Title: Antineutrino energy spectrum unfolding based on the Daya Bay measurement and its applications *
Abstract The prediction of reactor antineutrino spectra will play a crucial role as reactor experiments enter the precision era. The positron energy spectrum of 3.5 million antineutrino inverse beta decay reactions observed by the Daya Bay experiment, in combination with the fission rates of fissile isotopes in the reactor, is used to extract the positron energy spectra resulting from the fission of specific isotopes. This information can be used to produce a precise, data-based prediction of the antineutrino energy spectrum in other reactor antineutrino experiments with different fission fractions than Daya Bay. The positron energy spectra are unfolded to obtain the antineutrino energy spectra by removing the contribution from detector response with the Wiener-SVD unfolding method. Consistent results are obtained with other unfolding methods. A technique to construct a data-based prediction of the reactor antineutrino energy spectrum is proposed and investigated. Given the reactor fission fractions, the technique can predict the energy spectrum to a 2% precision. In addition, we illustrate how to perform a rigorous comparison between the unfolded antineutrino spectrum and a theoretical model prediction that avoids the input model bias of the unfolding method. more »« less
de Gouvêa, André; De Romeri, Valentina; Ternes, Christoph A.(
, Journal of High Energy Physics)
null
(Ed.)
A bstract We explore how well reactor antineutrino experiments can constrain or measure the loss of quantum coherence in neutrino oscillations. We assume that decoherence effects are encoded in the size of the neutrino wave-packet, σ . We find that the current experiments Daya Bay and the Reactor Experiment for Neutrino Oscillation (RENO) already constrain σ > 1 . 0 × 10 − 4 nm and estimate that future data from the Jiangmen Underground Neutrino Observatory (JUNO) would be sensitive to σ < 2 . 1 × 10 − 3 nm. If the effects of loss of coherence are within the sensitivity of JUNO, we expect σ to be measured with good precision. The discovery of nontrivial decoherence effects in JUNO would indicate that our understanding of the coherence of neutrino sources is, at least, incomplete.
Berryman, Jeffrey M.; Huber, Patrick(
, Journal of High Energy Physics)
null
(Ed.)
A bstract We present results from global fits to the available reactor antineutrino dataset, as of Fall 2019, to determine the global preference for a fourth, sterile neutrino. We have separately considered experiments that measure the integrated inverse-beta decay (IBD) rate from those that measure the energy spectrum of IBD events at one or more locations. The evidence that we infer from rate measurements varies between ≲ 3 σ and negligible depending on the reactor antineutrino flux model employed. Moreover, we find that spectral ratios ostensibly imply ≳ 3 σ evidence, consistent with previous work, though these measurements are known to be plagued by issues related to statistical interpretation; these results should therefore be viewed cautiously. The software used is the newly developed GLoBESfit tool set which is based on the publicly available GLoBES framework and will be released as open-source software.
Lewis, David Bruce(
, Environmental Data Initiative)
Site description. This data package consists of data obtained from sampling surface soil (the 0-7.6 cm depth profile) in black mangrove (Avicennia germinans) dominated forest and black needlerush (Juncus roemerianus) saltmarsh along the Gulf of Mexico coastline in peninsular west-central Florida, USA. This location has a subtropical climate with mean daily temperatures ranging from 15.4 °C in January to 27.8 °C in August, and annual precipitation of 1336 mm. Precipitation falls as rain primarily between June and September. Tides are semi-diurnal, with 0.57 m median amplitudes during the year preceding sampling (U.S. NOAA National Ocean Service, Clearwater Beach, Florida, station 8726724). Sea-level rise is 4.0 ± 0.6 mm per year (1973-2020 trend, mean ± 95 % confidence interval, NOAA NOS Clearwater Beach station). The A. germinans mangrove zone is either adjacent to water or fringed on the seaward side by a narrow band of red mangrove (Rhizophora mangle). A near-monoculture of J. roemerianus is often adjacent to and immediately landward of the A. germinans zone. The transition from the mangrove to the J. roemerianus zone is variable in our study area. An abrupt edge between closed-canopy mangrove and J. roemerianus monoculture may extend for up to several hundred meters in some locations, while other stretches of ecotone present a gradual transition where smaller, widely spaced trees are interspersed into the herbaceous marsh. Juncus roemerianus then extends landward to a high marsh patchwork of succulent halophytes (including Salicornia bigellovi, Sesuvium sp., and Batis maritima), scattered dwarf mangrove, and salt pans, followed in turn by upland vegetation that includes Pinus sp. and Serenoa repens. Field design and sample collection. We established three study sites spaced at approximately 5 km intervals along the western coastline of the central Florida peninsula. The sites consisted of the Salt Springs (28.3298°, -82.7274°), Energy Marine Center (28.2903°, -82.7278°), and Green Key (28.2530°, -82.7496°) sites on the Gulf of Mexico coastline in Pasco County, Florida, USA. At each site, we established three plot pairs, each consisting of one saltmarsh plot and one mangrove plot. Plots were 50 m^2 in size. Plots pairs within a site were separated by 230-1070 m, and the mangrove and saltmarsh plots composing a pair were 70-170 m apart. All plot pairs consisted of directly adjacent patches of mangrove forest and J. roemerianus saltmarsh, with the mangrove forests exhibiting a closed canopy and a tree architecture (height 4-6 m, crown width 1.5-3 m). Mangrove plots were located at approximately the midpoint between the seaward edge (water-mangrove interface) and landward edge (mangrove-marsh interface) of the mangrove zone. Saltmarsh plots were located 20-25 m away from any mangrove trees and into the J. roemerianus zone (i.e., landward from the mangrove-marsh interface). Plot pairs were coarsely similar in geomorphic setting, as all were located on the Gulf of Mexico coastline, rather than within major sheltering formations like Tampa Bay, and all plot pairs fit the tide-dominated domain of the Woodroffe classification (Woodroffe, 2002, "Coasts: Form, Process and Evolution", Cambridge University Press), given their conspicuous semi-diurnal tides. There was nevertheless some geomorphic variation, as some plot pairs were directly open to the Gulf of Mexico while others sat behind keys and spits or along small tidal creeks. Our use of a plot-pair approach is intended to control for this geomorphic variation. Plot center elevations (cm above mean sea level, NAVD 88) were estimated by overlaying the plot locations determined with a global positioning system (Garmin GPS 60, Olathe, KS, USA) on a LiDAR-derived bare-earth digital elevation model (Dewberry, Inc., 2019). The digital elevation model had a vertical accuracy of ± 10 cm (95 % CI) and a horizontal accuracy of ± 116 cm (95 % CI). Soil samples were collected via coring at low tide in June 2011. From each plot, we collected a composite soil sample consisting of three discrete 5.1 cm diameter soil cores taken at equidistant points to 7.6 cm depth. Cores were taken by tapping a sleeve into the soil until its top was flush with the soil surface, sliding a hand under the core, and lifting it up. Cores were then capped and transferred on ice to our laboratory at the University of South Florida (Tampa, Florida, USA), where they were combined in plastic zipper bags, and homogenized by hand into plot-level composite samples on the day they were collected. A damp soil subsample was immediately taken from each composite sample to initiate 1 y incubations for determination of active C and N (see below). The remainder of each composite sample was then placed in a drying oven (60 °C) for 1 week with frequent mixing of the soil to prevent aggregation and liberate water. Organic wetland soils are sometimes dried at 70 °C, however high drying temperatures can volatilize non-water liquids and oxidize and decompose organic matter, so 50 °C is also a common drying temperature for organic soils (Gardner 1986, "Methods of Soil Analysis: Part 1", Soil Science Society of America); we accordingly chose 60 °C as a compromise between sufficient water removal and avoidance of non-water mass loss. Bulk density was determined as soil dry mass per core volume (adding back the dry mass equivalent of the damp subsample removed prior to drying). Dried subsamples were obtained for determination of soil organic matter (SOM), mineral texture composition, and extractable and total carbon (C) and nitrogen (N) within the following week. Sample analyses. A dried subsample was apportioned from each composite sample to determine SOM as mass loss on ignition at 550 °C for 4 h. After organic matter was removed from soil via ignition, mineral particle size composition was determined using a combination of wet sieving and density separation in 49 mM (3 %) sodium hexametaphosphate ((NaPO_3)_6) following procedures in Kettler et al. (2001, Soil Science Society of America Journal 65, 849-852). The percentage of dry soil mass composed of silt and clay particles (hereafter, fines) was calculated as the mass lost from dispersed mineral soil after sieving (0.053 mm mesh sieve). Fines could have been slightly underestimated if any clay particles were burned off during the preceding ignition of soil. An additional subsample was taken from each composite sample to determine extractable N and organic C concentrations via 0.5 M potassium sulfate (K_2SO_4) extractions. We combined soil and extractant (ratio of 1 g dry soil:5 mL extractant) in plastic bottles, reciprocally shook the slurry for 1 h at 120 rpm, and then gravity filtered it through Fisher G6 (1.6 μm pore size) glass fiber filters, followed by colorimetric detection of nitrite (NO_2^-) + nitrate (NO_3^-) and ammonium (NH_4^+) in the filtrate (Hood Nowotny et al., 2010,Soil Science Society of America Journal 74, 1018-1027) using a microplate spectrophotometer (Biotek Epoch, Winooski, VT, USA). Filtrate was also analyzed for dissolved organic C (referred to hereafter as extractable organic C) and total dissolved N via combustion and oxidation followed by detection of the evolved CO_2 and N oxide gases on a Formacs HT TOC/TN analyzer (Skalar, Breda, The Netherlands). Extractable organic N was then computed as total dissolved N in filtrate minus extractable mineral N (itself the sum of extractable NH_4-N and NO_2-N + NO_3-N). We determined soil total C and N from dried, milled subsamples subjected to elemental analysis (ECS 4010, Costech, Inc., Valencia, CA, USA) at the University of South Florida Stable Isotope Laboratory. Median concentration of inorganic C in unvegetated surface soil at our sites is 0.5 % of soil mass (Anderson, 2019, Univ. of South Florida M.S. thesis via methods in Wang et al., 2011, Environmental Monitoring and Assessment 174, 241-257). Inorganic C concentrations are likely even lower in our samples from under vegetation, where organic matter would dilute the contribution of inorganic C to soil mass. Nevertheless, the presence of a small inorganic C pool in our soils may be counted in the total C values we report. Extractable organic C is necessarily of organic C origin given the method (sparging with HCl) used in detection. Active C and N represent the fractions of organic C and N that are mineralizable by soil microorganisms under aerobic conditions in long-term soil incubations. To quantify active C and N, 60 g of field-moist soil were apportioned from each composite sample, placed in a filtration apparatus, and incubated in the dark at 25 °C and field capacity moisture for 365 d (as in Lewis et al., 2014, Ecosphere 5, art59). Moisture levels were maintained by frequently weighing incubated soil and wetting them up to target mass. Daily CO_2 flux was quantified on 29 occasions at 0.5-3 week intervals during the incubation period (with shorter intervals earlier in the incubation), and these per day flux rates were integrated over the 365 d period to compute an estimate of active C. Observations of per day flux were made by sealing samples overnight in airtight chambers fitted with septa and quantifying headspace CO_2 accumulation by injecting headspace samples (obtained through the septa via needle and syringe) into an infrared gas analyzer (PP Systems EGM 4, Amesbury, MA, USA). To estimate active N, each incubated sample was leached with a C and N free, 35 psu solution containing micronutrients (Nadelhoffer, 1990, Soil Science Society of America Journal 54, 411-415) on 19 occasions at increasing 1-6 week intervals during the 365 d incubation, and then extracted in 0.5 M K_2SO_4 at the end of the incubation in order to remove any residual mineral N. Active N was then quantified as the total mass of mineral N leached and extracted. Mineral N in leached and extracted solutions was detected as NH_4-N and NO_2-N + NO_3-N via colorimetry as above. This incubation technique precludes new C and N inputs and persistently leaches mineral N, forcing microorganisms to meet demand by mineralizing existing pools, and thereby directly assays the potential activity of soil organic C and N pools present at the time of soil sampling. Because this analysis commences with disrupting soil physical structure, it is biased toward higher estimates of active fractions. Calculations. Non-mobile C and N fractions were computed as total C and N concentrations minus the extractable and active fractions of each element. This data package reports surface-soil constituents (moisture, fines, SOM, and C and N pools and fractions) in both gravimetric units (mass constituent / mass soil) and areal units (mass constituent / soil surface area integrated through 7.6 cm soil depth, the depth of sampling). Areal concentrations were computed as X × D × 7.6, where X is the gravimetric concentration of a soil constituent, D is soil bulk density (g dry soil / cm^3), and 7.6 is the sampling depth in cm.
Goldenstein, Christopher S.; Mathews, Garrett C.; Cole, Ryan K.; Makowiecki, Amanda S.; Rieker, Gregory B.(
, Applied Optics)
This paper presents a data-processing technique that improves the accuracy and precision of absorption-spectroscopy measurements by isolating the molecular absorbance signal from errors in the baseline light intensity () using cepstral analysis. Recently, cepstral analysis has been used with traditional absorption spectrometers to create a modified form of the time-domain molecular free-induction decay (m-FID) signal, which can be analyzed independently from. However, independent analysis of the molecular signature is not possible when the baseline intensity and molecular response do not separate well in the time domain, which is typical when using injection-current-tuned lasers [e.g., tunable diode and quantum cascade lasers (QCLs)] and other light sources with pronounced intensity tuning. In contrast, the method presented here is applicable to virtually all light sources since it determines gas properties by least-squares fitting a simulated m-FID signal (comprising an estimatedand simulated absorbance spectrum) to the measured m-FID signal in the time domain. This method is insensitive to errors in the estimated, which vary slowly with optical frequency and, therefore, decay rapidly in the time domain. The benefits provided by this method are demonstrated via scanned-wavelength direct-absorption-spectroscopy measurements acquired with a distributed-feedback (DFB) QCL. The wavelength of a DFB QCL was scanned across the CO P(0,20) and P(1,14) absorption transitions at 1 kHz to measure the gas temperature and concentration of CO. Measurements were acquired in a gas cell and in a laminar ethylene–air diffusion flame at 1 atm. The measured spectra were processed using the new m-FID-based method and two traditional methods, which rely on inferring (instead of rejecting) the baseline error within the spectral-fitting routine. The m-FID-based method demonstrated superior accuracy in all cases and a measurement precision that wasto 10 times smaller than that provided using traditional methods.
Xu, Hangkun; Abusleme, Angel; Anfimov, Nikolay V.; Callier, Stéphane; Campeny, Agustin; Cao, Guofu; Cao, Jun; Cerna, Cedric; Chen, Yu; Chepurnov, Alexander; et al(
, The European Physical Journal C)
Abstract The Taishan Antineutrino Observatory (TAO or JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). Located near a reactor of the Taishan Nuclear Power Plant, TAO will measure the reactor antineutrino energy spectrum with an unprecedented energy resolution of $$<2\%$$ < 2 % at 1 MeV. Energy calibration is critical to achieve such a high energy resolution. Using the Automated Calibration Unit (ACU) and the Cable Loop System (CLS), multiple radioactive sources are deployed to various positions in the TAO detector for energy calibration. The residual non-uniformity can be controlled within 0.2%. The energy resolution degradation and energy bias caused by the residual non-uniformity can be controlled within 0.05% and 0.3%, respectively. The uncertainty of the non-linear energy response can be controlled within 0.6% with the radioactive sources of various energies, and could be further improved with cosmogenic $$^{12}{\textrm{B}}$$ 12 B which is produced by the interaction of cosmic muon in the liquid scintillator. The stability of other detector parameters, e.g., the gain of each Silicon Photo-multiplier, will be monitored with an ultraviolet LED calibration system.
An, F. P., Balantekin, A. B., Bishai, M., Blyth, S., Cao, G. F., Cao, J., Chang, J. F., Chang, Y., Chen, H. S., Chen, S. M., Chen, Y., Chen, Y. X., Cheng, J., Cheng, Z. K., Cherwinka, J. J., Chu, M. C., Cummings, J. P., Dalager, O., Deng, F. S., Ding, Y. Y., Diwan, M. V., Dohnal, T., Dolzhikov, D., Dove, J., Dvořák, M., Dwyer, D. A., Gallo, J. P., Gonchar, M., Gong, G. H., Gong, H., Grassi, M., Gu, W. Q., Guo, J. Y., Guo, L., Guo, X. H., Guo, Y. H., Guo, Z., Hackenburg, R. W., Hans, S., He, M., Heeger, K. M., Heng, Y. K., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, J. R., Hu, T., Hu, Z. J., Huang, H. X., Huang, J. H., Huang, X. T., Huang, Y. B., Huber, P., Jaffe, D. E., Jen, K. L., Ji, X. L., Ji, X. P., Johnson, R. A., Jones, D., Kang, L., Kettell, S. H., Kohn, S., Kramer, M., Langford, T. J., Lee, J., Lee, J. H., Lei, R. T., Leitner, R., Leung, J. K., Li, F., Li, H. L., Li, J. J., Li, Q. J., Li, R. H., Li, S., Li, S. C., Li, W. D., Li, X. N., Li, X. Q., Li, Y. F., Li, Z. B., Liang, H., Lin, C. J., Lin, G. L., Lin, S., Ling, J. J., Link, J. M., Littenberg, L., Littlejohn, B. R., Liu, J. C., Liu, J. L., Liu, J. X., Lu, C., Lu, H. Q., Luk, K. B., Ma, B. Z., Ma, X. B., Ma, X. Y., Ma, Y. Q., Mandujano, R. C., Marshall, C., McDonald, K. T., McKeown, R. D., Meng, Y., Napolitano, J., Naumov, D., Naumova, E., Nguyen, T. M., Ochoa-Ricoux, J. P., Olshevskiy, A., Pan, H.-R., Park, J., Patton, S., Peng, J. C., Pun, C. S., Qi, F. Z., Qi, M., Qian, X., Raper, N., Ren, J., Reveco, C. Morales, Rosero, R., Roskovec, B., Ruan, X. C., Steiner, H., Sun, J. L., Tmej, T., Treskov, K., Tse, W.-H., Tull, C. E., Viren, B., Vorobel, V., Wang, C. H., Wang, J., Wang, M., Wang, N. Y., Wang, R. G., Wang, W., Wang, W., Wang, X., Wang, Y., Wang, Y. F., Wang, Z., Wang, Z., Wang, Z. M., Wei, H. Y., Wei, L. H., Wen, L. J., Whisnant, K., White, C. G., Wong, H. L., Worcester, E., Wu, D. R., Wu, F. L., Wu, Q., Wu, W. J., Xia, D. M., Xie, Z. Q., Xing, Z. Z., Xu, H. K., Xu, J. L., Xu, T., Xue, T., Yang, C. G., Yang, L., Yang, Y. Z., Yao, H. F., Ye, M., Yeh, M., Young, B. L., Yu, H. Z., Yu, Z. Y., Yue, B. B., Zavadskyi, V., Zeng, S., Zeng, Y., Zhan, L., Zhang, C., Zhang, F. Y., Zhang, H. H., Zhang, J. W., Zhang, Q. M., Zhang, S. Q., Zhang, X. T., Zhang, Y. M., Zhang, Y. X., Zhang, Y. Y., Zhang, Z. J., Zhang, Z. P., Zhang, Z. Y., Zhao, J., Zhao, R. Z., Zhou, L., Zhuang, H. L., and Zou, J. H. Antineutrino energy spectrum unfolding based on the Daya Bay measurement and its applications *. Retrieved from https://par.nsf.gov/biblio/10314316. Chinese Physics C 45.7 Web. doi:10.1088/1674-1137/abfc38.
An, F. P., Balantekin, A. B., Bishai, M., Blyth, S., Cao, G. F., Cao, J., Chang, J. F., Chang, Y., Chen, H. S., Chen, S. M., Chen, Y., Chen, Y. X., Cheng, J., Cheng, Z. K., Cherwinka, J. J., Chu, M. C., Cummings, J. P., Dalager, O., Deng, F. S., Ding, Y. Y., Diwan, M. V., Dohnal, T., Dolzhikov, D., Dove, J., Dvořák, M., Dwyer, D. A., Gallo, J. P., Gonchar, M., Gong, G. H., Gong, H., Grassi, M., Gu, W. Q., Guo, J. Y., Guo, L., Guo, X. H., Guo, Y. H., Guo, Z., Hackenburg, R. W., Hans, S., He, M., Heeger, K. M., Heng, Y. K., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, J. R., Hu, T., Hu, Z. J., Huang, H. X., Huang, J. H., Huang, X. T., Huang, Y. B., Huber, P., Jaffe, D. E., Jen, K. L., Ji, X. L., Ji, X. P., Johnson, R. A., Jones, D., Kang, L., Kettell, S. H., Kohn, S., Kramer, M., Langford, T. J., Lee, J., Lee, J. H., Lei, R. T., Leitner, R., Leung, J. K., Li, F., Li, H. L., Li, J. J., Li, Q. J., Li, R. H., Li, S., Li, S. C., Li, W. D., Li, X. N., Li, X. Q., Li, Y. F., Li, Z. B., Liang, H., Lin, C. J., Lin, G. L., Lin, S., Ling, J. J., Link, J. M., Littenberg, L., Littlejohn, B. R., Liu, J. C., Liu, J. L., Liu, J. X., Lu, C., Lu, H. Q., Luk, K. B., Ma, B. Z., Ma, X. B., Ma, X. Y., Ma, Y. Q., Mandujano, R. C., Marshall, C., McDonald, K. T., McKeown, R. D., Meng, Y., Napolitano, J., Naumov, D., Naumova, E., Nguyen, T. M., Ochoa-Ricoux, J. P., Olshevskiy, A., Pan, H.-R., Park, J., Patton, S., Peng, J. C., Pun, C. S., Qi, F. Z., Qi, M., Qian, X., Raper, N., Ren, J., Reveco, C. Morales, Rosero, R., Roskovec, B., Ruan, X. C., Steiner, H., Sun, J. L., Tmej, T., Treskov, K., Tse, W.-H., Tull, C. E., Viren, B., Vorobel, V., Wang, C. H., Wang, J., Wang, M., Wang, N. Y., Wang, R. G., Wang, W., Wang, W., Wang, X., Wang, Y., Wang, Y. F., Wang, Z., Wang, Z., Wang, Z. M., Wei, H. Y., Wei, L. H., Wen, L. J., Whisnant, K., White, C. G., Wong, H. L., Worcester, E., Wu, D. R., Wu, F. L., Wu, Q., Wu, W. J., Xia, D. M., Xie, Z. Q., Xing, Z. Z., Xu, H. K., Xu, J. L., Xu, T., Xue, T., Yang, C. G., Yang, L., Yang, Y. Z., Yao, H. F., Ye, M., Yeh, M., Young, B. L., Yu, H. Z., Yu, Z. Y., Yue, B. B., Zavadskyi, V., Zeng, S., Zeng, Y., Zhan, L., Zhang, C., Zhang, F. Y., Zhang, H. H., Zhang, J. W., Zhang, Q. M., Zhang, S. Q., Zhang, X. T., Zhang, Y. M., Zhang, Y. X., Zhang, Y. Y., Zhang, Z. J., Zhang, Z. P., Zhang, Z. Y., Zhao, J., Zhao, R. Z., Zhou, L., Zhuang, H. L., & Zou, J. H. Antineutrino energy spectrum unfolding based on the Daya Bay measurement and its applications *. Chinese Physics C, 45 (7). Retrieved from https://par.nsf.gov/biblio/10314316. https://doi.org/10.1088/1674-1137/abfc38
An, F. P., Balantekin, A. B., Bishai, M., Blyth, S., Cao, G. F., Cao, J., Chang, J. F., Chang, Y., Chen, H. S., Chen, S. M., Chen, Y., Chen, Y. X., Cheng, J., Cheng, Z. K., Cherwinka, J. J., Chu, M. C., Cummings, J. P., Dalager, O., Deng, F. S., Ding, Y. Y., Diwan, M. V., Dohnal, T., Dolzhikov, D., Dove, J., Dvořák, M., Dwyer, D. A., Gallo, J. P., Gonchar, M., Gong, G. H., Gong, H., Grassi, M., Gu, W. Q., Guo, J. Y., Guo, L., Guo, X. H., Guo, Y. H., Guo, Z., Hackenburg, R. W., Hans, S., He, M., Heeger, K. M., Heng, Y. K., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, J. R., Hu, T., Hu, Z. J., Huang, H. X., Huang, J. H., Huang, X. T., Huang, Y. B., Huber, P., Jaffe, D. E., Jen, K. L., Ji, X. L., Ji, X. P., Johnson, R. A., Jones, D., Kang, L., Kettell, S. H., Kohn, S., Kramer, M., Langford, T. J., Lee, J., Lee, J. H., Lei, R. T., Leitner, R., Leung, J. K., Li, F., Li, H. L., Li, J. J., Li, Q. J., Li, R. H., Li, S., Li, S. C., Li, W. D., Li, X. N., Li, X. Q., Li, Y. F., Li, Z. B., Liang, H., Lin, C. J., Lin, G. L., Lin, S., Ling, J. J., Link, J. M., Littenberg, L., Littlejohn, B. R., Liu, J. C., Liu, J. L., Liu, J. X., Lu, C., Lu, H. Q., Luk, K. B., Ma, B. Z., Ma, X. B., Ma, X. Y., Ma, Y. Q., Mandujano, R. C., Marshall, C., McDonald, K. T., McKeown, R. D., Meng, Y., Napolitano, J., Naumov, D., Naumova, E., Nguyen, T. M., Ochoa-Ricoux, J. P., Olshevskiy, A., Pan, H.-R., Park, J., Patton, S., Peng, J. C., Pun, C. S., Qi, F. Z., Qi, M., Qian, X., Raper, N., Ren, J., Reveco, C. Morales, Rosero, R., Roskovec, B., Ruan, X. C., Steiner, H., Sun, J. L., Tmej, T., Treskov, K., Tse, W.-H., Tull, C. E., Viren, B., Vorobel, V., Wang, C. H., Wang, J., Wang, M., Wang, N. Y., Wang, R. G., Wang, W., Wang, W., Wang, X., Wang, Y., Wang, Y. F., Wang, Z., Wang, Z., Wang, Z. M., Wei, H. Y., Wei, L. H., Wen, L. J., Whisnant, K., White, C. G., Wong, H. L., Worcester, E., Wu, D. R., Wu, F. L., Wu, Q., Wu, W. J., Xia, D. M., Xie, Z. Q., Xing, Z. Z., Xu, H. K., Xu, J. L., Xu, T., Xue, T., Yang, C. G., Yang, L., Yang, Y. Z., Yao, H. F., Ye, M., Yeh, M., Young, B. L., Yu, H. Z., Yu, Z. Y., Yue, B. B., Zavadskyi, V., Zeng, S., Zeng, Y., Zhan, L., Zhang, C., Zhang, F. Y., Zhang, H. H., Zhang, J. W., Zhang, Q. M., Zhang, S. Q., Zhang, X. T., Zhang, Y. M., Zhang, Y. X., Zhang, Y. Y., Zhang, Z. J., Zhang, Z. P., Zhang, Z. Y., Zhao, J., Zhao, R. Z., Zhou, L., Zhuang, H. L., and Zou, J. H.
"Antineutrino energy spectrum unfolding based on the Daya Bay measurement and its applications *". Chinese Physics C 45 (7). Country unknown/Code not available. https://doi.org/10.1088/1674-1137/abfc38.https://par.nsf.gov/biblio/10314316.
@article{osti_10314316,
place = {Country unknown/Code not available},
title = {Antineutrino energy spectrum unfolding based on the Daya Bay measurement and its applications *},
url = {https://par.nsf.gov/biblio/10314316},
DOI = {10.1088/1674-1137/abfc38},
abstractNote = {Abstract The prediction of reactor antineutrino spectra will play a crucial role as reactor experiments enter the precision era. The positron energy spectrum of 3.5 million antineutrino inverse beta decay reactions observed by the Daya Bay experiment, in combination with the fission rates of fissile isotopes in the reactor, is used to extract the positron energy spectra resulting from the fission of specific isotopes. This information can be used to produce a precise, data-based prediction of the antineutrino energy spectrum in other reactor antineutrino experiments with different fission fractions than Daya Bay. The positron energy spectra are unfolded to obtain the antineutrino energy spectra by removing the contribution from detector response with the Wiener-SVD unfolding method. Consistent results are obtained with other unfolding methods. A technique to construct a data-based prediction of the reactor antineutrino energy spectrum is proposed and investigated. Given the reactor fission fractions, the technique can predict the energy spectrum to a 2% precision. In addition, we illustrate how to perform a rigorous comparison between the unfolded antineutrino spectrum and a theoretical model prediction that avoids the input model bias of the unfolding method.},
journal = {Chinese Physics C},
volume = {45},
number = {7},
author = {An, F. P. and Balantekin, A. B. and Bishai, M. and Blyth, S. and Cao, G. F. and Cao, J. and Chang, J. F. and Chang, Y. and Chen, H. S. and Chen, S. M. and Chen, Y. and Chen, Y. X. and Cheng, J. and Cheng, Z. K. and Cherwinka, J. J. and Chu, M. C. and Cummings, J. P. and Dalager, O. and Deng, F. S. and Ding, Y. Y. and Diwan, M. V. and Dohnal, T. and Dolzhikov, D. and Dove, J. and Dvořák, M. and Dwyer, D. A. and Gallo, J. P. and Gonchar, M. and Gong, G. H. and Gong, H. and Grassi, M. and Gu, W. Q. and Guo, J. Y. and Guo, L. and Guo, X. H. and Guo, Y. H. and Guo, Z. and Hackenburg, R. W. and Hans, S. and He, M. and Heeger, K. M. and Heng, Y. K. and Hor, Y. K. and Hsiung, Y. B. and Hu, B. Z. and Hu, J. R. and Hu, T. and Hu, Z. J. and Huang, H. X. and Huang, J. H. and Huang, X. T. and Huang, Y. B. and Huber, P. and Jaffe, D. E. and Jen, K. L. and Ji, X. L. and Ji, X. P. and Johnson, R. A. and Jones, D. and Kang, L. and Kettell, S. H. and Kohn, S. and Kramer, M. and Langford, T. J. and Lee, J. and Lee, J. H. and Lei, R. T. and Leitner, R. and Leung, J. K. and Li, F. and Li, H. L. and Li, J. J. and Li, Q. J. and Li, R. H. and Li, S. and Li, S. C. and Li, W. D. and Li, X. N. and Li, X. Q. and Li, Y. F. and Li, Z. B. and Liang, H. and Lin, C. J. and Lin, G. L. and Lin, S. and Ling, J. J. and Link, J. M. and Littenberg, L. and Littlejohn, B. R. and Liu, J. C. and Liu, J. L. and Liu, J. X. and Lu, C. and Lu, H. Q. and Luk, K. B. and Ma, B. Z. and Ma, X. B. and Ma, X. Y. and Ma, Y. Q. and Mandujano, R. C. and Marshall, C. and McDonald, K. T. and McKeown, R. D. and Meng, Y. and Napolitano, J. and Naumov, D. and Naumova, E. and Nguyen, T. M. and Ochoa-Ricoux, J. P. and Olshevskiy, A. and Pan, H.-R. and Park, J. and Patton, S. and Peng, J. C. and Pun, C. S. and Qi, F. Z. and Qi, M. and Qian, X. and Raper, N. and Ren, J. and Reveco, C. Morales and Rosero, R. and Roskovec, B. and Ruan, X. C. and Steiner, H. and Sun, J. L. and Tmej, T. and Treskov, K. and Tse, W.-H. and Tull, C. E. and Viren, B. and Vorobel, V. and Wang, C. H. and Wang, J. and Wang, M. and Wang, N. Y. and Wang, R. G. and Wang, W. and Wang, W. and Wang, X. and Wang, Y. and Wang, Y. F. and Wang, Z. and Wang, Z. and Wang, Z. M. and Wei, H. Y. and Wei, L. H. and Wen, L. J. and Whisnant, K. and White, C. G. and Wong, H. L. and Worcester, E. and Wu, D. R. and Wu, F. L. and Wu, Q. and Wu, W. J. and Xia, D. M. and Xie, Z. Q. and Xing, Z. Z. and Xu, H. K. and Xu, J. L. and Xu, T. and Xue, T. and Yang, C. G. and Yang, L. and Yang, Y. Z. and Yao, H. F. and Ye, M. and Yeh, M. and Young, B. L. and Yu, H. Z. and Yu, Z. Y. and Yue, B. B. and Zavadskyi, V. and Zeng, S. and Zeng, Y. and Zhan, L. and Zhang, C. and Zhang, F. Y. and Zhang, H. H. and Zhang, J. W. and Zhang, Q. M. and Zhang, S. Q. and Zhang, X. T. and Zhang, Y. M. and Zhang, Y. X. and Zhang, Y. Y. and Zhang, Z. J. and Zhang, Z. P. and Zhang, Z. Y. and Zhao, J. and Zhao, R. Z. and Zhou, L. and Zhuang, H. L. and Zou, J. H.},
}
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