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At least 271 records · Page 15

Evidence for compensatory photosynthetic and yield response of soybeans to aphid herbivory

The soybean aphid, Aphis glycines Matsumura, an exotic species in North America that has been detected in 21 U.S. states and Canada, is a major pest for soybean that can reduce maximum photosynthetic capacity and yields. Our existing knowledge is based on relatively few studies that do not span a wide variety of environmental conditions, and often focus on relatively high and damaging population pressure. We examined the effects of varied populations and duration of soybean aphids on soybean photosynthetic rates and yield in two experiments. In a 2011 field study, we found that plants with low cumulative aphid days (CAD, less than 2,300) had higher yields than plants not experiencing significant aphid pressure, suggesting a compensatory growth response to low aphid pressure. This response did not hold at higher CAD, and yields declined. In a 2013 controlled-environment greenhouse study, soybean plants were well-watered and fertilized with nitrogen (N), and aphid populations were manipulated to reach moderate to high levels (8,000–50,000 CAD). Plants tolerated these population levels when aphids were introduced during the vegetative or reproductive phenological stages of the plant, showing no significant reduction in yield. Leaf N concentration and CAD were positively and significantly correlated with increasing ambient photosynthetic rates. Our findings suggest that, given the right environmental conditions, modern soybean plants can withstand higher aphid pressure than previously assumed. Moreover, soybean plants also responded positively through a compensatory photosynthetic effect to moderate population pressure, contributing to stable or increased yield.

54 ENVIRONMENTAL SCIENCES↗

Functional Gene Discovery and Characterization of Genes and Alleles Affecting Wood Biomass Yield and Quality in Populus

Adoption of biofuels as economically and environmentally viable alternative to fossil fuels would require development of specialized bioenergy varieties. A major goal in the breeding of such varieties is the improvement of lignocellulosic biomass yield and quality. These are complex traits and understanding the underpinning molecular mechanism can assist and accelerate their improvement. This is particularly important for tree bioenergy crops like poplars (species and hybrids from the genus Populus), for which breeding progress is extremely slow due to long generation cycles. A variety of approaches have been already undertaken to better understand the molecular bases of biomass yield and quality in poplar. An obvious void in these undertakings has been the application of mutagenesis. Mutagenesis has been instrumental in the discovery and characterization of many plant traits including such that affect biomass yield and quality. In this proposal we use activation tagging to discover genes that can significantly affect biomass associated traits directly in poplar, a premier bioenergy crop. We screened a population of 5,000 independent poplar activation tagging lines under greenhouse conditions for a battery of biomass yield traits. These same plants were then analyzed for changes in wood chemistry using pyMBMS. As a result of these screens we have identified nearly 800 mutants, which are significantly (P<0.05) different when compared to wild type. Of these majority (~700) are affected in one of ten different biomass yield traits and 100 in biomass quality traits (e.g., lignin, S/G ration and C6/C5 sugars). We successfully recovered the position of the tag in approximately 130 lines, showed activation in nearly half of them and performed recapitulation experiments with 20 genes prioritized by the significance of the phenotype. Recapitulation experiments are still ongoing for many of the genes but the results are encouraging. For example, we have shown successful recapitulation for a fascilin-like gene that when overexpressed increase many biomass-yield associated traits. Genes discovered through activation tagging showed polymorphisms in P. trichocarpa association mapping population linked to the traits modified by the activation tagging. This suggests that putative alleles that are associated with improvement of the trait o interest can be discovered and used in marker associated selection. This will significantly simplify and accelerate the breeding efforts.

09 BIOMASS FUELS↗

The Effects of Herbarium Specimen Characteristics on Short-Read NGS Sequencing Success in Nearly 8000 Specimens: Old, Degraded Samples Have Lower DNA Yields but Consistent Sequencing Success

Phylogenetic datasets are now commonly generated using short-read sequencing technologies unhampered by degraded DNA, such as that often extracted from herbarium specimens. The compatibility of these methods with herbarium specimens has precipitated an increase in broad sampling of herbarium specimens for inclusion in phylogenetic studies. Understanding which sample characteristics are predictive of sequencing success can guide researchers in the selection of tissues and specimens most likely to yield good results. Multiple recent studies have considered the relationship between sample characteristics and DNA yield and sequence capture success. Here we report an analysis of the relationship between sample characteristics and sequencing success for nearly 8,000 herbarium specimens. This study, the largest of its kind, is also the first to include a measure of specimen quality (“greenness”) as a predictor of DNA sequencing success. We found that taxonomic group and source herbarium are strong predictors of both DNA yield and sequencing success and that the most important specimen characteristics for predicting success differ for DNA yield and sequencing: greenness was the strongest predictor of DNA yield, and age was the strongest predictor of proportion-on-target reads recovered. Surprisingly, the relationship between age and proportion-on-target reads is the inverse of expectations; older specimens performed slightly better in our capture-based protocols. We also found that DNA yield itself is not a strong predictor of sequencing success. Most literature on DNA sequencing from herbarium specimens considers specimen selection for optimal DNA extraction success, which we find to be an inappropriate metric for predicting success using next-generation sequencing technologies.

54 ENVIRONMENTAL SCIENCES↗

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

54 ENVIRONMENTAL SCIENCES↗

Mapping intra-field yield variation using high resolution satellite imagery to integrate bioenergy and environmental stewardship in an agricultural watershed

Biofuels are important alternatives for meeting our future energy needs. Successful bioenergy crop production requires maintaining environmental sustainability and minimum impacts on current net annual food, feed, and fiber production. The objectives of this study were to: (1) determine under-productive areas within an agricultural field in a watershed using a single date; high resolution remote sensing and (2) examine impacts of growing bioenergy crops in the under-productive areas using hydrologic modeling in order to facilitate sustainable landscape design. Normalized difference indices (NDIs) were computed based on the ratio of all possible two-band combinations using the RapidEye and the National Agricultural Imagery Program images collected in summer 2011. A multiple regression analysis was performed using 10 NDIs and five RapidEye spectral bands. The regression analysis suggested that the red and near infrared bands and NDI using red-edge and near infrared that is known as the red-edge normalized difference vegetation index (RENDVI) had the highest correlation (R 2 = 0.524) with the reference yield. Although predictive yield map showed striking similarity to the reference yield map, the model had modest correlation; thus, further research is needed to improve predictive capability for absolute yields. Forecasted impact using the Soil and Water Assessment Tool model of growing switchgrass ( Panicum virgatum ) on under-productive areas based on corn yield thresholds of 3.1, 4.7, and 6.3 Mg·ha -1 showed reduction of tile NO 3 -N and sediment exports by 15.9%–25.9% and 25%–39%, respectively. Corresponding reductions in water yields ranged from 0.9% to 2.5%. While further research is warranted, the study demonstrated the integration of remote sensing and hydrologic modeling to quantify the multifunctional value of projected future landscape patterns in a context of sustainable bioenergy crop production.

09 BIOMASS FUELS↗

Comments on extracting the resonance strength parameter from yield data

The F(α,n) reaction is the focus of on-going research in part because it is an important source of neutrons in the nuclear fuel cycle which can be exploited to assay nuclear materials, especially uranium in the form of UF 6 . At the present time there remains some considerable uncertainty (of the order of ± 20%) in the thick target integrated over angle (α,n) yield from 19 F (100% natural abundance) and its compounds as discussed. An important thin target cross-section measurement is that of Wrean and Kavanagh who explore the region from below threshold (2.36 MeV) to approximately 3.1 MeV with fine energy resolution. Integration of their cross-section data over the slowing down history of a stopping α-particle allows the thick target yield to be calculated for incident energies up to 3.1 MeV. This trend can then be combined with data from other sources to obtain a thick target yield curve over the wider range of interest to the fuel cycle (roughly threshold to 10 MeV to include all relevant α-emitters). To estimate the thickness of the CaF 2 target they used, Wrean and Kavanagh separately measured the integrated yield of the 6.129 MeV γ-rays from the resonance at 340.5 keV (laboratory α-particle kinetic energy) in the 19 F(p,αγ) reaction. To interpret the data they adopted a resonance strength parameter of (22.3 ± 0.8) eV based on a determination by Becker et al. The value and its uncertainty directly affects the thickness estimate and the extracted (α,n) cross-section values. In their citation to Becker et al's work, Wrean and Kavanagh comment that they did not make use of an alternative value of (23.7±1.0) eV reported by Croft because they were unable to reproduce the value from the data given in that paper. The value they calculated for the resonance strength from the thick target yield given by Croft was 21.4 eV. The purpose of this communication is to revisit the paper by Croft published in this journal and specifically to explain the origin of the reported resonance strength. Fortunately the original notes spanning the period 12 January 1988 to 16 January 1990 were available to consult. Finally, in hindsight there is certainly a case of excessive brevity to rectify. In essence the step requiring explanation is how to compute the resonance strength, ω γ , from the reported thick target resonance yield Y.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Understanding the Impacts of Biomass Blending on the Uncertainty of Hydrolyzed Sugar Yield from a Stochastic Perspective

Feedstock price and availability are key challenges for biorefinery development and biomass blending has been suggested as a route to overcome these limitations. However, the impacts of feedstock blending on the variability of yields of hydrolyzed sugar remained unclear. This study quantifies the uncertainties in the sugar yields from the blends of corn stover, switchgrass, and grass clippings by considering both variable and model uncertainties. The results indicate that feedstock blending reduces the uncertainties in sugar yields and delivers feedstock of more uniform quality. A further investigation on xxx demonstrates that the reduction in overall uncertainty is achieved by reduced compositional uncertainties of blended feedstock. Based on these results, the optimization of blending ratios was performed with respect to three objectives: (1) probability of meeting the sugar yield target; (2) expected sugar yields; and (3) sugar yield per unit feedstock expense, while satisfying constraints of feedstock availability and cost. In conclusion, the optimization method developed in this study, is readily applied to other combinations of feedstocks, biofuels production processes, and constraints.

09 BIOMASS FUELS↗

Changes in Wood Biomass and Crop Yields in Response to Projected CO 2 , O 3 , Nitrogen Deposition, and Climate

As the world's population increases, so will the demand for food and timber resources. Though carbon dioxide (CO 2 ) fertilization and, to a lesser extent, nitrogen (N) deposition are expected to increase future resource production, changes in ozone (O 3 ) and climate have the potential to decrease production due to increased phytotoxic damage, drought, and heat stress. To determine how crop and timber production may change in the future, we use the Community Land Model version 4.5 with prognostic crops to simulate responses of wood biomass and crop yields to CO 2 , O 3 , N deposition, and climate under Representative Concentration Pathway 8.5 forcings. Generally, rising CO 2 increases wood biomass and crop yields, while projected climate change causes decreases. Small projected changes in O 3 and N deposition do not strongly affect yields, though additional research is needed on future O 3 and N deposition trends and impacts. By the end of the 21st century, global wood biomass increases by ~16% due to the dominating impact of CO 2 . The positive effect of CO 2 on future crop yields is muted by the negative impacts of climate, with a ~5% net global increase. Future projections suggest that rice and wheat yields typically increase under the combination of future forcings, whereas soy and corn yields are regionally variable. While short-term resource management strategies can benefit from planting heat-tolerant species and cultivars, technological advances and intensification, among other management strategies not included here, must be employed to meet the future demand for these resources.

54 ENVIRONMENTAL SCIENCES↗

Impoverishing Roots Will Improve Wheat Yield and Profitability Through Increased Water and Nitrogen Use Efficiencies

We report that more than a 60% increase in crop production is required by the 2050's to feed a growing world population. Understanding how plant functional traits and field management affect crop yields has the potential to improve agricultural productivity, minimize economic and environmental losses, and maximize food security. We explored the influence of winter wheat root characteristics and management on winter wheat growth, yield, and profit using a mechanistic and well-tested ecosystem and crop model, ecosys. We applied and further tested ecosys at an agricultural farm growing winter wheat in Ardmore, Oklahoma, United States. The model accurately predicted observed shoot carbon (R 2 = 0.95), soil moisture (R 2 = 0.67), soil temperature (R 2 = 0.91), and yield (percent error = 17%). Numerical optimization experiments were conducted to explore potential improvements of winter wheat yield and profit by modifying root characteristics, including root radius and root:shoot carbon transfer conductance, and fertilizer inputs. Our results show the potential for simultaneously improving winter wheat yields and profits. The optimum conditions were found to be in the range of root radius between 0.1 and 0.3 mm, carbon transfer conductance between 0.004 and 0.01 h -1 , and the currently applied fertilizer rate of 112 kg ha -1 . Under these conditions, improvements in yields and profits of up to approximately 25% and 110%, respectively, were modeled compared to those under baseline root traits. These improvements were achieved by impoverishing root structures, thereby increasing nutrient allocation to grains. Our results also demonstrate and motivate model structures that integrate the complex network of plant physiology, soil nutrient biogeochemistry, hydrology, and management.

54 ENVIRONMENTAL SCIENCES↗

Climate variation explains a third of global crop yield variability

Many studies have examined the role of mean climate change in agriculture, but an understanding of the influence of inter-annual climate variations on crop yields in different regions remains elusive. We use detailed crop statistics time series for B13,500 political units to examine how recent climate variability led to variations in maize, rice, wheat and soybean crop yields worldwide. While some areas show no significant influence of climate variability, in substantial areas of the global breadbaskets, 460% of the yield variability can be explained by climate variability. Globally, climate variability accounts for roughly a third (B32–39%) of the observed yield variability. Our study uniquely illustrates spatial patterns in the relationship between climate variability and crop yield variability, highlighting where variations in temperature, precipitation or their interaction explain yield variability. We discuss key drivers for the observed variations to target further research and policy interventions geared towards buffering future crop production from climate variability

54 ENVIRONMENTAL SCIENCES↗

Fission Product Yield Modeling and Evaluation

Although independent and cumulative fission product yields have been a part of evaluated libraries for decades, there have been few updates over the years. The fission product yield sub-library in the ENDF/B-VIII.0 library is still largely based on the evaluation of England and Rider from the mid-90’s, with only more recent updates to the energy dependence of 239 Pu below 2 MeV and fixes to isomeric states and missing fission products. Over the past several years, there have been a wealth of new measurements of independent and cumulative fission product yields, particularly those with short half-lives, and there have been significant improvements in the modeling of prompt and delayed fission observables. Here, we describe recent progress in the improvement of fission product yield calculations, using the BeoH code and the underlying Hauser Fesh-bach Fission Fragment Decay (HF 3 D) model, developed at Los Alamos National Laboratory. We will describe our recent calculations for consistent prompt and delayed fission observables for major and minor actinides, including new work investigating isomeric ratios. We will detail the ongoing evaluation process for energy-dependent fission product yields from thermal up to 20 MeV incident neutron energy and some validation work that has been performed for these new fission product yield calculations. Additionally, we will discuss future perspectives of this work, highlighting the need for additional data.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Yield degradation in inertial-confinement-fusion implosions due to shock-driven kinetic fuel-species stratification and viscous heating

Anomalous thermonuclear yield degradation (i.e., that not describable by single-fluid radiation hydrodynamics) in Inertial Confinement Fusion (ICF) implosions is ubiquitously observed in both Omega and National Ignition experiments. Multiple experimental and theoretical studies have been carried out to investigate the origin of such a degradation. Relative concentration changes of fuel-ion species, as well as kinetically enhanced viscous heating, have been among possible explanations proposed for certain classes of ICF experiments. In this study, we investigate the role of such kinetic plasma effects in detail. To this end, we use the iFP code to perform multi-species ion Vlasov-Fokker-Planck simulations of ICF capsule implosions with the fuel comprising various hydrodynamically equivalent mixtures of deuterium (D) and helium-3 (3He), as in the original. We employ the same computational setup as in O. Larroche, which was the first to simulate the experiments kinetically. However, unlike the Larroche study, and in partial agreement with experimental data, we find a systematic yield degradation in multi-species simulations versus averaged-ion simulations when the D-fuel fraction is decreased. This yield degradation originates in the fuel-ion species stratification induced by plasma shocks, which imprints the imploding system and results in the relocation of the D ions from the core of the capsule to its periphery, thereby reducing the yield relative to a non-separable averaged-ion case. By comparing yields from the averaged-ion kinetic simulations and from the hydrodynamic scaling, we also observe yield variations associated with ion kinetic effects other than fuel-ion stratification, such as ion viscous heating, which is typically neglected in hydrodynamic implosions' simulations. Since our kinetic simulations are driven by hydrodynamic boundary conditions at the fuel-ablator interface, they cannot capture the effects of ion viscosity on the capsule compression, or effects associated with the interface, which are expected to be important. As a result, studies of such effects are left for future work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

The nonlinear rheology of complex yield stress foods

Many foods have a yield stress that allows them to retain a desired shape at rest, but transition into a viscous fluid when being served or consumed. The determination of the yield stress of the food dictates how the foods are formed and packaged, how they are served, and how they are perceived when being eaten. Oscillatory shearing provides an ideal test protocol to map the rheology across a range of time and flow strength scales. We couple oscillatory shearing and an iterative recovery procedure to show that the yielding process is a continuous transition for two common yield stress foods. We show that unrecoverable processes from oscillatory tests are equivalent to the steady shear flow behavior. We show that this yielding behavior can be well approximated by a recently published model that treats yield stress materials as continuous viscoelastic fluids with a rate-dependent relaxation time and has parameters that can be obtained from the linear viscoelastic oscillatory frequency sweep and the steady shear flow curve.

Griebler, James J.↗

A new neutron time-of-flight detector for yield and ion-temperature measurements at the OMEGA Laser Facility

A new neutron time-of-flight (nTOF) detector for deuterium–deuterium (DD)-fusion yield and ion-temperature measurements was designed, installed, and calibrated for the OMEGA Laser Facility. This detector provides an additional line of sight for DD neutron yield and ion-temperature measurements for yields exceeding 1 × 10 10 with higher precision than existing detectors. The nTOF detector consists of a 90-mm-diam, 20-mm-thick BC-422 scintillator and a gated Photek photomultiplier tube (PMT240). The PMT collects scintillating light through the 20-mm side of the scintillator without the use of a light guide. There is no lead shielding from hard x rays in order to allow the x-ray instrument response function of the detector to be measured easily. Instead, hard x-ray signals generated in implosion experiments are gated out by the PMT. The design provides a place for glass neutral-density filters between the scintillator and the PMT to avoid PMT saturation at high yields. The nTOF detector is installed in the OMEGA Target Bay along the P8A sub-port line of sight at a distance of 5.3 m from the target chamber center. In addition to DD measurements, the same detector can be used to measure the neutron yield and ion temperature from deuterium–tritium (DT) implosion targets in the 5 × 10 10 to 2 × 10 12 yield range. Here, the design details and the calibration results of this nTOF detector for both D 2 and DT implosions on OMEGA will be presented.

47 OTHER INSTRUMENTATION↗

Measurements of the 27 Al(α,n) Thick Target Yield near Threshold

In this work, we present results from direct measurements of the 27 Al(α,n) thick-target yield from laboratory incident energies E α ≈ 3 to 5 MeV, performed with the 3 HeBF 3 Giant Barrel (HeBGB) neutron detector at the Edwards Accelerator Laboratory. Our measurements have a small energy cadence in order to address discrepancies and sparseness of thick-target-yield data sets existing for this energy region. We find general agreement with existing data sets, including yields derived from cross-section data, while resolving a discrepancy between existing thick-target-yield data sets for E α ≈ 4–5 MeV. However, for E α < 3.5 MeV, our results are substantially lower than previous thick-target-yield data and somewhat larger than yields calculated from existing cross-section data. Our data complete the energy range needed for estimates of the 27 Al(α,n) contribution to neutrino and dark matter detector backgrounds and result in increased viability of 27 Al(α,n) as a plasma diagnostic tool at fusion facilities such as the National Ignition Facility.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Genomic prediction of regional-scale performance in switchgrass ( Panicum virgatum ) by accounting for genotype-by-environment variation and yield surrogate traits

Switchgrass is a potential crop for bioenergy or carbon capture schemes, but further yield improvements through selective breeding are needed to encourage commercialization. To identify promising switchgrass germplasm for future breeding efforts, we conducted multisite and multitrait genomic prediction with a diversity panel of 630 genotypes from 4 switchgrass subpopulations (Gulf, Midwest, Coastal, and Texas), which were measured for spaced plant biomass yield across 10 sites. Our study focused on the use of genomic prediction to share information among traits and environments. Specifically, we evaluated the predictive ability of cross-validation (CV) schemes using only genetic data and the training set (cross-validation 1: CV1), a subset of the sites (cross-validation 2: CV2), and/or with 2 yield surrogates (flowering time and fall plant height). We found that genotype-by-environment interactions were largely due to the north–south distribution of sites. The genetic correlations between the yield surrogates and the biomass yield were generally positive (mean height r = 0.85; mean flowering time r = 0.45) and did not vary due to subpopulation or growing region (North, Middle, or South). Genomic prediction models had CV predictive abilities of –0.02 for individuals using only genetic data (CV1), but 0.55, 0.69, 0.76, 0.81, and 0.84 for individuals with biomass performance data from 1, 2, 3, 4, and 5 sites included in the training data (CV2), respectively. To simulate a resource-limited breeding program, we determined the predictive ability of models provided with the following: 1 site observation of flowering time (0.39); 1 site observation of flowering time and fall height (0.51); 1 site observation of fall height (0.52); 1 site observation of biomass (0.55); and 5 site observations of biomass yield (0.84). The ability to share information at a regional scale is very encouraging, but further research is required to accurately translate spaced plant biomass to commercial-scale sward biomass performance.

09 BIOMASS FUELS↗

Hauser-Feshbach fission fragment de-excitation with calculated macroscopic-microscopic mass yields

The Hauser-Feshbach statistical model is applied to the de-excitation of primary fission fragments using input mass yields calculated with macroscopic-microscopic models of the potential energy surface. We test the sensitivity of the prompt fission observables to the input mass yields for two important reactions, 235 U (n th , f) and 239 Pu (n th , f) , for which good experimental data exist. General traits of the mass yields, such as the location of the peaks and their widths, can impact both the prompt neutron and γ-ray multiplicities, as well as their spectra. Specifically, we use several mass yields to determine a linear correlation between the calculated prompt neutron multiplicity $\bar{v}$ and the average heavy-fragment mass $\langle$A h $\rangle$ of the input mass yields ∂$\bar{v}$/∂ $\langle$A h $\rangle$ = ± 0.1 (n / f )/u . The mass peak width influences the correlation between the total kinetic energy of the fission fragments and the total number of prompt neutrons emitted, $\bar{v}_T$ ( TKE ) . Finally, typical biases on prompt particle observables from using calculated mass yields instead of experimental ones are δ$\bar{v}$ = 4 % for the average prompt neutron multiplicity, δ$\overline{M}_γ$ = 1% for the average prompt γ-ray multiplicity, δ$\bar{ε}$ $LAB\atop{n}$ = 1 % for the average outgoing neutron energy, δ$\bar{ε}_γ$ = 1 % for the average γ-ray energy, and δ $\langle$TKE$\rangle$ = 0.4 % for the average total kinetic energy of the fission fragments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Ionization yield measurement in a germanium CDMSlite detector using photo-neutron sources

Two photo-neutron sources, Y 88 Be 9 and Sb 124 Be 9 , have been used to investigate the ionization yield of nuclear recoils in the CDMSlite germanium detectors by the SuperCDMS collaboration. In this study, we evaluate the yield for nuclear recoil energies between 1 and 7 keV at a temperature of ~ 50 mK . We use a geant4 simulation to model the neutron spectrum assuming a charge yield model that is a generalization of the standard Lindhard model and consists of two energy dependent parameters. We perform a likelihood analysis using the simulated neutron spectrum, modeled background, and experimental data to obtain the best fit values of the yield model. The ionization yield between recoil energies of 1 and 7 keV is shown to be significantly lower than predicted by the standard Lindhard model for germanium. There is a general lack of agreement among different experiments using a variety of techniques studying the low energy range of the nuclear recoil yield, which is most critical for interpretation of direct dark matter searches. This suggests complexity in the physical process that many direct detection experiments use to model their primary signal detection mechanism and highlights the need for further studies to clarify underlying systematic effects that have not been well understood up to this point.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗