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#autoencoders

4 curated events
papersTODAY 04:00 UTC

Autoencoder Method Targets Quasinormal Mode Parameter Estimation in Ringdown Signals

A new arXiv preprint describes using an autoencoder to estimate the parameters of ringdown gravitational waves, which are modeled as combinations of quasinormal modes carrying information about the remnant Kerr black hole. The work focuses on reliably extracting multiple quasinormal modes, a task that is difficult with conventional fitting methods. The paper appears in the cross-listed machine learning category.

gravitational-waves/autoencoders/physics/research
papersTODAY 04:00 UTC

Deep Autoencoder Estimates Intrinsic Dimensionality of FPUT Trajectories

The paper applies a deep autoencoder to estimate the intrinsic dimensionality of high-dimensional trajectories from the Fermi-Pasta-Ulam-Tsingou beta model with 32 oscillators. The dataset spans roughly 4 million data points, and the authors take a nonlinear approach to characterize the underlying low-dimensional structure of the dynamics.

autoencoders/physics/dimensionality-reduction/research
papersTODAY 04:00 UTC

Autoencoder Method Estimates Parameters of Overlapping Damped Sinusoidal Signals

A new arXiv paper proposes an autoencoder-based approach for estimating parameters of damped sinusoidal signals that contain multiple overlapping components and decay quickly. Such signals appear across many physical systems, where their parameters reveal underlying physical properties, but conventional estimation struggles when components superpose or fade fast. The work is a replacement submission to arXiv cs.LG and has not necessarily been peer reviewed.

research/signal-processing/autoencoders/arxiv
papersSEP 12 04:00 UTC

Variational Autoencoders Improve Faint Object Detection in Space Surveillance

A new arXiv preprint describes a deep-learning pipeline that boosts detection of dim moving objects in optical space situational awareness imagery. The method combines automated star removal with background reconstruction to help recover objects at low signal-to-noise ratios. The authors frame the work as a step toward more reliable tracking of faint orbital targets.

arxiv/computer-vision/space/autoencoders