2,924 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Machine learning applications in search algorithms for gravitational waves from compact binary mergers

    Get PDF
    Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe. However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing. In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software. Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals

    Data-assisted modeling of complex chemical and biological systems

    Get PDF
    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter

    Peering into the Dark: Investigating dark matter and neutrinos with cosmology and astrophysics

    Get PDF
    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

    30th European Congress on Obesity (ECO 2023)

    Get PDF
    This is the abstract book of 30th European Congress on Obesity (ECO 2023

    Topological diffusive metal in amorphous transition metal mono-silicides

    Full text link
    In chiral crystals crystalline symmetries can protect multifold fermions, pseudo-relativistic masless quasiparticles that have no high-energy counterparts. Their realization in transition metal mono-silicides has exemplified their intriguing physical properties, such as long Fermi arc surface states and unusual optical responses. Recent experimental studies on amorphous transition metal mono-silicides suggest that topological properties may survive beyond crystals, even though theoretical evidence is lacking. Motivated by these findings, we theoretically study a tight-binding model of amorphous transition metal mono-silicides. We find that topological properties of multifold fermions survive in the presence of structural disorder that converts the semimetal into a diffusive metal. We characterize this topological diffusive metal phase with the spectral localizer, a real-space topological indicator that we show can signal multifold fermions. Our findings showcase how topological properties can survive in disordered metals, and how they can be uncovered using the spectral localizer.Comment: 7 + 9 pages; 4 + 9 figure

    Beam scanning by liquid-crystal biasing in a modified SIW structure

    Get PDF
    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

    Aspects of Holographic Entanglement Entropy in Cubic Curvature Gravity

    Full text link
    In this thesis we explore general aspects of the entanglement entropy (EE) for Conformal Field Theories (CFTs) dual to Cubic Curvature Gravity. We derived a covariant expression for the EE by using a scheme inherited from the bulk renormalization method through extrinsic counterterms. We evaluate this functional in different entangling regions to calculate CFT data. In particular, we compute the t4t_4 coefficient of the 3-point function of the stress-tensor correlator by considering a deformed entangling region. We observe that there is a discrepancy between the outcomes attained through the employment of the EE functional for minimal and non-minimal splittings. We find that only the t4t_4 obtained from the non-minimal functional agrees with previous results in the literature that were computed by splitting-independent CFT methods for specific theories such as the massless graviton case.Comment: 72 pages, 6 figures, MSc Thesi

    The self-confinement of electrons and positrons from dark matter

    Full text link
    Radiative emissions from electrons and positrons generated by dark matter (DM) annihilation or decay are one of the most investigated signals in indirect searches of WIMPs. Ideal targets must have large ratio of DM to baryonic matter. However, such ``dark'' systems have a poorly known level of magnetic turbulence, which determines the residence time of the electrons and positrons and therefore also the strength of the expected signal. This typically leads to significant uncertainties in the derived DM bounds. In a novel approach, we compute the self-confinement of the DM-induced electrons and positrons. Indeed, they themselves generate irregularities in the magnetic field, thus setting a lower limit on the presence of the magnetic turbulence. We specifically apply this approach to dwarf spheroidal galaxies. Finally, by comparing the expected synchrotron emission with radio data from the direction of the Draco galaxy collected at the Giant Metre Radio Telescope, we show that the proposed approach can be used to set robust and competitive bounds on WIMP DM.Comment: 18 pages, 10 figures. v2: minor revision, matches published versio
    • …
    corecore