3,256 research outputs found

    Differential spectrum modeling and sensitivity for keV sterile neutrino search at KATRIN

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    Starting in 2026, the KATRIN experiment will conduct a high-statistics measurement of the differential tritium β\beta-spectrum to energies deep below the kinematic endpoint. This enables the search for keV sterile neutrinos with masses less than the kinematic endpoint energy m4E0=18.6keVm_\mathrm{4} \leq E_0 = 18.6\,\mathrm{keV}, aiming for a statistical sensitivity of Ue42=sin2θ106|U_\mathrm{e4}|^2=\sin^2\theta\sim 10^{-6} for the mixing amplitude. The differential spectrum is obtained by decreasing the retarding potential of KATRIN\u27s main spectrometer, and by determining the β\beta-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

    Bayesian inference for challenging scientific models

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    Advances in technology and computation have led to ever more complicated scientific models of phenomena across a wide variety of fields. Many of these models present challenges for Bayesian inference, as a result of computationally intensive likelihoods, high-dimensional parameter spaces or large dataset sizes. In this thesis we show how we can apply developments in probabilistic machine learning and statistics to do inference with examples of these types of models. As a demonstration of an applied inference problem involving a non-trivial likelihood computation, we show how a combination of optimisation and MCMC methods along with careful consideration of priors can be used to infer the parameters of an ODE model of the cardiac action potential. We then consider the problem of pileup, a phenomenon that occurs in astronomy when using CCD detectors to observe bright sources. It complicates the fitting of even simple spectral models by introducing an observation model with a large number of continuous and discrete latent variables that scales with the size of the dataset. We develop an MCMC-based method that can work in the presence of pileup by explicitly marginalising out discrete variables and using adaptive HMC on the remaining continuous variables. We show with synthetic experiments that it allows us to fit spectral models in the presence of pileup without biasing the results. We also compare it to neural Simulation- Based Inference approaches, and find that they perform comparably to the MCMC-based approach whilst being able to scale to larger datasets. As an example of a problem where we wish to do inference with extremely large datasets, we consider the Extreme Deconvolution method. The method fits a probability density to a dataset where each observation has Gaussian noise added with a known sample-specific covariance, originally intended for use with astronomical datasets. The existing fitting method is batch EM, which would not normally be applied to large datasets such as the Gaia catalog containing noisy observations of a billion stars. In this thesis we propose two minibatch variants of extreme deconvolution, based on an online variation of the EM algorithm, and direct gradient-based optimisation of the log-likelihood, both of which can run on GPUs. We demonstrate that these methods provide faster fitting, whilst being able to scale to much larger models for use with larger datasets. We then extend the extreme deconvolution approach to work with non- Gaussian noise, and to use more flexible density estimators such as normalizing flows. Since both adjustments lead to an intractable likelihood, we resort to amortized variational inference in order to fit them. We show that for some datasets that flows can outperform Gaussian mixtures for extreme deconvolution, and that fitting with non-Gaussian noise is now possible

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Undergraduate Catalog of Studies, 2022-2023

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    Bayesian Forecasting in Economics and Finance: A Modern Review

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    The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified explicitly, and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large, or complex. The current state of play in Bayesian forecasting in economics and finance is the subject of this review. The aim is to provide the reader with an overview of modern approaches to the field, set in some historical context; and with sufficient computational detail given to assist the reader with implementation.Comment: The paper is now published online at: https://doi.org/10.1016/j.ijforecast.2023.05.00

    Surface Reactions of Biomass Derived Oxygenates on Lewis Acidic Metal Oxides

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    Lignocellulosic biomass is currently the only source of organic carbon making it a sustainable source for production of liquid hydrocarbon fuels. One main challenge for valorization of biomass is reducing the oxygen content of the starting feedstock and producing high value chemicals. Using heterogeneous catalysts for conversion of biomass feedstock to commodity chemicals is one strategy for the valorization process. Specifically, using Lewis acidic metal oxides for this upgrading process has shown promise due to its ability to catalyze relevant reactions such as isomerization and (retro-) aldol condensation. This work seeks to elucidate the surface interactions of biomass derived oxygenates with solid Lewis acid sites. This is done using in-situ spectroscopic techniques such as Fourier transformed infrared, nuclear magnetic resonance and ultra-violet spectroscopies. These techniques were applied for studying the following reactions: (i) aldol condensation of ethanol and acetaldehyde over reduced molybdenum oxide; (ii) aldol condensation of acetaldehyde over supported molybdenum oxides; (iii) dehydration and retro-aldol condensation of C4 polyoxygenates using various Lewis acidic metal oxides and (iv) ring opening and esterification of erythrose using various Lewis acidic metal oxides. Surface properties such as Lewis and Brønsted acid site and reducibility of metal center are essential to rationalizing the reaction pathway of the above reactions. The aforementioned studies provide fundamental knowledge regarding how different oxygenates can interact with solid Lewis acid sites.Ph.D

    Image Diversification via Deep Learning based Generative Models

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    Machine learning driven pattern recognition from imagery such as object detection has been prevalenting among society due to the high demand for autonomy and the recent remarkable advances in such technology. The machine learning technologies acquire the abstraction of the existing data and enable inference of the pattern of the future inputs. However, such technologies require a sheer amount of images as a training dataset which well covers the distribution of the future inputs in order to predict the proper patterns whereas it is impracticable to prepare enough variety of images in many cases. To address this problem, this thesis pursues to discover the method to diversify image datasets for fully enabling the capability of machine learning driven applications. Focusing on the plausible image synthesis ability of generative models, we investigate a number of approaches to expand the variety of the output images using image-to-image translation, mixup and diffusion models along with the technique to enable a computation and training dataset efficient diffusion approach. First, we propose the combined use of unpaired image-to-image translation and mixup for data augmentation on limited non-visible imagery. Second, we propose diffusion image-to-image translation that generates greater quality images than other previous adversarial training based translation methods. Third, we propose a patch-wise and discrete conditional training of diffusion method enabling the reduction of the computation and the robustness on small training datasets. Subsequently, we discuss a remaining open challenge about evaluation and the direction of future work. Lastly, we make an overall conclusion after stating social impact of this research field

    Markov field models of molecular kinetics

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    Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions
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