Browsing by Author "Xiao, X."

Now showing 1 - 3 of 3
  • Thumbnail Image
    CALIS - A CALibration Insertion System for the DarkSide-50 dark matter search experiment
    Agnes, P.; Albuquerque, I. F. M.; Alexander, T.; Alton, A. K.; Asner, D. M.; Back, H. O.; Baldin, B.; Biery, K.; Bocci, V.; Bonfini, G.; Bonivento, W.; Bossa, M.; Bottino, B.; Brigatti, A.; Brodsky, J.; Budano, F.; Bussino, S.; Cadeddu, M.; Cadonati, L.; Cadoni, M.; Calaprice, F.; Canci, N.; Candela, A.; Caravati, M.; Cariello, M.; Carlini, M.; Catalanotti, S.; Cavalcante, P.; Chepurnov, A.; Cicalo, C.; Cocco, A. G.; Covone, G.; D'Angelo, D.; D'Incecco, M.; Davini, S.; De Cecco, S.; De Deo, M.; De Vincenzi, M.; Derbin, A. V.; Devoto, A.; Di Eusanio, F.; Di Pietro, G.; Dionisi, C.; Edkins, E.; Empl, A.; Fan, A.; Fiorillo, G.; Fomenko, K.; Forster, G.; Franco, D.; Gabriele, Federico; Galbiati, C.; Giagu, S.; Giganti, C.; Giovanetti, G. K.; Goretti, A. M.; Granato, F.; Grandi, L.; Gromov, M.; Guan, M.; Guardincerri, Y.; Hackett, B. R.; Herner, K.; Hughes, D.; Humble, P.; Hungerford, Ed V.; Ianni, A.; Ianni, A.; James, I.; Johnson, T. N.; Jollet, C.; Keeter, K.; Kendziora, C. L.; Koh, G.; Korablev, D.; Korga, G.; Kubankin, A.; Li, X.; Lissia, M.; Loer, B.; Lombardi, Paolo; Longo, G.; Ma, Y.; Machado, A. A.; Machulin, I. N.; Mandarano, A.; Mari, S. M.; Maricic, Jelena; Marini, L.; Martoff, C. J.; Meregaglia, A.; Meyers, P. D.; Milincic, R.; Miller, J. D.; Montanari, D.; Monte, A.; Mount, B. J.; Muratova, V. N.; Musico, P.; Napolitano, J.; Agasson, A. Navrer; Odrowski, S.; Orsini, M.; Ortica, F.; Pagani, L.; Pallavicini, M.; Pantic, E.; Parmeggiano, S.; Pelczar, K.; Pelliccia, N.; Pocar, A.; Pordes, S.; Pugachev, D. A.; Qian, H.; Randle, K.; Ranucci, G.; Razeti, M.; Razeto, A.; Reinhold, B.; Renshaw, A. L.; Rescigno, M.; Riffard, Q.; Romani, A.; Rossi, B.; Rossi, Nicola; Rountree, D.; Sablone, D.; Saggese, P.; Saldanha, R.; Sands, W.; Savarese, C.; Schlitzer, B.; Segreto, E.; Semenov, D. A.; Shields, E.; Singh, P. N.; Skorokhvatov, Mikhail D.; Smirnov, O. Y.; Sotnikov, A.; Stanford, C.; Suvorov, Yura; Tartaglia, R.; Tatarowicz, J.; Testera, G.; Tonazzo, A.; Trinchese, P.; Unzhakov, E. V.; Verducci, M.; Vishneva, A.; Vogelaar, R. Bruce; Wada, M.; Walker, S.; Wang, H.; Wang, Y.; Watson, A. W.; Westerdale, S.; Wilhelmi, J.; Wojcik, M. M.; Xiang, Xi.; Xiao, X.; Xu, J.; Yang, C.; Zec, A.; Zhong, W.; Zhu, C.; Zuzel, G. (2017-12)
    This paper describes the design, fabrication, commissioning and use of a CALibration source Insertion System (CALIS) in the DarkSide-50 direct dark matter search experiment. CALIS deploys radioactive sources into the liquid scintillator veto to characterize the detector response and detection efficiency of the DarkSide-50 Liquid Argon Time Projection Chamber, and the surrounding 30 t organic liquid scintillator neutron veto. It was commissioned in September 2014 and has been used successfully in several gamma and neutron source campaigns since then. A description of the hardware and an excerpt of calibration analysis results are given below.
  • Thumbnail Image
    Cytokine mRNA Expression in the Small Intestine of Weanling Pigs Fed Diets Supplemented with Specialized Protein or Peptide Sources
    Zhao, J.; Harper, Allen F.; Webb, Kenneth E. Jr.; Kuehn, Larry Alexander; Gilbert, Elizabeth R.; Xiao, X.; Wong, Eric A. (2008-12)
    Cytokines play a central role in the mucosal immune response and are involved in regulation of nutrient absorption, metabolism and animal growth This study investigated the effect of diet manipulation with specialized protein or peptide sources on expression of cytokine (IL-1, IL-6, IL-10, and TNF-alpha) mRNA abundance in different intestinal regions and at different ages post-weaning in piglets. A total of 48 (17 days of age, 6.16 +/- 0.34 kg BW) weanling pigs were fed either a corn-soy/whey protein basal diet, the basal diet supplemented with spray-dried plasma protein (SDPP), or the basal diet supplemented with Peptiva (R), a hydrolyzed marine plant protein. A fourth treatment group was fed the SDPP diet, but the feed intake level was limited (SDPP-LF). Pigs were killed at 3 and 10 d, and intestinal cytokine mRNA was measured by real-time PCR using the relative quantification method. The SDPP-LF group exhibited an increased TNF-alpha mRNA abundance compared with the ad libitum SDPP group (p<0.05). The TNF-alpha and IL-10 mRNA abundance increased from the proximal to distal part of the intestine, and the mRNA abundance was greater (p<0.01) in the distal intestine as compared with the proximal and middle intestine. The cytokines IL-1-beta, IL-10 and TNF-alpha mRNA abundance also increased from d3 to d10 postweaning (p<0.01). In summary, restricted feeding increased the TNF-alpha mRNA abundance in the small intestine, however neither SDPP nor peptide supplementation affected cytokine mRNA expression. Abundance of mRNA for most cytokines examined in this study increased with age post-weaning, suggesting that during 10 d after weaning the mucosal immune system is still under development.
  • Thumbnail Image
    Search for low mass dark matter in DarkSide-50: the bayesian network approach
    Agnes, P.; Albuquerque, I. F. M.; Alexander, T.; Alton, A. K.; Ave, M.; Back, H. O.; Batignani, G.; Biery, K.; Bocci, V.; Bonivento, W. M.; Bottino, B.; Bussino, S.; Cadeddu, M.; Cadoni, M.; Calaprice, F.; Caminata, A.; Campos, M. D.; Canci, N.; Caravati, M.; Cargioli, N.; Cariello, M.; Carlini, M.; Cataudella, V.; Cavalcante, P.; Cavuoti, S.; Chashin, S.; Chepurnov, A.; Cicalò, C.; Covone, G.; D’Angelo, D.; Davini, S.; De Candia, A.; De Cecco, S.; De Filippis, G.; De Rosa, G.; Derbin, A. V.; Devoto, A.; D’Incecco, M.; Dionisi, C.; Dordei, F.; Downing, M.; D’Urso, D.; Fairbairn, M.; Fiorillo, G.; Franco, D.; Gabriele, F.; Galbiati, C.; Ghiano, C.; Giganti, C.; Giovanetti, G. K.; Goretti, A. M.; Grilli di Cortona, G.; Grobov, A.; Gromov, M.; Guan, M.; Gulino, M.; Hackett, B. R.; Herner, K.; Hessel, T.; Hosseini, B.; Hubaut, F.; Hungerford, E. V.; Ianni, An.; Ippolito, V.; Keeter, K.; Kendziora, C. L.; Kimura, M.; Kochanek, I.; Korablev, D.; Korga, G.; Kubankin, A.; Kuss, M.; La Commara, M.; Lai, M.; Li, X.; Lissia, M.; Longo, G.; Lychagina, O.; Machulin, I. N.; Mapelli, L. P.; Mari, S. M.; Maricic, J.; Messina, A.; Milincic, R.; Monroe, J.; Morrocchi, M.; Mougeot, X.; Muratova, V. N.; Musico, P.; Nozdrina, A. O.; Oleinik, A.; Ortica, F.; Pagani, L.; Pallavicini, M.; Pandola, L.; Pantic, E.; Paoloni, E.; Pelczar, K.; Pelliccia, N.; Piacentini, S.; Pocar, A.; Poehlmann, D. M.; Pordes, S.; Poudel, S. S.; Pralavorio, P.; Price, D. D.; Ragusa, F.; Razeti, M.; Razeto, A.; Renshaw, A. L.; Rescigno, M.; Rode, J.; Romani, A.; Sablone, D.; Samoylov, O.; Sandford, E.; Sands, W.; Sanfilippo, S.; Savarese, C.; Schlitzer, B.; Semenov, D. A.; Shchagin, A.; Sheshukov, A.; Skorokhvatov, M. D.; Smirnov, O.; Sotnikov, A.; Stracka, S.; Suvorov, Y.; Tartaglia, R.; Testera, G.; Tonazzo, A.; Unzhakov, E. V.; Vishneva, A.; Vogelaar, R. Bruce; Wada, M.; Wang, H.; Wang, Y.; Westerdale, S.; Wojcik, M. M.; Xiao, X.; Yang, C.; Zuzel, G. (2023-04-24)
    We present a novel approach for the search of dark matter in the DarkSide-50 experiment, relying on Bayesian Networks. This method incorporates the detector response model into the likelihood function, explicitly maintaining the connection with the quantity of interest. No assumptions about the linearity of the problem or the shape of the probability distribution functions are required, and there is no need to morph signal and background spectra as a function of nuisance parameters. By expressing the problem in terms of Bayesian Networks, we have developed an inference algorithm based on a Markov Chain Monte Carlo to calculate the posterior probability. A clever description of the detector response model in terms of parametric matrices allows us to study the impact of systematic variations of any parameter on the final results. Our approach not only provides the desired information on the parameter of interest, but also potential constraints on the response model. Our results are consistent with recent published analyses and further refine the parameters of the detector response model.