Heegaard Floer correction terms, with a twist

We use Heegaard Floer homology with twisted coefficients to define numerical invariants for arbitrary closed 3-manifolds equipped torsion spin$^c$ structures, generalising the correction terms (or $d$--invariants) defined by Ozsv\'ath and Szab\'o for integer homology 3-spheres and, more generally, for 3-manifolds with standard ${\rm HF}^\infty$. Our twisted correction terms share many properties with their untwisted analogues. In particular, they provide restrictions on the topology of 4-manifolds bounding a given 3-manifold.Comment: 24 pages, 2 figures; New proof of additivity (Proposition 3.7) based on a connected sum formula for twisted coefficients (Proposition 2.3); exposition improved, mainly in Section 4; Proposition 3.8 downgraded to an inequality due to an error in the previous version found by Adam Levin

Access to Electronic Data for Criminal Investigations Purposes in the EU. CEPS Paper in liberty and security in Europe No. 2020-01, February 2020

Within the EU and across the Atlantic, investigation and prosecution of crime increasingly relies on the possibility to access, collect and transfer electronic information and personal data held by private companies across borders. Cross-border access to and collection of data for the purpose of fighting crime raise several legal and jurisdictional issues. This paper comparatively examines the constitutional, legal and administrative frameworks on access to and use of digital information in cross-border criminal justice cooperation in a selection of EU member states. It presents key challenges in the application of the EU mutual recognition and mutual legal assistance instruments, as well as the existence of 'promising practices' across the EU and in transatlantic relations. The paper also assesses a set of legal and practical questions raised by the ongoing policy and normative debate on the so-called “E-Evidence” Package. Finally, it sets out a number of policy options and practical ways forward for EU and national policy makers to promote judicial cooperation for cross-border access to and collection of electronic data in line with EU and international rule law and fundamental rights standards

Domain decomposition and multilevel integration for fermions

The numerical computation of many hadronic correlation functions is exceedingly difficult due to the exponentially decreasing signal-to-noise ratio with the distance between source and sink. Multilevel integration methods, using independent updates of separate regions in space-time, are known to be able to solve such problems but have so far been available only for pure gauge theory. We present first steps into the direction of making such integration schemes amenable to theories with fermions, by factorizing a given observable via an approximated domain decomposition of the quark propagator. This allows for multilevel integration of the (large) factorized contribution to the observable, while its (small) correction can be computed in the standard way.Comment: 14 pages, 6 figures, v2: published version, talk presented at the 34th annual International Symposium on Lattice Field Theory, 24-30 July 2016, University of Southampton, U

Local multiboson factorization of the quark determinant

We discuss the recently proposed multiboson domain-decomposed factorization of the gauge-field dependence of the fermion determinant in lattice QCD. In particular, we focus on the case of a lattice divided in an arbitrary number of thick time slices. As a consequence, multiple space-time regions can be updated independently. This allows to address the exponential degradation of the signal-to-noise ration of correlation functions with multilevel Monte Carlo sampling. We show numerical evidence of the effectiveness of a two-level integration for pseudoscalar propagators with momentum and for vector propagators, in a two active regions setup. These results are relevant to lattice computation of the hadronic contributions to the anomalous magnetic moment of the muon and to heavy meson decay form factors.Comment: 8 pages, 4 figures, talk presented at the 35th International Symposium on Lattice Field Theory, 18-24 June 2017, Granada, Spai

Estimating Extinction using Unsupervised Machine Learning

Dust extinction is the most robust tracer of the gas distribution in the interstellar medium, but measuring extinction is limited by the systematic uncertainties involved in estimating the intrinsic colors to background stars. In this paper we present a new technique, PNICER, that estimates intrinsic colors and extinction for individual stars using unsupervised machine learning algorithms. This new method aims to be free from any priors with respect to the column density and intrinsic color distribution. It is applicable to any combination of parameters and works in arbitrary numbers of dimensions. Furthermore, it is not restricted to color space. Extinction towards single sources is determined by fitting Gaussian Mixture Models along the extinction vector to (extinction-free) control field observations. In this way it becomes possible to describe the extinction for observed sources with probability densities. PNICER effectively eliminates known biases found in similar methods and outperforms them in cases of deep observational data where the number of background galaxies is significant, or when a large number of parameters is used to break degeneracies in the intrinsic color distributions. This new method remains computationally competitive, making it possible to correctly de-redden millions of sources within a matter of seconds. With the ever-increasing number of large-scale high-sensitivity imaging surveys, PNICER offers a fast and reliable way to efficiently calculate extinction for arbitrary parameter combinations without prior information on source characteristics. PNICER also offers access to the well-established NICER technique in a simple unified interface and is capable of building extinction maps including the NICEST correction for cloud substructure. PNICER is offered to the community as an open-source software solution and is entirely written in Python.Comment: Accepted for publication in A&A, source code available at http://smeingast.github.io/PNICER

Recent advances in the simulation of particle-laden flows

A substantial number of algorithms exists for the simulation of moving particles suspended in fluids. However, finding the best method to address a particular physical problem is often highly non-trivial and depends on the properties of the particles and the involved fluid(s) together. In this report we provide a short overview on a number of existing simulation methods and provide two state of the art examples in more detail. In both cases, the particles are described using a Discrete Element Method (DEM). The DEM solver is usually coupled to a fluid-solver, which can be classified as grid-based or mesh-free (one example for each is given). Fluid solvers feature different resolutions relative to the particle size and separation. First, a multicomponent lattice Boltzmann algorithm (mesh-based and with rather fine resolution) is presented to study the behavior of particle stabilized fluid interfaces and second, a Smoothed Particle Hydrodynamics implementation (mesh-free, meso-scale resolution, similar to the particle size) is introduced to highlight a new player in the field, which is expected to be particularly suited for flows including free surfaces.Comment: 16 pages, 4 figure