2,924 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Machine learning applications in search algorithms for gravitational waves from compact binary mergers
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
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
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
Recommended from our members
The Forward Physics Facility at the High-Luminosity LHC
High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF’s physics potential
30th European Congress on Obesity (ECO 2023)
This is the abstract book of 30th European Congress on Obesity (ECO 2023
Topological diffusive metal in amorphous transition metal mono-silicides
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
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
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 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 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
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
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