dort, K., Dreyer, S., Dubey, S., Dujany, G., Ecker, P., Eliachevitch, M., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Halder, S., Han, Y., Hara, T., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Joo, K. K., Junkerkalefeld, H., Kakuno, H., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kumar, M., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Levit, D., Li, C., Li, L. K., Li, Y., Li, Y. B., Libby, J., Liu, M., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Panta, A., Pardi, S., Parham, K., Park, H., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Sutcliffe, W., Svidras, H., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yusa, Y., Zani, L., Zhang, B., Zhang, Y., Zhilich, V., Zhou, Q. D., Zhou, X. Y., and Zhukova, V. I.
Subjects: High Energy Physics - Experiment
Abstract: We present an analysis of the process $e^{+}e^{-}\to\pi^{+}\pi^{-}\Upsilon(nS)$ (where $n$ = 1, 2, or 3) reconstructed in $19.6\rm$ $\rm fb^{-1}$ of Belle II data during a special run of the SuperKEKB collider at four energy points near the peak of the $\Upsilon(10753)$ resonance. By analyzing the mass distribution of the $\pi^+\pi^-\Upsilon(nS)$ system and the Born cross sections of the $e^{+}e^{-}\to\pi^{+}\pi^{-}\Upsilon(nS)$ process, we report the first observation of $\Upsilon(10753)$ decays to the $\pi^{+}\pi^{-}\Upsilon(1S)$ and $\pi^{+}\pi^{-}\Upsilon(2S)$ final states, and find no evidence for decays to $\pi^{+}\pi^{-}\Upsilon(3S)$. Possible intermediate states in the $\pi^+\pi^-\Upsilon(1S,2S)$ transitions are also investigated, and no evidence for decays proceeding via the $\pi^\mp Z_b^\pm$ or $f_0(980)\Upsilon(nS)$ intermediate states is found. We measure Born cross sections for the $e^{+}e^{-}\to\pi^{+}\pi^{-}\Upsilon(nS)$ process that, combined with results from Belle, improve the precision of measurements of the $\Upsilon(10753)$ mass and width by nearly a factor of two to $(10756.3\pm2.7\pm0.6)$ MeV/$c^2$ and $(29.7\pm8.5\pm1.1)$ MeV, respectively. The relative ratios of the Born cross sections at the $\Upsilon(10753)$ resonance peak are also reported for the first time.
Published: 2024

63. Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models

Author: Hazra, Rima, Layek, Sayan, Banerjee, Somnath, and Poria, Soujanya
Subjects: Computer Science - Computation and Language
Abstract: In the rapidly advancing field of artificial intelligence, the concept of Red-Teaming or Jailbreaking large language models (LLMs) has emerged as a crucial area of study. This approach is especially significant in terms of assessing and enhancing the safety and robustness of these models. This paper investigates the intricate consequences of such modifications through model editing, uncovering a complex relationship between enhancing model accuracy and preserving its ethical integrity. Our in-depth analysis reveals a striking paradox: while injecting accurate information is crucial for model reliability, it can paradoxically destabilize the model's foundational framework, resulting in unpredictable and potentially unsafe behaviors. Additionally, we propose a benchmark dataset NicheHazardQA to investigate this unsafe behavior both within the same and cross topical domain. This aspect of our research sheds light on how the edits, impact the model's safety metrics and guardrails. Our findings show that model editing serves as a cost-effective tool for topical red-teaming by methodically applying targeted edits and evaluating the resultant model behavior., Comment: Accepted at ACL 2024
Published: 2024

64. DiffClone: Enhanced Behaviour Cloning in Robotics with Diffusion-Driven Policy Learning

Author: Mani, Sabariswaran, Venkataraman, Sreyas, Chandra, Abhranil, Rizvi, Adyan, Sirvi, Yash, Bhattacharya, Soumojit, and Hazra, Aritra
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Robot learning tasks are extremely compute-intensive and hardware-specific. Thus the avenues of tackling these challenges, using a diverse dataset of offline demonstrations that can be used to train robot manipulation agents, is very appealing. The Train-Offline-Test-Online (TOTO) Benchmark provides a well-curated open-source dataset for offline training comprised mostly of expert data and also benchmark scores of the common offline-RL and behaviour cloning agents. In this paper, we introduce DiffClone, an offline algorithm of enhanced behaviour cloning agent with diffusion-based policy learning, and measured the efficacy of our method on real online physical robots at test time. This is also our official submission to the Train-Offline-Test-Online (TOTO) Benchmark Challenge organized at NeurIPS 2023. We experimented with both pre-trained visual representation and agent policies. In our experiments, we find that MOCO finetuned ResNet50 performs the best in comparison to other finetuned representations. Goal state conditioning and mapping to transitions resulted in a minute increase in the success rate and mean-reward. As for the agent policy, we developed DiffClone, a behaviour cloning agent improved using conditional diffusion., Comment: NeurIPS 2023 Train Offline Test Online Workshop and Competition (Best Paper Oral Presentation / Winning Competition Submission)
Published: 2024

65. Predicting The One-Particle Density Matrix With Machine Learning

Author: Hazra, S., Patil, U., and Sanvito, S.
Subjects: Physics - Chemical Physics, Physics - Computational Physics
Abstract: Two of the most widely used electronic structure theory methods, namely Hartree-Fock and Kohn-Sham density functional theory, both requires the iterative solution of a set of Schr\"odinger-like equations. The speed of convergence of such self-consistent field process depends on the complexity of the system under investigation and on the initial guess for the density matrix. An initial density matrix close to the ground-state one will effectively allow one to cut out many of the self-consistent steps necessary to achieve convergence. Here, we predict the density matrix of Kohn-Sham density functional theory by constructing a neural network, which uses the atomic positions as only information. Such neural network provides an initial guess for the density matrix far superior to any other recipes available, in particular for molecules with metallic bonds. Furthermore, the quality of such neural-network density matrix is good enough for the evaluation of interatomic forces. This allows us to run accelerated {\it ab-initio} molecular dynamics with little to no self-consistent steps.
Published: 2024
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66. Towards better representation of Indian summer monsoon rainfall in CMIP6 models: Evaluation of MISO and MJO simulation

Author: Dutta, Ushnanshu, Bhowmik, Moumita, Hazra, Anupam, Shiu, Chein-Jung, and Chen, Jen-Ping
Subjects: Physics - Atmospheric and Oceanic Physics
Abstract: The seasonal prediction of the Indian summer monsoon (ISM) and Monsoon Intraseasonal Oscillations (MISO), as well as the Madden Julian Oscillations (MJO) that strongly modulate MISO, is important to the country for water and crop management. We have analyzed the precipitation, convection, and total cloud fraction (TCF) in the sixth Coupled Model Intercomparison Projects (CMIP6). This study highlights the significant differences in simulating MISO and MJO between the two groups of selected CMIP6 models and physical reasons behind them. The mean and intraseasonal features of MISO and MJO varied significantly in CMIP6 models, which are linked with a better depiction of convection and total cloud fraction. The probability distributions of rainfall and OLR in CMIP6 models indicate significant variations in simulating ISM precipitating clouds. TaiESM1 and IITM-ESM demonstrate improvements in capturing the MISO features. TaiESM1 depicts better eastward propagation of MJO during both summer and winter. The biases in OLR and TCF are also less in the IITM-ESM and TaiESM1 models than in CanESM5 and FGOALS-g3. The results demonstrate the importance of cloud and convection in CMIP6 models to depict realistic MISO and MJO and provide a road map for improving ISM climate prediction and projections. Keywords: ISM rainfall, CMIP6 models, MISO, MJO
Published: 2024

67. Measurements of the branching fraction, polarization, and $CP$ asymmetry for the decay $B^0\rightarrow \omega \omega$

Author: Belle Collaboration, Guan, Y., Schwartz, A. J., Kinoshita, K., Adachi, I., Aihara, H., Said, S. Al, Asner, D. M., Atmacan, H., Ayad, R., Bahinipati, S., Banerjee, Sw., Belous, K., Bennett, J., Bessner, M., Bhardwaj, V., Bhuyan, B., Biswas, D., Bobrov, A., Bodrov, D., Borah, J., Bozek, A., Bračko, M., Branchini, P., Budano, A., Campajola, M., Cao, L., Červenkov, D., Chang, M. -C., Chang, P., Cheon, B. G., Chilikin, K., Cho, H. E., Cho, K., Choi, S. -K., Choi, Y., Choudhury, S., Das, S., De Nardo, G., De Pietro, G., Dhamija, R., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dubey, S., Ecker, P., Epifanov, D., Ferber, T., Ferlewicz, D., Fulsom, B. G., Gaur, V., Giri, A., Goldenzweig, P., Graziani, E., Gu, T., Gudkova, K., Hadjivasiliou, C., Hayasaka, K., Hayashii, H., Hazra, S., Hedges, M. T., Hou, W. -S., Hsu, C. -L., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jia, S., Jin, Y., Joo, K. K., Kawasaki, T., Kim, C. H., Kim, D. Y., Kim, K. -H., Kim, Y. J., Kim, Y. -K., Kodyš, P., Korobov, A., Korpar, S., Kovalenko, E., Križan, P., Krokovny, P., Kuhr, T., Kumar, R., Kumara, K., Kumita, T., Kwon, Y. -J., Lai, Y. -T., Lee, S. C., Levit, D., Li, L. K., Li, Y., Li, Y. B., Gioi, L. Li, Libby, J., Liventsev, D., Luo, T., Masuda, M., Matsuda, T., Maurya, S. K., Meier, F., Merola, M., Metzner, F., Miyabayashi, K., Mizuk, R., Mohanty, G. B., Mussa, R., Nakamura, I., Nakao, M., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Niiyama, M., Nishida, S., Ogawa, S., Ono, H., Pakhlova, G., Pardi, S., Park, H., Park, J., Park, S. -H., Paul, S., Pestotnik, R., Piilonen, L. E., Podobnik, T., Prencipe, E., Prim, M. T., Röhrken, M., Russo, G., Sandilya, S., Santelj, L., Savinov, V., Schnell, G., Schwanda, C., Seino, Y., Senyo, K., Sevior, M. E., Shan, W., Shiu, J. -G., Solovieva, E., Starič, M., Sumisawa, K., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Tiwary, R., Trabelsi, K., Uchida, M., Unno, Y., Uno, S., Urquijo, P., Usov, Y., Vahsen, S. E., Varvell, K. E., Vinokurova, A., Wang, M. -Z., Watanuki, S., Won, E., Xu, X., Yabsley, B. D., Yan, W., Yook, Y., Yuan, L., Zhang, Z. P., Zhilich, V., and Zhukova, V.
Subjects: High Energy Physics - Experiment
Abstract: We present a measurement of $B^{0} \rightarrow \omega \omega$, a charmless decay into two vector mesons, using 772 $\times 10^6$ $B\overline{B}$ pairs collected with the Belle detector at the KEKB $e^+e^-$ collider. The decay is observed with a significance of 7.9 standard deviations. We measure a branching fraction $\mathcal{B} = (1.53 \pm 0.29 \pm 0.17) \times 10^{-6}$, a fraction of longitudinal polarization $f_L = 0.87 \pm 0.13 \pm 0.13$, and a time-integrated $CP$ asymmetry $A_{CP}$ = $-0.44 \pm 0.43 \pm 0.11$, where the first uncertainties listed are statistical and the second are systematic. This is the first observation of $B^{0} \rightarrow \omega \omega$, and the first measurements of $f_L$ and $A_{CP}$ for this decay., Comment: 7 pages, 1 figure, submitted to Phys. Rev. Lett
Published: 2024

68. A test of lepton flavor universality with a measurement of $R(D^{*})$ using hadronic $B$ tagging at the Belle II experiment

Author: Belle II Collaboration, Adachi, I., Adamczyk, K., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Baudot, J., Bauer, M., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Bilokin, S., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cremaldi, L. M., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dort, K., Dreyer, S., Dubey, S., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Han, Y., Hara, K., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hill, E. C., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Inguglia, G., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Joo, K. K., Junkerkalefeld, H., Kakuno, H., Kaleta, M., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Konno, T., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, Y., Li, Y. B., Libby, J., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Manthei, A. C., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuda, T., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Milesi, M., Miller, C., Mirra, M., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakamura, K. R., Nakao, M., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Paladino, A., Panta, A., Paoloni, E., Pardi, S., Parham, K., Park, H., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Pham, F., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Sangal, A., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Smith, K., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uematsu, Y., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Wiechczynski, J., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yusa, Y., Zhang, B., Zhang, Y., Zhilich, V., Zhou, Q. D., Zhou, X. Y., Zhukova, V. I., and Žlebčík, R.
Subjects: High Energy Physics - Experiment
Abstract: The ratio of branching fractions $R(D^{*}) = \mathcal{B}(\overline{B} \rightarrow D^{*} \tau^{-} \overline{\nu}_{\tau})$/$\mathcal{B} (\overline{B} \rightarrow D^{*} \ell^{-} \overline{\nu}_{\ell})$, where $\ell$ is an electron or muon, is measured using a Belle~II data sample with an integrated luminosity of $189~\mathrm{fb}^{-1}$ at the SuperKEKB asymmetric-energy $e^{+} e^{-}$ collider. Data is collected at the $\Upsilon(\mathrm{4S})$ resonance, and one $B$ meson in the $\Upsilon(\mathrm{4S})\rightarrow B\overline{B}$ decay is fully reconstructed in hadronic decay modes. The accompanying signal $B$ meson is reconstructed as $\overline{B}\rightarrow D^{*} \tau^{-}\overline{\nu}_{\tau}$ using leptonic $\tau$ decays. The normalization decay, $\overline{B}\rightarrow D^{*} \ell^{-} \overline{\nu}_{\ell}$, where $\ell$ is an electron or muon, produces the same observable final state particles. The ratio of branching fractions is extracted in a simultaneous fit to two signal-discriminating variables in both channels and yields $R(D^{*}) = 0.262~_{-0.039}^{+0.041}(\mathrm{stat})~_{-0.032}^{+0.035}(\mathrm{syst})$. This result is consistent with the current world average and with standard model predictions., Comment: 16 pages, 17 figures, submitted to PRD
Published: 2024

69. Evaluation of Quality Parameters and Mineralogy of Iron Ore Pellets with Added Mill Scale

Author: Mogale, Amit, Das, Surya Kanta, Singhai, Mrigandra, Chavhan, Vijay, and Hazra, Sujoy
Published: 2024
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70. ‘ImageJ’ Based Screening Method for Field Pea Germplasm against Pea Leaf Miner (Phytomyza horticola Goureau)

Author: Chandra, Anup, Hazra, K. K., Parihar, Ashok Kumar, G.K., Sujayanand, and Dixit, G. P.
Published: 2024
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71. Estimation of Health State Utility Values for Immunoglobulin A Nephropathy: A Time Trade-Off Analysis

Author: Zhou, Zheng-Yi, Bensink, Mark E., Hazra, Nisha C., Xu, Chunyi, Hendry, Bruce, Sharpe, Claire C., and Zhou, Mo
Published: 2024
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72. An optimized cluster validity index for identification of cancer mediating genes

Author: Hazra, Subir and Ghosh, Anupam
Published: 2024
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73. A utopic adventure in the modelling of conditional univariate and multivariate extremes: EVA (2023) Conference Data Challenge: Team Yahabe

Author: Belzile, Léo R., Hazra, Arnab, and Yadav, Rishikesh
Published: 2024
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74. FQPDR: federated quantum neural network for privacy-preserving early detection of diabetic retinopathy

Author: De, Debashis, Nandy Pal, Mahua, and Hazra, Dipankar
Published: 2024
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75. Evaluation of the impact of the tropical oscillations on the Indian summer monsoon in the global climate models

Author: Dutta, Ushnanshu, Bhowmik, Moumita, Hazra, Anupam, Shiu, Chein-Jung, and Chen, Jen-Ping
Published: 2024
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76. Nucleoredoxin Vis-à-Vis a Novel Thioredoxin in Regulating Oxidative Stress in Plants: A Review

Author: Hazra, Soham, Chatterjee, Avishek, Bhattacharyya, Somnath, and Sen, Poulomi
Published: 2024
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77. Multivariate stochastic comparisons of sequential order statistics with non-identical components

Author: Sahoo, Tanmay, Hazra, Nil Kamal, and Balakrishnan, Narayanaswamy
Published: 2024
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78. Fusarium Dieback: Scientific Challenges and Priorities to Safeguard Indian tea Industry

Author: Hazra, Anjan, Chaudhuri, Rituparna Kundu, and Chakraborti, Dipankar
Published: 2024
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79. Genetic diversity of female clones of pointed gourd (Trichosanthes dioica Roxb.) based on multivariate analysis and inter simple sequence repeat (ISSR) marker

Author: Sinha, Gayatri, Pramanik, Subhradeep, Maurya, Praveen Kumar, Mallick, Rajdeep Guha, Baul, Debanjan, Bhattacharjee, Tridip, Banerjee, Swadesh, Dutta, Subrata, Chatterjee, Soumitra, Mandal, Asit Kumar, Chakraborty, Ivi, Hazra, Pranab, and Chattopadhyay, Arup
Published: 2024
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80. Dicyanamide Anion Protracted Assemblies of Cd(II)-Salen Type Coordination Polymers: Synthetic Perspective, Characterization, Crystallographic Notability, DFT Overview, and Biological Appraisal

Author: Hazra, Suman, Majumdar, Dhrubajyoti, Philip, Jessica Elizabeth, Gassoumi, Bouzid, Ghalla, Houcine, Roy, Sourav, and Dalai, Sudipta
Published: 2024
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81. Knöcherne Rekonstruktionsoption bei ausgeprägter bipolarer Knochendefektsituation und anteriorer Schulterinstabilität

Author: Imiolczyk, Jan-Philipp, Akgün, Doruk, Minkus, Marvin, Paksoy, Alp, Hazra, Rony-Orjit Dey, Weiß, Isabella, Thiele, Kathi, and Moroder, Philipp
Published: 2024
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82. A Simple Surgical Technique for Correction of Varus Deformity in Advanced Osteoarthritis of Knees by Medial Femoral Condylar Sliding Osteotomy-Description of Procedure and short term Outcome-A Prospective Study

Author: Pathak, Chandan, Chattaraj, Anjan, Hazra, Sunit, Saha, Rwivudeep, Kumar, Sanjay, and Chandra, Mainak
Published: 2024
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83. Exploring the clinical connections between epilepsy and diabetes mellitus: Promising therapeutic strategies utilizing agmatine and metformin

Author: Nangia, Aayushi, Saravanan, Janani Srividya, Hazra, Shruti, Priya, Vijayan, Sudesh, Ravi, Rana, Sandeep Singh, and Ahmad, Faraz
Published: 2024
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84. Optimal Power Flow of Multi-objective Combined Heat and Power with Wind-Solar-Electric Vehicle-Tidal Using Hybrid Evolutionary Approach

Author: Paul, Chandan, Sarkar, Tushnik, Dutta, Susanta, Hazra, Sunanda, and Roy, Provas Kumar
Published: 2024
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85. State of the art for moth-flame optimization applied electric vehicles–solar–wind–hydro–thermal power system

Author: Hazra, Sunanda, Roy, Provas Kumar, and Paul, Chandan
Published: 2024
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86. A Decade Review of Device-to-Device Communication in mm-Wave Network: Challenges and Solutions

Author: Sarma, Subhra Sankha and Hazra, Ranjay
Published: 2024
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87. Deciphering putative protein profile of a photomorphogenic high pigment mutant of Solanum lycopersicum (hp-1) by high-throughput LC–MS/MS analysis

Author: Pal, Harshata, Sethi, Avinash, Dhal, Somali, Khan, Tahsin, and Hazra, Pranab
Published: 2024
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88. Herausforderungen und Chancen für die chirurgische Weiterbildung: Ein fachgesellschaftsübergreifendes Positionspapier vor dem Hintergrund der Krankenhausstrukturreform

Author: Schlottmann, Frederik, Drossard, Sabine, Dey Hazra, Maria, Blank, Beate, Herbolzheimer, Marit, Mulorz, Joscha, Kröplin, Juliane, Huber, Tobias, Doukas, Panagiotis, Sadat, Najla, Rüsseler, Miriam, Rösch, Romina, Bouffleur, Frederic, Lif Keller, Sarah, and Freund, Gerrit
Published: 2024
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89. Characterization of gynoecious-parthenocarpic and monoecious cucumber lines (Cucumis sativus L.) and regression modelling to obtain high yielding and functionally rich genotypes

Author: Bose, Suvojit, Hazra, Soham, Hazra, Pranab, Chattopadhyay, Arup, Maji, Anirban, Basfore, Shibnath, and Karak, Chandan
Published: 2024
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90. Genetic control of important yield attributing characters predicted through machine learning in segregating generations of interspecific crosses of tomato (Solanum lycopersicum L.)

Author: Acharya, Brati, Kumar, P. Pradeep, Hazra, Soham, Dutta, Subhramalya, Saha, Subhrajyoti, Roy, Sourav, Maji, Anirban, Chakraborty, Ivi, Chattopadhyay, Arup, and Hazra, Pranab
Published: 2024
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91. Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.

Author: Sun, Jingwen, Rodríguez, Oliver, Hoar, Benjamin, Zhang, Weitong, Xiang, Danlei, Tang, Tianhua, Hazra, Avijit, Min, Daniel, Sigman, Matthew, Costentin, Cyrille, Gu, Quanquan, Rodríguez-López, Joaquín, Liu, Chong, Doyle, Abigail, and Sheng, Hongyuan
Abstract: Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanisms presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.
Published: 2024

92. Site-specific acetylation of polynucleotide kinase 3′-phosphatase regulates its distinct role in DNA repair pathways

Author: Islam, Azharul, Chakraborty, Anirban, Sarker, Altaf H, Aryal, Uma K, Pan, Lang, Sharma, Gulshan, Boldogh, Istvan, and Hazra, Tapas
Subjects: Biochemistry and Cell Biology, Biological Sciences, Genetics, Rare Diseases, Neurosciences, 2.1 Biological and endogenous factors, Underpinning research, Aetiology, 1.1 Normal biological development and functioning, Generic health relevance, Animals, Humans, Mice, Acetylation, DNA Damage, DNA Repair, DNA Repair Enzymes, Mammals, Phosphotransferases (Alcohol Group Acceptor), Polynucleotide 5'-Hydroxyl-Kinase, Environmental Sciences, Information and Computing Sciences, Developmental Biology, Biological sciences, Chemical sciences, Environmental sciences
Abstract: Mammalian polynucleotide kinase 3'-phosphatase (PNKP), a DNA end-processing enzyme with 3'-phosphatase and 5'-kinase activities, is involved in multiple DNA repair pathways, including base excision (BER), single-strand break (SSBR), and double-strand break repair (DSBR). However, little is known as to how PNKP functions in such diverse repair processes. Here we report that PNKP is acetylated at K142 (AcK142) by p300 constitutively but at K226 (AcK226) by CBP, only after DSB induction. Co-immunoprecipitation analysis using AcK142 or AcK226 PNKP-specific antibodies showed that AcK142-PNKP associates only with BER/SSBR, and AcK226 PNKP with DSBR proteins. Despite the modest effect of acetylation on PNKP's enzymatic activity in vitro, cells expressing non-acetylable PNKP (K142R or K226R) accumulated DNA damage in transcribed genes. Intriguingly, in striatal neuronal cells of a Huntington's Disease (HD)-based mouse model, K142, but not K226, was acetylated. This is consistent with the reported degradation of CBP, but not p300, in HD cells. Moreover, transcribed genomes of HD cells progressively accumulated DSBs. Chromatin-immunoprecipitation analysis demonstrated the association of Ac-PNKP with the transcribed genes, consistent with PNKP's role in transcription-coupled repair. Thus, our findings demonstrate that acetylation at two lysine residues, located in different domains of PNKP, regulates its distinct role in BER/SSBR versus DSBR.
Published: 2024

93. Imperfect Entangling Power of Quantum Gates

Author: Mondal, Sudipta, Hazra, Samir Kumar, and De, Aditi Sen
Subjects: Quantum Physics
Abstract: Achieving perfect control over the parameters defining a quantum gate is, in general, a very challenging task, and at the same time, environmental interactions can introduce disturbances to the initial states as well. Here we address the problem of how the imperfections in unitaries and noise present in the input states affect the entanglement-generating power of a given quantum gate -- we refer to it as imperfect (noisy) entangling power. We observe that, when the parameters of a given unitary are chosen randomly from a Gaussian distribution centered around the desired mean, the quenched average entangling power -- averaged across multiple random samplings -- exhibits intriguing behavior like it may increase or show nonmonotonic behavior with the increase of disorder strength for certain classes of diagonal unitary operators. For arbitrary unitary operators, the quenched average power tends to stabilize, showing almost constant behavior with variation in the parameters instead of oscillating. Our observations also reveal that, in the presence of a local noise model, the input states that maximize the entangling power of a given unitary operator differ considerably from the noiseless scenario. Additionally, we report that the rankings among unitary operators according to their entangling power in the noiseless case change depending on the noise model and noise strength., Comment: 15 pages, 15 figures
Published: 2023

94. The friendship paradox for sparse random graphs

Author: Hazra, Rajat Subhra, Hollander, Frank den, and Parvaneh, Azadeh
Subjects: Mathematics - Probability
Abstract: Let $G_n$ be an undirected finite graph on $n\in\mathbb{N}$ vertices labelled by $[n] = \{1,\ldots,n\}$. For $i \in [n]$, let $\Delta_{i,n}$ be the friendship bias of vertex $i$, defined as the difference between the average degree of the neighbours of vertex $i$ and the degree of vertex $i$ itself when $i$ is not isolated, and zero when $i$ is isolated. Let $\mu_n$ denote the friendship-bias empirical distribution, i.e., the measure that puts mass $\frac{1}{n}$ at each $\Delta_{i,n}$, $i \in [n]$. The friendship paradox says that if $G_n$ has no self-loops, then $\int_{\mathbb{R}} x\mu_n(\mathrm{d}x) \geq 0$, with equality if and only if in each connected component of $G_n$ all the degrees are the same. We show that if $(G_n)_{n\in\mathbb{N}}$ is a sequence of sparse random graphs that converges to a rooted random tree in the sense of convergence locally in probability, then $\mu_n$ converges weakly to a limiting measure $\mu$ that is expressible in terms of the law of the rooted random tree. We study $\mu$ for four classes of sparse random graphs: the homogeneous Erd\H{o}s-R\'enyi random graph, the inhomogeneous Erd\H{o}s-R\'enyi random graph, the configuration model and the preferential attachment model. In particular, we compute the first two moments of $\mu$, identify the right tail of $\mu$, and argue that $\mu([0,\infty))\geq\tfrac{1}{2}$, a property we refer to as friendship-paradox significance.
Published: 2023

95. Percolation on preferential attachment models

Author: Hazra, Rajat Subhra, van der Hofstad, Remco, and Ray, Rounak
Subjects: Mathematics - Probability, 05C80
Abstract: We study the percolation phase transition on preferential attachment models, in which vertices enter with $m$ edges and attach proportionally to their degree plus $\delta$. We identify the critical percolation threshold as $$\pi_c=\frac{\delta}{2\big(m(m+\delta)+\sqrt{m(m-1)(m+\delta)(m+1+\delta)}\big)}$$ for $\delta$ positive and $\pi_c=0$ for non-positive values of $\delta$. Therefore the giant component is robust for $\delta\in(-m,0]$, while it is not for $\delta>0$. Our proof for the critical percolation threshold consists of three main steps. First, we show that preferential attachment graphs are large-set expanders, enabling us to verify the conditions outlined by Alimohammadi, Borgs, and Saberi (2023). Within their conditions, the proportion of vertices in the largest connected component in a sequence converges to the survival probability of percolation on the local limit. In particular, the critical percolation threshold for both the graph and its local limit are identical. Second, we identify $1/\pi_c$ as the spectral radius of the mean offspring operator of the P\'olya point tree, the local limit of preferential attachment models. Lastly, we prove that the critical percolation threshold for the P\'olya point tree is the inverse of the spectral radius of the mean offspring operator. For positive $\delta$, we use sub-martingales to prove sub-criticality and apply spine decomposition theory to demonstrate super-criticality, completing the third step of the proof. For $\delta\leq 0$ and any $\pi>0$ instead, we prove that the percolated P\'olya point tree dominates a supercritical branching process, proving that the critical percolation threshold equals $0$.
Published: 2023

96. Search for the $e^+e^-\to\eta_{b}(1S)\omega$ and $e^+e^-\to\chi_{b0}(1P)\omega$ processes at $\sqrt{s}=10.745\,\mathrm{GeV}$

Author: Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Babu, V., Bae, H., Bahinipati, S., Bambade, P., Banerjee, Sw., Barrett, M., Baudot, J., Bauer, M., Baur, A., Beaubien, A., Becherer, F., Becker, J., Behera, P. K., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bilka, T., Bilokin, S., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Bozek, A., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Bussino, S., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Das, S., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Ferlewicz, D., Fillinger, T., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Garmash, A., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Grammatico, T., Granderath, S., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Gudkova, K., Halder, S., Han, Y., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hill, E. C., Hoek, M., Hohmann, M., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kakuno, H., Kalita, D., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Konno, T., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Kraetzschmar, T. M. G., Križan, P., Krokovny, P., Kulii, Y., Kuhr, T., Kumar, J., Kumar, M., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lam, T., Lanceri, L., Lange, J. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Leitl, P., Levit, D., Lewis, P. M., Li, L. K., Li, Y., Li, Y. B., Libby, J., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lozar, A., Lueck, T., Lyu, C., Ma, Y., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Manthei, A. C., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martel, L., Martellini, C., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuda, T., Matsuoka, K., Matvienko, D., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Milesi, M., Miller, C., Mirra, M., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Molina-Gonzalez, N., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, H., Nakazawa, Y., Charan, A. Narimani, Naruki, M., Narwal, D., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nazaryan, G., Niebuhr, C., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Oskin, P., Otani, F., Pakhlov, P., Pakhlova, G., Paladino, A., Panta, A., Pardi, S., Parham, K., Park, H., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Pham, F., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Russo, G., Sanders, D. A., Sandilya, S., Sangal, A., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Uematsu, Y., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Volpe, R., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Zani, L., Zhang, B., Zhilich, V., Zhou, Q. D., Zhou, X. Y., Zhukova, V. I., and Žlebčík, R.
Subjects: High Energy Physics - Experiment
Abstract: We search for the $e^+e^-\to\eta_b(1S)\omega$ and $e^+e^-\to\chi_{b0}(1P)\omega$ processes at a center-of-mass energy of 10.745 GeV, which is close to the peak of the $\Upsilon(10753)$ state. We use data collected by the Belle II experiment during a special run, corresponding to an integrated luminosity of $9.8\,\mathrm{fb}^{-1}$. We reconstruct $\omega\to\pi^+\pi^-\pi^0$ decays and use the $\omega$ meson's recoil mass to search for the signals. We do not find evidence for either process, and set upper limits on the corresponding Born-level cross sections of 2.5 pb and 7.8 pb, respectively, at the 90% confidence level. The $\chi_{b0}(1P)\omega$ limit is the result of a combination of this analysis and a previous search using full reconstruction., Comment: 8 pages, 3 figures, submitted to PRD
Published: 2023

97. An utopic adventure in the modelling of conditional univariate and multivariate extremes

Author: Belzile, Léo R., Hazra, Arnab, and Yadav, Rishikesh
Subjects: Statistics - Applications, Statistics - Methodology
Abstract: The EVA 2023 data competition consisted of four challenges, ranging from interval estimation for very high quantiles of univariate extremes conditional on covariates, point estimation of unconditional return levels under a custom loss function, to estimation of the probabilities of tail events for low and high-dimensional multivariate data. We tackle these tasks by revisiting the current and existing literature on conditional univariate and multivariate extremes. We propose new cross-validation methods for covariate-dependent models, validation metrics for exchangeable multivariate models, formulae for the joint probability of exceedance for multivariate generalized Pareto vectors and a composition sampling algorithm for generating multivariate tail events for the latter. We highlight overarching themes ranging from model validation at extremely high quantile levels to building custom estimation strategies that leverage model assumptions., Comment: 42 pages, 9 figures, 10 tables
Published: 2023

98. Anomalous relaxation and hyperuniform fluctuations in center-of-mass conserving systems with broken time-reversal symmetry

Author: Mukherjee, Anirban, Tapader, Dhiraj, Hazra, Animesh, and Pradhan, Punyabrata
Subjects: Condensed Matter - Statistical Mechanics
Abstract: We study a paradigmatic model of absorbing-phase transition - the Oslo model - on a one-dimensional ring of $L$ sites with a fixed global density $\bar{\rho}$; notably, microscopic dynamics conserve both mass and \textit{center of mass (CoM), but lacks time-reversal symmetry}. Despite having highly constrained dynamics due to CoM conservation, the system exhibits diffusive relaxation away from criticality and superdiffusive relaxation near criticality. Furthermore, the CoM conservation severely restricts particle movement, rendering the mobility to vanish exactly. Indeed the temporal growth of current fluctuation is qualitatively different from that observed in diffusive systems with a single conservation law. Away from criticality, steady-state fluctuation $\langle \mathcal{Q}_i^2(T,\Delta) \rangle$ of current $\mathcal{Q}_i$ across $i$th bond up to time $T$ \textit{saturates} as $\langle \mathcal{Q}_i^2 \rangle \simeq \Sigma_Q^2(\Delta) - {\rm const.} T^{-1/2}$; near criticality, it grows subdiffusively as $\langle \mathcal{Q}_i^2 \rangle \sim T^\alpha$, with $0 < \alpha < 1/2$, and eventually \textit{saturates} to $\Sigma_Q^2(\Delta)$. The asymptotic current fluctuation $\Sigma_Q^2(\Delta)$ is a \textit{nonmonotonic} function of $\Delta$: It diverges as $\Sigma_Q^2(\Delta) \sim \Delta^2$ for $\Delta \gg \rho_c$ and $\Sigma_Q^2(\Delta) \sim \Delta^{-\delta}$, with $\delta > 0$, for $\Delta \to 0^+$. By using a mass-conservation principle, we exactly determine the exponents $\delta = 2(1-1/\nu_\perp)/\nu_\perp$ and $\alpha = \delta/z \nu_\perp$ via the correlation-length and dynamic exponents, $\nu_\perp$ and $z$, respectively. Finally, we show that, in the steady state, the self-diffusion coefficient $\mathcal{D}_s(\bar{\rho})$ of tagged particles is connected to activity by $\mathcal{D}_s(\bar{\rho}) = a(\bar{\rho}) / \bar{\rho}$., Comment: 27 pages, 14 figures, some small changes in the text and figures
Published: 2023
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99. Largest Eigenvalue of the Configuration Model and Breaking of Ensemble Equivalence

Author: Dionigi, Pierfrancesco, Garlaschelli, Diego, Hazra, Rajat Subhra, and Hollander, Frank den
Subjects: Mathematics - Probability, Mathematical Physics
Abstract: We analyse the largest eigenvalue of the adjacency matrix of the configuration model with large degrees, where the latter are treated as hard constraints. In particular, we compute the expectation of the largest eigenvalue for degrees that diverge as the number of vertices $n$ tends to infinity, uniformly on a scale between $1$ and $\sqrt{n}$, and show that a weak law of large numbers holds. We compare with what was derived in our earlier paper "Central limit theorem for the principal eigenvalue and eigenvector of Chung-Lu random graphs" for the Chung-Lu model, which in the regime considered represents the corresponding configuration model with soft constraints, and show that the expectation is shifted down by $1$ asymptotically. This shift is a signature of breaking of ensemble equivalence between the hard and soft (also known as micro-canonical and canonical) versions of the configuration model. The latter result generalizes a previous finding in "A spectral signature of breaking of ensemble equivalence for constrained random graphs" obtained in the case when all degrees are equal., Comment: 21 pages, 3 figures
Published: 2023

100. Silicon Vertex Detector of the Belle II Experiment

Author: Mondal, S., Adamczyk, K., Aggarwal, L., Aihara, H., Aziz, T., Bacher, S., Bahinipati, S., Batignani, G., Baudot, J., Behera, P. K., Bettarini, S., Bilka, T., Bozek, A., Buchsteiner, F., Casarosa, G., Corona, L., Das, S. B., Dujany, G., Finck, C., Forti, F., Friedl, M., Gabrielli, A., Gobbo, B., Halder, S., Hara, K., Hazra, S., Higuchi, T., Irmler, C., Ishikawa, A., Jin, Y., Kaleta, M., Kaliyar, A. B., Kandra, J., Kang, K. H., Kodyš, P., Kohriki, T., Kumar, R., Lalwani, K., Lautenbach, K., Leboucher, R., Lee, S. C., Libby, J., Martel, L., Massaccesi, L., Mohanty, G. B., Nakamura, K. R., Natkaniec, Z., Onuki, Y., Otani, F., PaladinoA, A., Paoloni, E., Park, H., Polat, L., Rao, K. K., Ripp-Baudot, I., Rizzo, G., Sato, Y., Schwanda, C., Serrano, J., Shimasaki, T., Suzuki, J., Tanaka, S., Tanigawa, H., Tenchini, F., Thalmeier, R., Tiwary, R., Tsuboyama, T., Uematsu, Y., Vitale, L., Wang, Z., Webb, J., Werbycka, O., Wiechczynski, J., Yin, H., and Zani, L.
Subjects: High Energy Physics - Experiment
Abstract: The silicon vertex detector (SVD) is installed at the heart of the Belle II experiment, taking data at the high-luminosity $B$-Factory SuperKEKB since 2019. The detector has shown a stable and above-99\% hit efficiency, with a large signal-to-noise in all sensors since the beginning of data taking. Cluster position and time resolution have been measured with 2020 and 2022 data and show excellent performance and stability. The effect of radiation damage is visible, but not affecting the performance. As the luminosity increases, higher machine backgrounds are expected and the excellent hit-time information in SVD can be exploited for background rejection. In particular, we have recently developed a novel procedure to select hits by grouping them event-by-event based on their time. This new procedure allows a significant reduction of the fake rate, while preserving the tracking efficiency, and it has therefore replaced the previous cut-based procedure. We have developed a method that uses the SVD hits to estimate the track time (previously unavailable) and the collision time. It has a similar precision to the estimate based on the drift chamber but its execution time is three orders of magnitude smaller, allowing a faster online reconstruction that is crucial in a high luminosity regime. The track time is a powerful information provided to analysis that allows, together with the above-mention grouping selection, to raise the occupancy limit above that expected at nominal luminosity, leaving room for a safety factor. Finally, in June 2022 the data taking of the Belle II experiment was stopped to install a new two-layer DEPFET detector (PXD) and upgrade components of the accelerator. The whole silicon tracker (PXD+SVD) has been extracted from Belle II, the new PXD installed, the detector closed and commissioned. We briefly describe the SVD results of this upgrade., Comment: 16$^{\text{th}}$ Topical Seminar on Innovative Particle and Radiation Detectors (IPRD23) 25--29 September 2023 Siena, Italy
Published: 2023
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