4,852 research outputs found
Differential spectrum modeling and sensitivity for keV sterile neutrino search at KATRIN
Starting in 2026, the KATRIN experiment will conduct a high-statistics measurement of the differential tritium -spectrum to energies deep below the kinematic endpoint. This enables the search for keV sterile neutrinos with masses less than the kinematic endpoint energy , aiming for a statistical sensitivity of for the mixing amplitude. The differential spectrum is obtained by decreasing the retarding potential of KATRIN\u27s main spectrometer, and by determining the -electron energies by their energy deposition in the new TRISTAN SDD array. In this mode of operation, the existing integral model of the tritium spectrum is insufficient, and a novel differential model is developed in this work.
The new model (TRModel) convolves the differential tritium spectrum using responese matrices to predict the energy spectrum of registered events after data acquisition. Each response matrix encodes the spectral spectral distrortion from individual experimental effects, which depend on adjustable systematic parameters. This approach allows to efficiently assess the sensitivity impact of each systematics individually or in combination with others. The response matrices are obtained from monte carlo simulations, numerical convolution, and analytical computation.
In this work, the sensitivity impact of 20 systematic parameters is assessed for the TRISTAN Phase-1 measurement for which nine TRISTAN SDD modules are integrated into the KATRIN beamline. Furthermore, it is demonstrated that the sensitivity impact is significantly mitigated with several beamline field adjustments and minimal hardware modifications
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics
It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been
emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations
Peering into the Dark: Investigating dark matter and neutrinos with cosmology and astrophysics
The LCDM model of modern cosmology provides a highly accurate description of our universe.
However, it relies on two mysterious components, dark matter and dark energy. The cold dark matter
paradigm does not provide a satisfying description of its particle nature, nor any link to the Standard
Model of particle physics.
I investigate the consequences for cosmological structure formation in models with a coupling
between dark matter and Standard Model neutrinos, as well as probes of primordial black holes as
dark matter.
I examine the impact that such an interaction would have through both linear perturbation theory and
nonlinear N-body simulations. I present limits on the possible interaction strength from cosmic
microwave background, large scale structure, and galaxy population data, as well as forecasts on the
future sensitivity. I provide an analysis of what is necessary to distinguish the cosmological impact of
interacting dark matter from similar effects. Intensity mapping of the 21 cm line of neutral hydrogen at
high redshift using next generation observatories, such as the SKA, would provide the strongest
constraints yet on such interactions, and may be able to distinguish between different scenarios
causing suppressed small scale structure. I also present a novel type of probe of structure formation,
using the cosmological gravitational wave signal of high redshift compact binary mergers to provide
information about structure formation, and thus the behaviour of dark matter. Such observations
would also provide competitive constraints.
Finally, I investigate primordial black holes as an alternative dark matter candidate, presenting an
analysis and framework for the evolution of extended mass populations over cosmological time and
computing the present day gamma ray signal, as well as the allowed local evaporation rate. This is
used to set constraints on the allowed population of low mass primordial black holes, and the
likelihood of witnessing an evaporation
Leveraging elasticity theory to calculate cell forces: From analytical insights to machine learning
Living cells possess capabilities to detect and respond to mechanical features of their surroundings. In traction force microscopy, the traction of cells on an elastic substrate is made visible by observing substrate deformation as measured by the movement of embedded marker beads. Describing the substrates by means of elasticity theory, we can calculate the adhesive forces, improving our understanding of cellular function and behavior. In this dissertation, I combine analytical solutions with numerical methods and machine learning techniques to improve traction prediction in a range of experimental applications. I describe how to include the normal traction component in regularization-based Fourier approaches, which I apply to experimental data. I compare the dominant strategies for traction reconstruction, the direct method and inverse, regularization-based approaches and find, that the latter are more precise while the former is more stress resilient to noise. I find that a point-force based reconstruction can be used to study the force balance evolution in response to microneedle pulling showing a transition from a dipolar into a monopolar force arrangement. Finally, I show how a conditional invertible neural network not only reconstructs adhesive areas more localized, but also reveals spatial correlations and variations in reliability of traction reconstructions
Enhancing the Structural Stability of α-phase Hybrid Perovskite Films through Defect Engineering Approaches under Ambient Conditions
This thesis investigates methods whereby perovskite solar cell power conversion efficiency and material stability
may be improved. Hybrid perovskites have gained increased attention for optoelectronic applications due to
favourable properties such as strong absorption, facile processing, and changeable band-gap. Despite excellent
improvements in power conversion efficiency of devices, perovskite films are unstable, degrading with relative
ease in the presence of moisture, oxygen, light, heat, and electric fields. The focus of this thesis is on ambient
atmosphere stability, concerned with the influence of moisture in particular on perovskite film fabrication,
degradation, and device functionality. In order to shed light on the impact of ambient atmosphere on perovskite
films, experiments are designed to investigate films during fabrication and degradation. The influences firstly of
stoichiometry during ambient fabrication, and then ionic substitution (with caesium and formadinium) upon
moisture-induced degradation are investigated. Finally, films and devices with a novel composition
incorporating Zn are fabricated under ambient conditions to investigate the effect of Zn addition on perovskite
film stability
2017 GREAT Day Program
SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Mathematical Problems in Rock Mechanics and Rock Engineering
With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms
We propose to learn non-convex regularizers with a prescribed upper bound on
their weak-convexity modulus. Such regularizers give rise to variational
denoisers that minimize a convex energy. They rely on few parameters (less than
15,000) and offer a signal-processing interpretation as they mimic handcrafted
sparsity-promoting regularizers. Through numerical experiments, we show that
such denoisers outperform convex-regularization methods as well as the popular
BM3D denoiser. Additionally, the learned regularizer can be deployed to solve
inverse problems with iterative schemes that provably converge. For both CT and
MRI reconstruction, the regularizer generalizes well and offers an excellent
tradeoff between performance, number of parameters, guarantees, and
interpretability when compared to other data-driven approaches
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