1,542 research outputs found
R-matrix analysis of ^{16}O compound nucleus reactions
No full textBackground: Over the past 60 years, a large amount of experimental nuclear data have been obtained for reactions which probe the 16O compound nucleus near the α and proton separation energies, the energy regimes most important for nuclear astrophysics. Difficulties and inconsistencies in R-matrix fits of the individual reactions prompt a more complete analysis.
Purpose: Determine the level of consistency between the wide variety of experimental data using a multiple entrance/exit channel R-matrix framework. Using a consistent set of data from multiple reaction channels, attain an improved fitting for the 15N(p,γ0)16O reaction data.
Methods: Reaction data for all available reaction channels were fit simultaneously using a multichannel R-matrix code.
Results: Over the wide range of experimental data considered, a high level of consistency was found, resulting in a single consistent R-matrix fit which described the broad level structure of 16O below Ex=13.5 MeV. The resulting fit was used to extract an improved determination of the low-energy S factor for the reactions 15N(p, γ)16O and 15N(p, α)12C.
Conclusion: The feasibility and advantages of a complete multiple entrance/exit channel R-matrix description for the broad level structure of 16O has been achieved. A future publication will investigate the possible effects of the multiple-channel analysis on the reaction 12C(α, γ)16O
Lois Engen teaching, L to R: Bonnie Speyer, Rob Speyer and Bruce DeBoer, 1947.
No full textPhoto shows Lois Engen teaching Bonnie Speyer, Rob Speyer and Bruce DeBoer to sk
Investigation of secondary γ -ray angular distributions using the N 15 (p,α1γ) C ∗ 12 reaction
No full textThe observation of secondary γ-rays provides an alternative method of measuring cross sections that populate excited final states in nuclear reactions. The angular distributions of these γ-rays also provide information on the underlying reaction mechanism. Despite the large number of data of this type in the literature, publicly available R-matrix codes do not have the ability to calculate these types of angular distributions. In this paper, the mathematical formalism derived by C. R. Brune and R. J. deBoer [Phys. Rev. C 102, 024628 (2020)2469-998510.1103/PhysRevC.102.024628] is implemented in the R-matrix code azure2 and calculations are compared with previous data from the literature for the N15(p,α1γ)C∗12 reaction. In addition, new measurements, made at the University of Notre Dame Nuclear Science Laboratory using the Hybrid Array of Gamma Ray Detectors (HAGRiD), are reported that span the energy range from Ep=0.88 MeV to Ep=4.0MeV. Excellent agreement between the data and the phenomenological fit is obtained up to the limit of the previous fit at Ep=2.0MeV and the R-matrix fit is extended from Ex≈13.5 MeV up to Ex≈15.3 MeV, where N15+p and C12+α reactions are fit simultaneously for the first time. An excellent reproduction of the N15(p,α1γ)C∗12 and C12(α,α)C12 data is achieved, but inconsistencies and difficulty in fitting other data are encountered and discussed
Digging through the dirt: a general method for abstract discrete state estimation with limited prior knowledge
No full textAutonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system dynamics. An example of a typical ADS problem that is used in this thesis is that of a wheeled robot driving through puddles of mud that, when entered, alters the velocity of the robot. When the robot is in such a puddle, it is in an ADS 'mud', and when it is not, it is in an ADS 'free'. ADS can be indirectly inferred through the analysis of lower-level data such as the velocity of the robot. The goal of this thesis is to design a general abstract discrete state estimator (ADSE) operating with limited prior knowledge. An ADSE is a hierarchical system for detecting changes in ADS. The ADSE should be general; applicable to multiple ADSE problems. The ADSE should further operate under limited prior knowledge: only assuming that the amount of ADS and the ADS that describes the regular operation are known. The basis for the ADSE designed in this thesis is a Gaussian hidden Markov model (GHMM), a hidden Markov model enhanced with Gaussian emissions. Randomly generated experiments are done on a simple but general ADSE problem. Two unsupervised learning methods derived from Expectation Maximization are evaluated, namely Baum-Welch (BW) and forward extraction (FWE). FWE is introduced in this thesis and is a simpler implementation of Viterbi extraction, leveraging assumptions of ADSE to in theory gain computational efficiency. We found that both BW and FWE exhibit superior performance compared to a likelihood-based baseline estimator when the maximum score of the learning curve is considered. When the final score is considered, in some cases, FWE displays a deteriorating learning curve, resulting in worse final scores compared to the baseline. Furthermore, it was found that the lower the overlap coefficient (therefore the less similar the ADS), the higher the maximum reached score. It was further shown that BW exhibits better convergence than FWE to the true model parameters. Besides this, FWE obtained comparable or in some cases even superior scores compared to BW. In general, from the results, the diversity of the experiments conducted, and the assumptions made we can conclude that the GHMM can be a general method for an ADSE with limited prior knowledge. To quantify the suitability of the GHMM for ADSE, further research should include the evaluation of different ADSE methods on the same problem. There exists a tradeoff between the lower computational cost FWE and the more stable but more computationally intensive BW learning. Therefore, future research can include a combination of these methods. Other extensions include extending the GHMM to a Gaussian mixture hidden Markov model to allow for the modeling of more complex distributions, or the application to multiple states or a changing environment.https://github.com/Wouter-deBoer/adseMechanical Engineering | Vehicle Engineering | Cognitive Robotic
Elastic scattering of protons from 15N
No full textBackground: Resonances observed through elastic scattering of protons on 15N can provide information about the partial widths, spin parities, and energies of excited states in 16O near the proton separation energy. This is the same energy region important for the nuclear astrophysics reactions 15N(p,γ)16O and 15N(p,α)12C. While previous measurements have been made, they are limited in scope, especially in their angular coverage. Purpose: Obtain additional 15N(p,p)15N reaction data which can be used in a global multiple-channel R-matrix analysis of the 16O compound nucleus in order to better constrain the level parameters of states which contribute to the reaction 15N(p,γ)16O. Methods: Measure the excitation functions of 15N(p,p)15N over an energy range from Ep = 0.6 to 1.8 MeV at laboratory angles of 90∘, 105∘, 135∘, 150∘, and 165∘. The reaction 15N(p,α0)12C was measured concurrently. Results: Ratios of the excitation functions were extracted from the yield data. Resonances were identified in the yield ratio data which correspond to previously reported levels in 16O. An R-matrix analysis, which fits the present data as well as previous measurements from the literature simultaneously, finds reasonable agreement between the current measurements and those in the literature. Conclusions: The additional data from this measurement will be combined with previous literature data in a comprehensive R-matrix analysis of reactions which populate 16O over a similar energy region
Monte Carlo uncertainty of the H3(alpha, gamma)B7 reaction rate
No full textBackground: The He3(α,γ)Be7 reaction is of critical importance in determining the flux of solar neutrinos through the pp-II and pp-III chains. For this reason and others, the description of the cross section and its extrapolation towards low-energy has always been a matter of intense debate. While large systematic differences have been present in the past, several recent measurements are all in excellent statistical agreement.
Purpose: The convergence of the recent individual experimental measurements of the He3(α,γ)Be7 reaction prompts a global analysis of the reaction data. From the combined data, a more precise estimate of the low-energy cross section can be determined.
Results: A global R-matrix fit is used to describe the He3(α,γ)Be7 data as well as scattering data over a similar energy range. The R-matrix fit is then subjected to a Monte Carlo analysis to extract the uncertainties on the cross section and corresponding reaction rate.
Conclusion: By combining several recent measurements of the He3(α,γ)Be7 reaction, the combined data yield a zero energy S factor of S(0)=0.542±0.011(MCfit)±0.006(model)+0.019−0.011(phaseshifts) keV b. This gives a total uncertainty in S(0) of +0.023/−0.017 keV b
The role of HuD, a post-transcriptional regulator, in the development and function of the murine neocortex
No full textThe neocortex is a unique six-layered brain region composed of an array of morphologically and functionally distinct subpopulations of primary projection neurons forming complex circuits across the central nervous system. The developmentally progressive specification, differentiation, and signaling of these distinct subpopulations of neocortical projection neurons is critical to mammalian cognitive and sensorimotor abilities. Recent research points to mRNA metabolism as a key regulator of this development and maturation process. Hu antigen D (HuD), an RNA binding protein has been implicated in the establishment of neuronal identity and neurite outgrowth in vitro. Therefore, we investigated the role of HuD loss of function on neuron specification and dendritogenesis in vivo using a mouse model. We found that loss of HuD early in development results in a defective early dendritic overgrowth phase as well as pervasive deficits in neuron specification in the lower neocortical layers, as well as defects in dendritogenesis in the CA3 region of the hippocampus. Subsequent behavioral analysis revealed a deficit in performance of a hippocampal dependent task: the Morris water maze. Further, HuD knockout (KO) mice exhibited lower levels of anxiety than wild type counterparts, and were overall less active. Last, we found that HuD KO mice are more susceptible to auditory-induced seizures, often resulting in death. I have also discovered that HuD itself is heavily regulated at the post-transcriptional level, and is expressed in four transcript variants which encode 4 functionally distinct protein isoforms. Specifically, my data indicate that HuD4 is translated during early neocortical neurogenesis when lower layers are formed, where HuD3 is specifically translated during late neocortical neurogenesis. Further, early HuD3 overexpression drives the production of upper layer neurons, where HuD4 overexpression drives the fate of lower layer neurons. Using a conditional transgenic line as well as in-vitro cell cycle analysis, I also determined that the translational regulation of HuD3 is dependent upon NT-3 arriving from the thalamic afferents to the neocortex. This trophic source appears to only affect those stem cells distal to the ventricle when they are in S-phase.Ph. D.Includes bibliographical referencesby Erik Michael DeBoe
sj-pdf-1-cpj-10.1177_00099228231179453 – Supplemental material for Cardiopulmonary Phenotypes and Protein Signatures in Children With Down Syndrome
No full textSupplemental material, sj-pdf-1-cpj-10.1177_00099228231179453 for Cardiopulmonary Phenotypes and Protein Signatures in Children With Down Syndrome by Emily M. DeBoer, Kristine Wolter-Warmerdam, Robin R. Deterding, Juana Marmolejo, Tom Blumenthal, Joaquin M. Espinosa, Francis Hickey and Brandie D. Wagner in Clinical Pediatrics</p
sj-pdf-2-cpj-10.1177_00099228231179453 – Supplemental material for Cardiopulmonary Phenotypes and Protein Signatures in Children With Down Syndrome
No full textSupplemental material, sj-pdf-2-cpj-10.1177_00099228231179453 for Cardiopulmonary Phenotypes and Protein Signatures in Children With Down Syndrome by Emily M. DeBoer, Kristine Wolter-Warmerdam, Robin R. Deterding, Juana Marmolejo, Tom Blumenthal, Joaquin M. Espinosa, Francis Hickey and Brandie D. Wagner in Clinical Pediatrics</p
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