arXiv subjects

Beka Modrekiladze

Publications and source records attributed to Beka Modrekiladze.

9 recordsLinked to original sources

arXiv2026

Effective Field Theory of Gravity in Relativistic Media

We develop an effective field theory of gravity in relativistic media. Integrating out the medium leaves vacuum gravity with updated Feynman rules: a graviton propagator dressed by the stress-energy two-point function of the environment, medium-induced bulk graviton vertices from higher correlators, and generalized worldline couplings encoded in matched Wilson coefficients. The diagram topologies are unchanged from vacuum, so different media (perfect fluids, collisionless matter, coherent scalar fields such as wave dark matter) are not different theories but different correlators inserted into the same diagrams. We derive the in-medium rules for a relativistic fluid and obtain the full 1PN Einstein-Infeld-Hoffmann potential, the 1PN Stokes drag, the gravitational self-energy and a generalized Christodoulou memory whose new tensor structure records the direction of the surrounding flow, turning the permanent strain into an $\textit{astrophysical weathervane}$. The same rules activate phenomena forbidden in vacuum: the sound pole converts the symmetry-protected, non-running black-hole Love number into a resonant, running one, and opens the channel $h\to hh$ in a moving medium, yielding a closed-form decay rate and a birefringent $\textit{gravitational opacity}$ set by the local flow geometry. We assess observational prospects, from dephasing and tidal resonances within reach of the Einstein Telescope and LISA to proof-of-principle memory and opacity signatures.

gr-qc↗

arXiv2026

A Unified Treatment of the Self-Force Problem

This paper introduces formalism, based on worldline effective field theory, which allows one to systematically calculate the gravitational self-force on a compact object immersed in an environment with non-vanishing stress energy. Using the closed time path integral we present a universal effective action that can be used to calculate the equations of motion of a compact object due to its interaction with the environment to any order in the relevant expansion parameters, the relative importance of which depends upon the choice of environment. We show that the leading order equations of motion can be universally written in terms of the retarded two-point function of the stress-energy tensor for the environment. The resulting action can be used to calculate both dissipative (dynamical friction) and conservative forces in a completely relativistic fashion. We demonstrate the utility of the result by calculating the dissipative force due to dust, an inviscid fluid, and a coherent field. Our results agree with those previously derived in the literature. We furthermore show that the famous ``Coulomb Log" found by Chandrasekhar should be interpreted as a renormalization group log due to a UV divergence that renormalizes the dissipative part of the in-in action. We then prove that this log is universal in the Newtonian limit in that its value is universal, for a generic class of environments and trajectories. This log is the leading log in an RG flow in the dissipative action that has yet to be explored.

gr-qc↗

arXiv2026

A fast, differentiable neural-network surrogate for precessing binary black-hole waveforms

Gravitational-wave parameter estimation requires millions of waveform evaluations per event, a cost that constrains real-time inference and population studies. We present a fast, fully differentiable neural-network surrogate for the precessing numerical-relativity model \NRSur{}, spanning its full intrinsic parameter space $\lambda=(q,\,\boldsymbol{\chi}_1,\,\boldsymbol{\chi}_2)$ together with the reference orbital frequency $\omega_0$. Rather than a single polarization at a fixed orientation, the surrogate predicts the \emph{inertial-frame spherical-harmonic modes} $h_{\ell m}$ ($\ell\leq 4$), so that both polarizations $h_+,h_\times$ at an arbitrary orientation $(\iota,\varphi,\psi)$ are reconstructed from one network evaluation through an analytic, differentiable mode-to-strain projection. Trained on $6\times10^5$ waveforms, it attains a fixed-orientation match of mean $0.975$ (median $0.988$) and an orientation-averaged match of mean $0.940$ (median $0.975$) for $q\in[1,4]$, $|\boldsymbol{\chi}_{1,2}|\leq 0.8$, while keeping the overall strain amplitude physical (median ratio $0.98$). It generates a waveform in $\SI{12}{ms}$ (single) and ${\sim}3.5\times10^4$ per second in batches on a single GPU. Because the mode-to-strain projection is analytic, the surrogate is differentiable in both intrinsic and extrinsic parameters, yielding a full 13-dimensional Fisher matrix (validated against finite differences) and gradient-based (HMC/NUTS) parameter estimation.

gr-qc↗

arXiv2024

On the Motion of Compact Objects in Relativistic Viscous Fluids

We present a world-line effective field theory of compact objects moving relativistically through a viscous fluid. The theory is valid when velocity gradients are small compared to the inverse size of the object. Working within the EFT eliminates the need to solve a boundary value problem by turning all interactions between the fluid and the object into a source term in the action. We use the EFT to derive the relativistic equations of motion for a compact object immersed in a viscous fluid in a curved background.

gr-qc↗

arXiv2024

Dual Space Training for GANs: A Pathway to Efficient and Creative Generative Models

Generative Adversarial Networks (GANs) have demonstrated remarkable advancements in generative modeling; however, their training is often resource-intensive, requiring extensive computational time and hundreds of thousands of epochs. This paper proposes a novel optimization approach that transforms the training process by operating within a dual space of the initial data using invertible mappings, specifically autoencoders. By training GANs on the encoded representations in the dual space, which encapsulate the most salient features of the data, the generative process becomes significantly more efficient and potentially reveals underlying patterns beyond human recognition. This approach not only enhances training speed and resource usage but also explores the philosophical question of whether models can generate insights that transcend the human intelligence while being limited by the human-generated data.

cs.LG↗

arXiv2024

Transfer Learning Adapts to Changing PSD in Gravitational Wave Data

The detection of gravitational waves has opened unparalleled opportunities for observing the universe, particularly through the study of black hole inspirals. These events serve as unique laboratories to explore the laws of physics under conditions of extreme energies. However, significant noise in gravitational wave (GW) data from observatories such as Advanced LIGO and Virgo poses major challenges in signal identification. Traditional noise suppression methods often fall short in fully addressing the non-Gaussian effects in the data, including the fluctuations in noise power spectral density (PSD) over short time intervals. These challenges have led to the exploration of an AI approach that, while overcoming previous obstacles, introduced its own challenges, such as scalability, reliability issues, and the vanishing gradient problem. Our approach addresses these issues through a simplified architecture. To compensate for the potential limitations of a simpler model, we have developed a novel training methodology that enables it to accurately detect gravitational waves amidst highly complex noise. Employing this strategy, our model achieves over 99% accuracy in non-white noise scenarios and shows remarkable adaptability to changing noise PSD conditions. By leveraging the principles of transfer learning, our model quickly adapts to new noise profiles with just a few epochs of fine-tuning, facilitating real-time applications in dynamically changing noise environments.

gr-qc↗

arXiv2023

Gravitational Wave Signals from Finite Size Effects in Spinning Binary Inspirals Including Parity Violating Constituents

We generalize the world line EFT formalism to account for parity violating finite size effects. Results are presented for potentials and radiating moments of a binary inspiral for the parity conserving sector, and agreement is found with, previous calculations. Furthermore, we generate new results in this sector, calculating the current quadrupole moment induced by finite size gravitomagnetic effects. We also present novel results for parity violating sources, which might be due to beyond standard model physics, and show that they generate GW signals with the unique signature that the current-moment appears at 0.5PN order earlier relative to the mass-moment in the PN expansion. Parity violation also induces a new type of potential, which is proportional to the $\textbf{S}\cdot \textbf{r}$. Finally, we present new results for the dissipative force for parity violating constituents, which leads to the curious signature of a force normal to the orbit.

hep-th↗

arXiv2022

Probing the Information-Probabilistic Description

The information conservation principle is probed for classically isolated systems, like the Hubble sphere and black holes, for which the rise of entanglement entropy across their horizons is expected. We accept the analogy of Landauer's principle that entanglement information should introduce some negative potential energy, which corresponds to the positive energy of measurements that destroy this quantum behavior. We estimated these dark-energy-like contributions and found that they can explain the dark energy of the Universe and also are able to resolve the observed superluminal motion and redshift controversies for black holes.

gr-qc↗

arXiv2019

Gravitational Field of a Spherical Perfect Fluid

Analyzing the spacetime for a static spherically distributed perfect fluid we show that the smooth matching of the interior and exterior metrics for a realistic source is possible only for the distances from the origin that exceeds the photon sphere radius for this object.

physics.gen-ph↗