15,967 research outputs found
Dynamical Projective Operatorial Approach (DPOA) for out-of-equilibrium systems and its application to TR-ARPES
Efficiently simulating real materials under the application of a
time-dependent field and computing reliably the evolution over time of relevant
response functions, such as the TR-ARPES signal or differential transient
optical properties, has become one of the main concerns of modern condensed
matter theory in response to the recent developments in all areas of
experimental out-of-equilibrium physics. In this manuscript, we propose a novel
model-Hamiltonian method, the dynamical projective operatorial approach (DPOA),
designed and developed to overcome some of the limitations and drawbacks of
currently available methods. Relying on (i) many-body second-quantization
formalism and composite operators, DPOA is in principle capable of handling
both weakly and strongly correlated systems, (ii) tight-binding approach and
wannierization of DFT band structures, DPOA naturally deals with the complexity
and the very many degrees of freedom of real materials, (iii) dipole gauge and
Peierls substitution, DPOA is built to address pumped systems and, in
particular, pump-probe spectroscopies, (iv) a Peierls expansion we have devised
ad hoc, DPOA is numerically extremely efficient and fast. The latter expansion
clarifies how single- and multi-photon resonances, rigid shifts, band
dressings, and different types of sidebands emerge and allows understanding the
related phenomenologies. Comparing DPOA to the single-particle density-matrix
approach and the Houston method (this latter is generalized to
second-quantization formalism), we show how it can compute multi-particle
multi-time correlation functions and go well beyond these approaches for real
materials. We also propose protocols for evaluating the strength of single- and
multi-photon resonances and for assigning the residual excited electronic
population at each crystal momentum and band to a specific excitation process.
The expression for ...Comment: 27 pages, 15 figures, 38 panel
Magnetic excitation spectrum and Hamiltonian of the quantum spin chain compound BaCuTe2O6
AbstractThe magnetic excitation spectrum and Hamiltonian of the quantum magnet BaCuTe2O6 is studied by inelastic neutron scattering (INS) and density functional theory (DFT). INS on powder and single crystal samples reveals overlapping spinon continua—the spectrum of an antiferromagnetic spin-1/2 spin chain—due to equivalent chains running along the a, b, and c directions. Long-range magnetic order onsets below TN=6.3 K due to interchain interactions, and is accompanied by the emergence of sharp spin-wave excitations, which replace the continua at low energies. The spin-wave spectrum is highly complex and was successfully modelled achieving excellent agreement with the data. The extracted interactions reveal an intrachain interaction, J3=2.9 meV, while the antiferromagnetic hyperkagome interaction J2 is the subleading interaction responsible for coupling the chains together in a frustrated way. DFT calculations reveal a similar picture for BaCuTe2O6 of dominant J3 and subleading J2 antiferromagnetic interactions and also indicate a high sensitivity of the interactions to small changes of structure, which could explain the very different Hamiltonians observed in the sister compounds SrCuTe2O6 and PbCuTe2O6
Gravitational wave memory beyond general relativity
Gravitational wave memory is a nonoscillatory correction to the gravitational
wave strain predicted by general relativity, which has yet to be detected.
Within general relativity, its dominant component, known as the null memory,
can be understood as arising from the backreaction of the energy carried by
gravitational waves, and therefore it corresponds to a direct manifestation of
the nonlinearity of the theory. In this paper, we investigate the null-memory
prediction in a broad class of modified gravity theories, with the aim of
exploring potential lessons to be learned from future measurements of the
memory effect. Based on Isaacson's approach to the leading-order field
equations, we in particular compute the null memory for the most general
scalar-vector-tensor theory with second-order equations of motion and vanishing
field potentials. We find that the functional form of the null memory is only
modified through the potential presence of additional radiative null energy
sources in the theory. We subsequently generalize this result by proving a
theorem that states that the simple structure of the tensor null-memory
equation remains unaltered in any metric theory whose massless gravitational
fields satisfy decoupled wave equations to first order in perturbation theory,
which encompasses a large class of viable extensions to general relativity.Comment: 39 page
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
The future of cosmology? A case for CMB spectral distortions
This thesis treats the topic of CMB Spectral Distortions (SDs), which
represent any deviation from a pure black body shape of the CMB energy
spectrum. As such, they can be used to probe the inflationary, expansion and
thermal evolution of the universe both within CDM and beyond it. The
currently missing observation of this rich probe of the universe makes of it an
ideal target for future observational campaigns. In fact, while the
CDM signal guarantees a discovery, the sensitivity to a wide variety
of new physics opens the door to an enormous uncharted territory. In light of
these considerations, the thesis opens by reviewing the topic of CMB SDs in a
pedagogical and illustrative fashion, aimed at waking the interest of the
broader community. This introductory premise sets the stage for the first main
contribution of the thesis to the field of SDs: their implementation in the
Boltzmann solver CLASS and the parameter inference code MontePython. The
CLASS+MontePython pipeline is publicly available, fast, it includes all sources
of SDs within CDM and many others beyond that, and allows to
consistently account for any observational setup. By means of these numerical
tools, the second main contribution of the thesis consists in showcasing the
versatility and competitiveness of SDs for several cosmological models as well
as for a number of different mission designs. Among others, the results cover
features in the primordial power spectrum, primordial gravitational waves,
non-standard dark matter properties, primordial black holes, primordial
magnetic fields and Hubble tension. Finally, the manuscript is disseminated
with (20) follow-up ideas that naturally extend the work carried out so far,
highlighting how rich of unexplored possibilities the field of CMB SDs still
is. The hope is that these suggestions will become a propeller for further
interesting developments.Comment: PhD thesis. Pedagogical review of theory, experimental status and
numerical tools (CLASS+MontePython) with broad overview of applications.
Includes 20 original follow-up idea
Reinforcement learning in large state action spaces
Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios.
This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory).
In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications
Vegetation Reconfigures Barrier Coasts and Affects Tidal Basin Infilling Under Sea Level Rise
This is the final version. Available on open access from the American Geophysical Union via the DOI in this recordData Availability Statement:
Delft3D steering settings from our reference scenarios (model 1 and model 5) and main model results are available at the repository YODA (Boechat Albernaz, 2022). Delft3D source code is freely distributed and available at the Deltares (SVN) repository from Boechat Albernaz (2019). The vegetation module is also available at Brückner (2020) based on Brückner et al. (2019). Data from natural systems (see Figure 9) were obtained from DGT (2011), Richardson et al. (2018), Donatelli et al. (2020), and Sievers et al. (2020).Worldwide, many tidal basins associated with barrier coasts have infilled over the past millennia due to the combination of sediment supply, wave-tidal sediment transport, and eco-engineering effects of vegetation. However, the biogeomorphological interactions between saltmarsh and the morphodynamics of an entire coastal barrier system are poorly understood, especially under sea level rise (SLR). Here, we study the evolution of a barrier coast for combinations of mud availability, presence of vegetation, and SLR. We developed a novel biogeomorphological model of an idealized barrier coast enclosing a tidal basin with sandy-clayey sediments that was subjected to tides and waves for a century. The morphodynamic Delft3D model was coupled to a vegetation code which accounts for the dynamics of marsh-type vegetation. Initially, vegetation contributed to reducing the tidal prism while sediment was imported. However, with SLR this trend was reversed and the tidal basins started to export sediment for vegetated runs after about 50–60 years while the unvegetated scenarios continued to infill in pace with the SLR. The sediment export was caused by cascading biomorphodynamic feedback effects triggered by vegetation which modified channel and shoal dynamics. Even under higher mud supply, the SLR resulted in vegetation collapse. The hypsometries, similar to natural systems, showed that vegetated systems converge to an alternative stable state condition. We conclude that the long-term resilience of the tidal basin associated with sediment infilling under SLR can be reduced by cascading large-scale effects of vegetation on the morphodynamics of barrier coasts.European Research Council (ERC
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
We study robust reinforcement learning (RL) with the goal of determining a
well-performing policy that is robust against model mismatch between the
training simulator and the testing environment. Previous policy-based robust RL
algorithms mainly focus on the tabular setting under uncertainty sets that
facilitate robust policy evaluation, but are no longer tractable when the
number of states scales up. To this end, we propose two novel uncertainty set
formulations, one based on double sampling and the other on an integral
probability metric. Both make large-scale robust RL tractable even when one
only has access to a simulator. We propose a robust natural actor-critic (RNAC)
approach that incorporates the new uncertainty sets and employs function
approximation. We provide finite-time convergence guarantees for the proposed
RNAC algorithm to the optimal robust policy within the function approximation
error. Finally, we demonstrate the robust performance of the policy learned by
our proposed RNAC approach in multiple MuJoCo environments and a real-world
TurtleBot navigation task
Jahn-Teller distortion driven ferromagnetism in a perovskite fluoride monolayer
The Jahn-Teller distortion and the resulting orbital order usually cause some
fascinating correlated electronic behaviors, and generally lead to
antiferromagnetism in perovskite bulks. Here we demonstrate that the
Jahn-Teller distortion present in the perovskite fluoride KCrF bulk can be
retained to the two-dimensional limit, resulting in a staggered orbital order
and ferromagnetism in the perovskite monolayer. Octahedral tilt and rotation
distortion also appear in the ground-state structure of the perovskite
monolayer, which have minor effects on the electronic and magnetic properties
with respect to the Jahn-Teller distortion. In addition, in the prototype phase
without structural distortion, the partial occupation of the orbitals
leads to a ferromagnetic metallic state. This work facilitates the design of
two-dimensional ferromagnets and functional properties based on Jahn-Teller
distortion and orbital orderComment: 8 pages, 5 figures, 1 tabl
New Building Blocks for Cancer Phototherapeutics: 5d Metallocorroles
Corroles are ring-contracted, triprotic analogues of porphyrins. This PhD study expands earlier knowledge in particular on ReO corroles. Early on, it became apparent that ReO corroles exhibit the highest phosphorescence quantum yields among all metallocorroles. They also sensitize singlet oxygen formation and serve as oxygen sensors and as triplet-triplet annihilation upconverters. I accordingly wanted to synthesize new classes of functionalized 5d corroles as well as to examine ReO corroles as photosensitizers in in vitro photodynamic therapy experiments. I found that amphiphilic meta/para-carboxyl-appended ReO triphenylcorroles exhibit high photocytotoxicity against multiple cancer cell lines. In the synthetic realm, one study examined electrophilic chlorination and bromination of ReO corroles. X-ray structures of ReO octachloro- and octabromocorroles yielded a host of insights into the conformational preferences of sterically hindered corrole derivatives. Another synthetic study afforded an innovative approach to water-soluble iridium corroles, involving the use of water-soluble axial ligands. I also undertook extensive studies of formylation of ReO and Au triarylcorroles, arriving at the rather elegant conclusion that whereas the former largely afford 3-monoformyl products, the latter preferentially yield 3,17-diformylproducts, presumably reflecting the higher nucleophilicity of the Au complexes. The formylcorrole products could be readily postfunctionalized, such as via the Knoevenagel reaction. The 5d formylcorroles should serve as valuable starting materials for bio- and nanoconjugated 5d metallocorroles for advanced, targeted cancer therapies. I feel privileged to have developed a new class of triplet photosensitizers – the ReO corroles – that to this day remain unique to our Tromsø laboratory. I am confident, however, that we shall soon see exciting applications of these compounds as advanced photodynamic, photothermal and multimodal cancer therapeutics
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