arXiv subjects

Martin Bauer

Publications and source records attributed to Martin Bauer.

At least 73 records · Page 4Linked to original sources

arXiv2021

Relic neutrinos at accelerator experiments

We present a new technique for observing low energy neutrinos with the aim of detecting the cosmic neutrino background using ion storage rings. Utilising high energy targets exploits the quadratic increase in the neutrino capture cross section with beam energy, and with sufficient beam energy, enables neutrino capture through inverse-beta decay processes from a stable initial state. We also show that there exist ion systems admitting resonant neutrino capture, capable of achieving larger capture cross sections at lower beam energies than their non-resonant counterparts. We calculate the neutrino capture rate and the optimal experimental runtime for a range of different resonant processes and target ions and we demonstrate that the resonant capture experiment can be performed with beam energies as low as $\mathcal{O}(10\,\mathrm{TeV})$ per target nucleon. Unfortunately, none of the ion systems discussed here can provide sufficient statistics to discover the cosmic neutrino background with current technology. We address the challenges associated with realising this experiment in the future, taking into account the uncertainty in the beam and neutrino momentum distributions, synchrotron radiation, as well as the beam stability.

hep-ph↗

arXiv2021

Smooth perturbations of the functional calculus and applications to Riemannian geometry on spaces of metrics

We show for a certain class of operators $A$ and holomorphic functions $f$ that the functional calculus $A\mapsto f(A)$ is holomorphic. Using this result we are able to prove that fractional Laplacians $(1+Δ^g)^p$ depend real analytically on the metric $g$ in suitable Sobolev topologies. As an application we obtain local well-posedness of the geodesic equation for fractional Sobolev metrics on the space of all Riemannian metrics.

math.DG↗

arXiv2021

Physics Informed Convex Artificial Neural Networks (PICANNs) for Optimal Transport based Density Estimation

Optimal Mass Transport (OMT) is a well studied problem with a variety of applications in a diverse set of fields ranging from Physics to Computer Vision and in particular Statistics and Data Science. Since the original formulation of Monge in 1781 significant theoretical progress been made on the existence, uniqueness and properties of the optimal transport maps. The actual numerical computation of the transport maps, particularly in high dimensions, remains a challenging problem. By Brenier's theorem, the continuous OMT problem can be reduced to that of solving a non-linear PDE of Monge-Ampere type whose solution is a convex function. In this paper, building on recent developments of input convex neural networks and physics informed neural networks for solving PDE's, we propose a Deep Learning approach to solve the continuous OMT problem. To demonstrate the versatility of our framework we focus on the ubiquitous density estimation and generative modeling tasks in statistics and machine learning. Finally as an example we show how our framework can be incorporated with an autoencoder to estimate an effective probabilistic generative model.

cs.LG↗

arXiv2021

Flavor probes of axion-like particles

Axions and axion-like particles (ALPs) are well-motivated low-energy relics of high-energy extensions of the Standard Model (SM). We investigate the phenomenology of an ALP with flavor-changing couplings, and present a comprehensive analysis of quark and lepton flavor-changing observables within a general ALP effective field theory. Observables studied include rare meson decays, flavor oscillations of neutral mesons, rare lepton decays, and dipole moments. We derive bounds on the general ALP couplings as a function of its mass, consistently taking into account the ALP lifetime and branching ratios. We further calculate quark flavor-changing effects that are unavoidably induced by running and matching between the new physics scale and the scale of the measurements. This allows us to derive bounds on benchmark ALP models in which only a single (flavorless or flavor-universal) ALP coupling to SM particles is present at the new physics scale, and in this context we highlight the complementarity and competitiveness of flavor bounds with constraints derived from collider, beam dump and astrophysical measurements. We find that searches for ALPs produced in meson decays provide some of the strongest constraints in the MeV-GeV mass range, even for the most flavorless of ALP models. Likewise, we discuss the interplay of flavor-conserving and flavor-violating couplings of the ALP to leptons, finding that constraints from lepton flavor-violating observables generally depend strongly on both. Additionally, we analyze whether an ALP can provide an explanation for various experimental anomalies including those observed in rare B-meson decays, measurements at the ATOMKI and KTeV experiments, and in the anomalous magnetic moments of the muon and the electron.

hep-ph↗

arXiv2021

A numerical framework for elastic surface matching, comparison, and interpolation

Surface comparison and matching is a challenging problem in computer vision. While reparametrization-invariant Sobolev metrics provide meaningful elastic distances and point correspondences via the geodesic boundary value problem, solving this problem numerically tends to be difficult. Square root normal fields (SRNF) considerably simplify the computation of certain elastic distances between parametrized surfaces. Yet they leave open the issue of finding optimal reparametrizations, which induce elastic distances between unparametrized surfaces. This issue has concentrated much effort in recent years and led to the development of several numerical frameworks. In this paper, we take an alternative approach which bypasses the direct estimation of reparametrizations: we relax the geodesic boundary constraint using an auxiliary parametrization-blind varifold fidelity metric. This reformulation has several notable benefits. By avoiding altogether the need for reparametrizations, it provides the flexibility to deal with simplicial meshes of arbitrary topologies and sampling patterns. Moreover, the problem lends itself to a coarse-to-fine multi-resolution implementation, which makes the algorithm scalable to large meshes. Furthermore, this approach extends readily to higher-order feature maps such as square root curvature fields and is also able to include surface textures in the matching problem. We demonstrate these advantages on several examples, synthetic and real.

cs.CV↗

arXiv2021

The Low-Energy Effective Theory of Axions and ALPs

Axions and axion-like particles (ALPs) are well-motivated low-energy relics of high-energy extensions of the Standard Model, which interact with the known particles through higher-dimensional operators suppressed by the mass scale $Λ$ of the new-physics sector. Starting from the most general dimension-5 interactions, we discuss in detail the evolution of the ALP couplings from the new-physics scale to energies at and below the scale of electroweak symmetry breaking. We derive the relevant anomalous dimensions at two-loop order in gauge couplings and one-loop order in Yukawa interactions, carefully considering the treatment of a redundant operator involving an ALP coupling to the Higgs current. We account for one-loop (and partially two-loop) matching contributions at the weak scale, including in particular flavor-changing effects. The relations between different equivalent forms of the effective Lagrangian are discussed in detail. We also construct the effective chiral Lagrangian for an ALP interacting with photons and light pseudoscalar mesons, pointing out important differences with the corresponding Lagrangian for the QCD axion.

hep-ph↗

arXiv2021

On the Flavour Structure of Anomaly-free Hidden Photon Models

Extensions of the Standard Model with an Abelian gauge group are constrained by gauge anomaly cancellation, so that only a limited number of possible charge assignments is allowed without the introduction of new chiral fermions. For flavour universal charges, couplings of the associated hidden photon to Standard Model fermions are flavour conserving at tree-level. We show explicitly that even the flavour specific charge assignments allowed by anomaly cancellation condition lead to flavour conserving tree-level couplings of the hidden photon to quarks and charged leptons \emph{if} the CKM or PMNS matrix can be successfully reconstructed. Further, loop-induced flavour changing couplings are strongly suppressed. As a consequence, the structure of the Majorana mass matrix is constrained and flavour changing tree-level couplings of the hidden photon to neutrino mass eigenstates are identified as a means to distinguish the $U(1)_{B-L}$ gauge boson from any other anomaly-free extension of the Standard Model without new chiral fermions. We present a comprehensive analysis of constraints and projections for future searches for a $U(1)_{B-3L_i}$ gauge boson, calculate the reach of resonance searches in $B$ meson decays and comment on the implications for non-standard neutrino interactions.

hep-ph↗

arXiv2021

Supervised deep learning of elastic SRV distances on the shape space of curves

Motivated by applications from computer vision to bioinformatics, the field of shape analysis deals with problems where one wants to analyze geometric objects, such as curves, while ignoring actions that preserve their shape, such as translations, rotations, or reparametrizations. Mathematical tools have been developed to define notions of distances, averages, and optimal deformations for geometric objects. One such framework, which has proven to be successful in many applications, is based on the square root velocity (SRV) transform, which allows one to define a computable distance between spatial curves regardless of how they are parametrized. This paper introduces a supervised deep learning framework for the direct computation of SRV distances between curves, which usually requires an optimization over the group of reparametrizations that act on the curves. The benefits of our approach in terms of computational speed and accuracy are illustrated via several numerical experiments.

cs.CV↗

arXiv2021

Structural Connectome Atlas Construction in the Space of Riemannian Metrics

The structural connectome is often represented by fiber bundles generated from various types of tractography. We propose a method of analyzing connectomes by representing them as a Riemannian metric, thereby viewing them as points in an infinite-dimensional manifold. After equipping this space with a natural metric structure, the Ebin metric, we apply object-oriented statistical analysis to define an atlas as the Fréchet mean of a population of Riemannian metrics. We demonstrate connectome registration and atlas formation using connectomes derived from diffusion tensors estimated from a subset of subjects from the Human Connectome Project.

cs.CV↗

arXiv2021

Consistent treatment of axions in the weak chiral Lagrangian

We present a consistent implementation of weak decays involving an axion or axion-like particle in the context of an effective chiral Lagrangian. We argue that previous treatments of such processes have used an incorrect representation of the flavor-changing quark currents in the chiral theory. As an application, we derive model-independent results for the decays $K^-\toπ^- a$ and $π^-\to e^-\barν_e a$ at leading order in the chiral expansion and for arbitrary axion couplings and mass. In particular, we find that the $K^-\toπ^- a$ branching ratio is almost 40 times larger than previously estimated.

hep-ph↗

arXiv2021

Can we run to infinity? The diameter of the diffeomorphism group with respect to right-invariant Sobolev metrics

The group $\text{Diff}(\mathcal{M})$ of diffeomorphisms of a closed manifold $\mathcal{M}$ is naturally equipped with various right-invariant Sobolev norms $W^{s,p}$. Recent work showed that for sufficiently weak norms, the geodesic distance collapses completely (namely, when $sp\le \text{dim}\mathcal{M}$ and $s<1$). But when there is no collapse, what kind of metric space is obtained? In particular, does it have a finite or infinite diameter? This is the question we study in this paper. We show that the diameter is infinite for strong enough norms, when $(s-1)p\ge \text{dim}\mathcal{M}$, and that for spheres the diameter is finite when $(s-1)p<1$. In particular, this gives a full characterization of the diameter of $\text{Diff}(S^1)$. In addition, we show that for $\text{Diff}_c(\mathbb{R}^n)$, if the diameter is not zero, it is infinite.

math.DG↗

arXiv2021

Light Dark Matter Annihilation and Scattering in LHC Detectors

We systematically study models with light scalar and pseudoscalar dark matter candidates and their potential signals at the LHC. First, we derive cosmological bounds on models with the Standard Model Higgs mediator and with a new weak-scale mediator. Next, we study two processes inspired by the indirect and direct detection process topologies, now happening inside the LHC detectors. We find that LHC can observe very light dark matter over a huge mass range if it is produced in mediator decays and then scatters with the detector material to generate jets in the nuclear recoil.

hep-ph↗

arXiv2020

Highly Efficient Lattice-Boltzmann Multiphase Simulations of Immiscible Fluids at High-Density Ratios on CPUs and GPUs through Code Generation

A high-performance implementation of a multiphase lattice Boltzmann method based on the conservative Allen-Cahn model supporting high-density ratios and high Reynolds numbers is presented. Metaprogramming techniques are used to generate optimized code for CPUs and GPUs automatically. The coupled model is specified in a high-level symbolic description and optimized through automatic transformations. The memory footprint of the resulting algorithm is reduced through the fusion of compute kernels. A roofline analysis demonstrates the excellent efficiency of the generated code on a single GPU. The resulting single GPU code has been integrated into the multiphysics framework waLBerla to run massively parallel simulations on large domains. Communication hiding and GPUDirect-enabled MPI yield near-perfect scaling behaviour. Scaling experiments are conducted on the Piz Daint supercomputer with up to 2048 GPUs, simulating several hundred fully resolved bubbles. Further, validation of the implementation is shown in a physically relevant scenario-a three-dimensional rising air bubble in water.

physics.flu-dyn↗

arXiv2020

Intrinsic Riemannian metrics on spaces of curves: theory and computation

This chapter reviews some past and recent developments in shape comparison and analysis of curves based on the computation of intrinsic Riemannian metrics on the space of curves modulo shape-preserving transformations. We summarize the general construction and theoretical properties of quotient elastic metrics for Euclidean as well as non-Euclidean curves before considering the special case of the square root velocity metric for which the expression of the resulting distance simplifies through a particular transformation. We then examine different numerical approaches that have been proposed to estimate such distances in practice and in particular to quotient out curve reparametrization in the resulting minimization problems.

math.DG↗

arXiv2020

A diffeomorphism-invariant metric on the space of vector-valued one-forms

In this article we introduce a diffeomorphism-invariant Riemannian metric on the space of vector valued one-forms. The particular choice of metric is motivated by potential future applications in the field of functional data and shape analysis and by connections to the Ebin metric on the space of all Riemannian metrics. In the present work we calculate the geodesic equations and obtain an explicit formula for the solutions to the corresponding initial value problem. Using this we show that it is a geodesically and metrically incomplete space and study the existence of totally geodesic subspaces. Furthermore, we calculate the sectional curvature and observe that, depending on the dimension of the base manifold and the target space, it either has a semidefinite sign or admits both signs.

math.DG↗

arXiv2020

Axion-Like Particles at Future Colliders

Axion-like particles (ALPs) are pseudo Nambu-Goldstone bosons of spontaneously broken global symmetries in high-energy extensions of the Standard Model (SM). This makes them a prime target for future experiments aiming to discover new physics which addresses some of the open questions of the SM. While future high-precision experiments can discover ALPs with masses well below the GeV scale, heavier ALPs can be searched for at future high-energy lepton and hadron colliders. We discuss the reach of the different proposed colliders, focusing on resonant ALP production, ALP production in the decay of heavy SM resonances, and associate ALP production with photons, Z bosons or Higgs bosons. We consider the leading effective operators mediating interactions between the ALP and SM particles and discuss search strategies for ALPs decaying promptly as well as ALPs with delayed decays. Projections for the high-luminosity run of the LHC and its high-energy upgrade, CLIC, the future $e^+e^-$ ring-colliders CEPC and FCC-ee, the future pp colliders SPPC and FCC-hh, and for the MATHUSLA surface array are presented. We further discuss the constraining power of future measurements of electroweak precision parameters on the relevant ALP couplings.

hep-ph↗

arXiv2020

A Standard-based Open Source IoT Platform: FIWARE

The ever-increasing acceleration of technology evolution in all fields is rapidly changing the architectures of data-driven systems towards the Internet-of-Things concept. Many general and specific-purpose IoT platforms are already available. This article introduces the capabilities of the FIWARE framework that is transitioning from a research to a commercial level. We base our exposition on the analysis of three real-world use cases (global IoT market, analytics in smart cities, and IoT augmented autonomous driving) and their requirements that are addressed with the usage of FIWARE. We highlight the lessons learnt during the design, implementation and deployment phases for each of the use cases and their critical issues. Finally we give two examples showing that FIWARE still maintains openness to innovation: semantics and privacy.

cs.NI↗

arXiv2020

lbmpy: Automatic code generation for efficient parallel lattice Boltzmann methods

Lattice Boltzmann methods are a popular mesoscopic alternative to macroscopic computational fluid dynamics solvers. Many variants have been developed that vary in complexity, accuracy, and computational cost. Extensions are available to simulate multi-phase, multi-component, turbulent, or non-Newtonian flows. In this work we present lbmpy, a code generation package that supports a wide variety of different methods and provides a generic development environment for new schemes as well. A high-level domain-specific language allows the user to formulate, extend and test various lattice Boltzmann schemes. The method specification is represented in a symbolic intermediate representation. Transformations that operate on this intermediate representation optimize and parallelize the method, yielding highly efficient lattice Boltzmann compute kernels not only for single- and two-relaxation-time schemes but also for multi-relaxation-time, cumulant, and entropically stabilized methods. An integration into the HPC framework waLBerla makes massively parallel, distributed simulations possible, which is demonstrated through scaling experiments on the SuperMUC-NG supercomputing system

cs.MS↗