Abstract The nEXO neutrinoless double beta (0 νββ ) decay experiment is designed to use a time projection chamber and 5000 kg of isotopically enriched liquid xenon to search for the decay in 136 Xe. Progress in the detector design, paired with higher fidelity in its simulation and an advanced data analysis, based on the one used for the final results of EXO-200, produce a sensitivity prediction that exceeds the half-life of 10 28 years. Specifically, improvements have been made in the understanding of production of scintillation photons and charge as well as of their transport and reconstruction in the detector. The more detailed knowledge of the detector construction has been paired with more assays for trace radioactivity in different materials. In particular, the use of custom electroformed copper is now incorporated in the design, leading to a substantial reduction in backgrounds from the intrinsic radioactivity of detector materials. Furthermore, a number of assumptions from previous sensitivity projections have gained further support from interim work validating the nEXO experiment concept. Together these improvements and updates suggest that the nEXO experiment will reach a half-life sensitivity of 1.35 × 10 28 yr at 90% confidence level in 10 years of data taking, more »
covering the parameter space associated with the inverted neutrino mass ordering, along with a significant portion of the parameter space for the normal ordering scenario, for almost all nuclear matrix elements. The effects of backgrounds deviating from the nominal values used for the projections are also illustrated, concluding that the nEXO design is robust against a number of imperfections of the model. « less
Liu, Jia; Liu, Zhen; Wang, Lian-Tao; Wang, Xiao-Ping(
, Journal of High Energy Physics)
A bstract The search for long-lived particles (LLP) is an exciting physics opportunity in the upcoming runs of the Large Hadron Collider. In this paper, we focus on a new search strategy of using the High Granularity Calorimeter (HGCAL), part of the upgrade of the CMS detector, in such searches. In particular, we demonstrate that the high granularity of the calorimeter allows us to see “shower tracks” in the calorimeter, and can play a crucial role in identifying the signal and suppressing the background. We study the potential reach of the HGCAL using a signal model in which the Standardmore »Model Higgs boson decays into a pair of LLPs, h → XX . After carefully estimating the Standard Model QCD and the misreconstructed fake-track backgrounds, we give the projected reach for both an existing vector boson fusion trigger and a novel displaced-track-based trigger. Our results show that the best reach for the Higgs decay branching ratio, BR( h → XX ), in the vector boson fusion channel is about $$ \mathcal{O} $$ O (10 − 4 ) with lifetime cτ X ∼ 0 . 1–1 meters, while for the gluon gluon fusion channel it is about $$ \mathcal{O} $$ O (10 − 5 –10 − 6 ) for similar lifetimes. For longer lifetime cτ X ∼ 10 3 meters, our search could probe BR( h → XX ) down to a few × 10 − 4 (10 − 2 ) in the gluon gluon fusion (vector boson fusion) channels, respectively. In comparison with these previous searches, our new search shows enhanced sensitivity in complementary regions of the LLP parameter space. We also comment on many improvements can be implemented to further improve our proposed search.« less
Adams, D. Q.; Alduino, C.; Alfonso, K.; Avignone, F. T.; Azzolini, O.; Bari, G.; Bellini, F.; Benato, G.; Beretta, M.; Biassoni, M.; et al(
, Nature)
Abstract The possibility that neutrinos may be their own antiparticles, unique among the known fundamental particles, arises from the symmetric theory of fermions proposed by Ettore Majorana in 1937 1 . Given the profound consequences of such Majorana neutrinos, among which is a potential explanation for the matter–antimatter asymmetry of the universe via leptogenesis 2 , the Majorana nature of neutrinos commands intense experimental scrutiny globally; one of the primary experimental probes is neutrinoless double beta (0 νββ ) decay. Here we show results from the search for 0 νββ decay of 130 Te, using the latest advanced cryogenic calorimetersmore »with the CUORE experiment 3 . CUORE, operating just 10 millikelvin above absolute zero, has pushed the state of the art on three frontiers: the sheer mass held at such ultralow temperatures, operational longevity, and the low levels of ionizing radiation emanating from the cryogenic infrastructure. We find no evidence for 0 νββ decay and set a lower bound of the process half-life as 2.2 × 10 25 years at a 90 per cent credibility interval. We discuss potential applications of the advances made with CUORE to other fields such as direct dark matter, neutrino and nuclear physics searches and large-scale quantum computing, which can benefit from sustained operation of large payloads in a low-radioactivity, ultralow-temperature cryogenic environment.« less
Hussain, Mir Zaman; Hamilton, Stephen; Robertson, G. Philip; Basso, Bruno(
)
Abstract
Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016
using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management.
Methods
Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements.
Other
Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1. annual precip_drainage 2. biomass_corn, perennial grasses 3. biomass_poplar 4. annual N leaching _vol-wtd conc 5. Summary_N leached 6. annual DOC leachin_vol-wtd conc 7. growing season length 8. correlation_nh4 VS no3 9. correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate Description year year of the observation crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G precipitation during growing period (milliMeter) precip_NG precipitation during non-growing period (milliMeter) drainage_G drainage during growing period (milliMeter) drainage_NG drainage during non-growing period (milliMeter) 2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Variate Description year year of the observation date day of the observation (mm/dd/yyyy) crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate each crop has four replicated plots, R1, R2, R3 and R4 station stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction Fraction of biomass biomass_plot biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate Description year year of the observation method methods of poplar biomass sampling date day of the observation (mm/dd/yyyy) replicate each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground poplar diameter (milliMeter) at the ground diameter_at_15cm poplar diameter (milliMeter) at 15 cm height biomass_tree biomass per plot (Grams_Per_Tree) biomass_ha biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year year of the observation replicate each crop has four replicated plots, R1, R2, R3 and R4 no3 leached annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc. Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc. Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year year of the observation replicate each crop has four replicated plots, R1, R2, R3 and R4 doc leached annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc. volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar). Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year year of the observation growing season length growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date date of the observation (mm/dd/yyyy) replicate each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc nh4 concentration (milliGrams_N_Per_Liter) no3 conc no3 concentration (milliGrams_N_Per_Liter) 9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate Description crop “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year year of the observation don don concentration (milliGrams_N_Per_Liter) no3 no3 concentration (milliGrams_N_Per_Liter) doc doc concentration (milliGrams_Per_Liter) More>>
Double-beta decays of 100Mo from the 6.0195-year exposure of a 6.914 kg high-purity sample were recorded by the NEMO-3 experiment that searched for neutrinoless double-beta decays. These ultra-rare transitions to 100Ru have a half-life of approximately 7 1018 years, and have been used to conduct the rst ever search for periodic variations of this decay mode. The Lomb-Scargle periodogram technique, and its error-weighted extension, were employed to look for periodic modulations of the half-life. Monte Carlo modeling was used to study the modulation sensitivity of the data over a broad range of amplitudes and frequencies. Data show no evidencemore »of modulations with amplitude greater than 2.5% in the frequency range of 0:33225 y1 to 365:25 y1.« less
Dagon, Katherine; Sanderson, Benjamin M.; Fisher, Rosie A.; Lawrence, David M.(
, Advances in Statistical Climatology, Meteorology and Oceanography)
Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus onmore »parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.« less
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Adhikari, G, Al Kharusi, S, Angelico, E, Anton, G, Arnquist, I J, Badhrees, I, Bane, J, Belov, V, Bernard, E P, Bhatta, T, Bolotnikov, A, Breur, P A, Brodsky, J P, Brown, E, Brunner, T, Caden, E, Cao, G F, Cao, L, Chambers, C, Chana, B, Charlebois, S A, Chernyak, D, Chiu, M, Cleveland, B, Collister, R, Czyz, S A, Dalmasson, J, Daniels, T, Darroch, L, DeVoe, R, Di Vacri, M L, Dilling, J, Ding, Y Y, Dolgolenko, A, Dolinski, M J, Dragone, A, Echevers, J, Elbeltagi, M, Fabris, L, Fairbank, D, Fairbank, W, Farine, J, Ferrara, S, Feyzbakhsh, S, Fu, Y S, Gallina, G, Gautam, P, Giacomini, G, Gillis, W, Gingras, C, Goeldi, D, Gornea, R, Gratta, G, Hardy, C A, Harouaka, K, Heffner, M, Hoppe, E W, House, A, Iverson, A, Jamil, A, Jewell, M, Jiang, X S, Karelin, A, Kaufman, L J, Kotov, I, Krücken, R, Kuchenkov, A, Kumar, K S, Lan, Y, Larson, A, Leach, K G, Lenardo, B G, Leonard, D S, Li, G, Li, S, Li, Z, Licciardi, C, Lindsay, R, MacLellan, R, Mahtab, M, Martel-Dion, P, Masbou, J, Massacret, N, McElroy, T, McMichael, K, Peregrina, M Medina, Michel, T, Mong, B, Moore, D C, Murray, K, Nattress, J, Natzke, C R, Newby, R J, Ni, K, Nolet, F, Nusair, O, Ondze, J C, Odgers, K, Odian, A, Orrell, J L, Ortega, G S, Overman, C T, Parent, S, Perna, A, Piepke, A, Pocar, A, Pratte, J-F, Priel, N, Radeka, V, Raguzin, E, Ramonnye, G J, Rao, T, Rasiwala, H, Rescia, S, Retière, F, Ringuette, J, Riot, V, Rossignol, T, Rowson, P C, Roy, N, Saldanha, R, Sangiorgio, S, Shang, X, Soma, A K, Spadoni, F, Stekhanov, V, Sun, X L, Tarka, M, Thibado, S, Tidball, A, Todd, J, Totev, T, Triambak, S, Tsang, R H, Tsang, T, Vachon, F, Veeraraghavan, V, Viel, S, Vivo-Vilches, C, Vogel, P, Vuilleumier, J-L, Wagenpfeil, M, Wager, T, Walent, M, Wamba, K, Wang, Q, Wei, W, Wen, L J, Wichoski, U, Wilde, S, Worcester, M, Wu, S X, Wu, W H, Wu, X, Xia, Q, Yan, W, Yang, H, Yang, L, Zeldovich, O, Zhao, J, and Ziegler, T. nEXO: neutrinoless double beta decay search beyond 10 28 year half-life sensitivity. Retrieved from https://par.nsf.gov/biblio/10331283. Journal of Physics G: Nuclear and Particle Physics 49.1 Web. doi:10.1088/1361-6471/ac3631.
Adhikari, G, Al Kharusi, S, Angelico, E, Anton, G, Arnquist, I J, Badhrees, I, Bane, J, Belov, V, Bernard, E P, Bhatta, T, Bolotnikov, A, Breur, P A, Brodsky, J P, Brown, E, Brunner, T, Caden, E, Cao, G F, Cao, L, Chambers, C, Chana, B, Charlebois, S A, Chernyak, D, Chiu, M, Cleveland, B, Collister, R, Czyz, S A, Dalmasson, J, Daniels, T, Darroch, L, DeVoe, R, Di Vacri, M L, Dilling, J, Ding, Y Y, Dolgolenko, A, Dolinski, M J, Dragone, A, Echevers, J, Elbeltagi, M, Fabris, L, Fairbank, D, Fairbank, W, Farine, J, Ferrara, S, Feyzbakhsh, S, Fu, Y S, Gallina, G, Gautam, P, Giacomini, G, Gillis, W, Gingras, C, Goeldi, D, Gornea, R, Gratta, G, Hardy, C A, Harouaka, K, Heffner, M, Hoppe, E W, House, A, Iverson, A, Jamil, A, Jewell, M, Jiang, X S, Karelin, A, Kaufman, L J, Kotov, I, Krücken, R, Kuchenkov, A, Kumar, K S, Lan, Y, Larson, A, Leach, K G, Lenardo, B G, Leonard, D S, Li, G, Li, S, Li, Z, Licciardi, C, Lindsay, R, MacLellan, R, Mahtab, M, Martel-Dion, P, Masbou, J, Massacret, N, McElroy, T, McMichael, K, Peregrina, M Medina, Michel, T, Mong, B, Moore, D C, Murray, K, Nattress, J, Natzke, C R, Newby, R J, Ni, K, Nolet, F, Nusair, O, Ondze, J C, Odgers, K, Odian, A, Orrell, J L, Ortega, G S, Overman, C T, Parent, S, Perna, A, Piepke, A, Pocar, A, Pratte, J-F, Priel, N, Radeka, V, Raguzin, E, Ramonnye, G J, Rao, T, Rasiwala, H, Rescia, S, Retière, F, Ringuette, J, Riot, V, Rossignol, T, Rowson, P C, Roy, N, Saldanha, R, Sangiorgio, S, Shang, X, Soma, A K, Spadoni, F, Stekhanov, V, Sun, X L, Tarka, M, Thibado, S, Tidball, A, Todd, J, Totev, T, Triambak, S, Tsang, R H, Tsang, T, Vachon, F, Veeraraghavan, V, Viel, S, Vivo-Vilches, C, Vogel, P, Vuilleumier, J-L, Wagenpfeil, M, Wager, T, Walent, M, Wamba, K, Wang, Q, Wei, W, Wen, L J, Wichoski, U, Wilde, S, Worcester, M, Wu, S X, Wu, W H, Wu, X, Xia, Q, Yan, W, Yang, H, Yang, L, Zeldovich, O, Zhao, J, & Ziegler, T. nEXO: neutrinoless double beta decay search beyond 10 28 year half-life sensitivity. Journal of Physics G: Nuclear and Particle Physics, 49 (1). Retrieved from https://par.nsf.gov/biblio/10331283. https://doi.org/10.1088/1361-6471/ac3631
Adhikari, G, Al Kharusi, S, Angelico, E, Anton, G, Arnquist, I J, Badhrees, I, Bane, J, Belov, V, Bernard, E P, Bhatta, T, Bolotnikov, A, Breur, P A, Brodsky, J P, Brown, E, Brunner, T, Caden, E, Cao, G F, Cao, L, Chambers, C, Chana, B, Charlebois, S A, Chernyak, D, Chiu, M, Cleveland, B, Collister, R, Czyz, S A, Dalmasson, J, Daniels, T, Darroch, L, DeVoe, R, Di Vacri, M L, Dilling, J, Ding, Y Y, Dolgolenko, A, Dolinski, M J, Dragone, A, Echevers, J, Elbeltagi, M, Fabris, L, Fairbank, D, Fairbank, W, Farine, J, Ferrara, S, Feyzbakhsh, S, Fu, Y S, Gallina, G, Gautam, P, Giacomini, G, Gillis, W, Gingras, C, Goeldi, D, Gornea, R, Gratta, G, Hardy, C A, Harouaka, K, Heffner, M, Hoppe, E W, House, A, Iverson, A, Jamil, A, Jewell, M, Jiang, X S, Karelin, A, Kaufman, L J, Kotov, I, Krücken, R, Kuchenkov, A, Kumar, K S, Lan, Y, Larson, A, Leach, K G, Lenardo, B G, Leonard, D S, Li, G, Li, S, Li, Z, Licciardi, C, Lindsay, R, MacLellan, R, Mahtab, M, Martel-Dion, P, Masbou, J, Massacret, N, McElroy, T, McMichael, K, Peregrina, M Medina, Michel, T, Mong, B, Moore, D C, Murray, K, Nattress, J, Natzke, C R, Newby, R J, Ni, K, Nolet, F, Nusair, O, Ondze, J C, Odgers, K, Odian, A, Orrell, J L, Ortega, G S, Overman, C T, Parent, S, Perna, A, Piepke, A, Pocar, A, Pratte, J-F, Priel, N, Radeka, V, Raguzin, E, Ramonnye, G J, Rao, T, Rasiwala, H, Rescia, S, Retière, F, Ringuette, J, Riot, V, Rossignol, T, Rowson, P C, Roy, N, Saldanha, R, Sangiorgio, S, Shang, X, Soma, A K, Spadoni, F, Stekhanov, V, Sun, X L, Tarka, M, Thibado, S, Tidball, A, Todd, J, Totev, T, Triambak, S, Tsang, R H, Tsang, T, Vachon, F, Veeraraghavan, V, Viel, S, Vivo-Vilches, C, Vogel, P, Vuilleumier, J-L, Wagenpfeil, M, Wager, T, Walent, M, Wamba, K, Wang, Q, Wei, W, Wen, L J, Wichoski, U, Wilde, S, Worcester, M, Wu, S X, Wu, W H, Wu, X, Xia, Q, Yan, W, Yang, H, Yang, L, Zeldovich, O, Zhao, J, and Ziegler, T.
"nEXO: neutrinoless double beta decay search beyond 10 28 year half-life sensitivity". Journal of Physics G: Nuclear and Particle Physics 49 (1). Country unknown/Code not available. https://doi.org/10.1088/1361-6471/ac3631.https://par.nsf.gov/biblio/10331283.
@article{osti_10331283,
place = {Country unknown/Code not available},
title = {nEXO: neutrinoless double beta decay search beyond 10 28 year half-life sensitivity},
url = {https://par.nsf.gov/biblio/10331283},
DOI = {10.1088/1361-6471/ac3631},
abstractNote = {Abstract The nEXO neutrinoless double beta (0 νββ ) decay experiment is designed to use a time projection chamber and 5000 kg of isotopically enriched liquid xenon to search for the decay in 136 Xe. Progress in the detector design, paired with higher fidelity in its simulation and an advanced data analysis, based on the one used for the final results of EXO-200, produce a sensitivity prediction that exceeds the half-life of 10 28 years. Specifically, improvements have been made in the understanding of production of scintillation photons and charge as well as of their transport and reconstruction in the detector. The more detailed knowledge of the detector construction has been paired with more assays for trace radioactivity in different materials. In particular, the use of custom electroformed copper is now incorporated in the design, leading to a substantial reduction in backgrounds from the intrinsic radioactivity of detector materials. Furthermore, a number of assumptions from previous sensitivity projections have gained further support from interim work validating the nEXO experiment concept. Together these improvements and updates suggest that the nEXO experiment will reach a half-life sensitivity of 1.35 × 10 28 yr at 90% confidence level in 10 years of data taking, covering the parameter space associated with the inverted neutrino mass ordering, along with a significant portion of the parameter space for the normal ordering scenario, for almost all nuclear matrix elements. The effects of backgrounds deviating from the nominal values used for the projections are also illustrated, concluding that the nEXO design is robust against a number of imperfections of the model.},
journal = {Journal of Physics G: Nuclear and Particle Physics},
volume = {49},
number = {1},
author = {Adhikari, G and Al Kharusi, S and Angelico, E and Anton, G and Arnquist, I J and Badhrees, I and Bane, J and Belov, V and Bernard, E P and Bhatta, T and Bolotnikov, A and Breur, P A and Brodsky, J P and Brown, E and Brunner, T and Caden, E and Cao, G F and Cao, L and Chambers, C and Chana, B and Charlebois, S A and Chernyak, D and Chiu, M and Cleveland, B and Collister, R and Czyz, S A and Dalmasson, J and Daniels, T and Darroch, L and DeVoe, R and Di Vacri, M L and Dilling, J and Ding, Y Y and Dolgolenko, A and Dolinski, M J and Dragone, A and Echevers, J and Elbeltagi, M and Fabris, L and Fairbank, D and Fairbank, W and Farine, J and Ferrara, S and Feyzbakhsh, S and Fu, Y S and Gallina, G and Gautam, P and Giacomini, G and Gillis, W and Gingras, C and Goeldi, D and Gornea, R and Gratta, G and Hardy, C A and Harouaka, K and Heffner, M and Hoppe, E W and House, A and Iverson, A and Jamil, A and Jewell, M and Jiang, X S and Karelin, A and Kaufman, L J and Kotov, I and Krücken, R and Kuchenkov, A and Kumar, K S and Lan, Y and Larson, A and Leach, K G and Lenardo, B G and Leonard, D S and Li, G and Li, S and Li, Z and Licciardi, C and Lindsay, R and MacLellan, R and Mahtab, M and Martel-Dion, P and Masbou, J and Massacret, N and McElroy, T and McMichael, K and Peregrina, M Medina and Michel, T and Mong, B and Moore, D C and Murray, K and Nattress, J and Natzke, C R and Newby, R J and Ni, K and Nolet, F and Nusair, O and Ondze, J C and Odgers, K and Odian, A and Orrell, J L and Ortega, G S and Overman, C T and Parent, S and Perna, A and Piepke, A and Pocar, A and Pratte, J-F and Priel, N and Radeka, V and Raguzin, E and Ramonnye, G J and Rao, T and Rasiwala, H and Rescia, S and Retière, F and Ringuette, J and Riot, V and Rossignol, T and Rowson, P C and Roy, N and Saldanha, R and Sangiorgio, S and Shang, X and Soma, A K and Spadoni, F and Stekhanov, V and Sun, X L and Tarka, M and Thibado, S and Tidball, A and Todd, J and Totev, T and Triambak, S and Tsang, R H and Tsang, T and Vachon, F and Veeraraghavan, V and Viel, S and Vivo-Vilches, C and Vogel, P and Vuilleumier, J-L and Wagenpfeil, M and Wager, T and Walent, M and Wamba, K and Wang, Q and Wei, W and Wen, L J and Wichoski, U and Wilde, S and Worcester, M and Wu, S X and Wu, W H and Wu, X and Xia, Q and Yan, W and Yang, H and Yang, L and Zeldovich, O and Zhao, J and Ziegler, T},
}
