Inferring Fundamental Spacetime Symmetries with Gravitational-Wave Memory: From LISA to the Einstein Telescope.

We revisit gravitational wave (GW) memory as the key to measuring spacetime symmetries, extending beyond its traditional role in GW searches. In particular, we show how these symmetries may be probed via displacement and spin memory observations, respectively. We further find that the Einstein Telescope's (ET) sensitivity enables constraining the strain amplitude of a displacement memory to 2% and that of spin memory to 22%. Finally, we point out that neglecting memory could lead to an overestimation of measurement uncertainties for parameters of binary black hole (BBH) mergers by about 10% in ET.

Second Data Release from the European Pulsar Timing Array: Challenging the Ultralight Dark Matter Paradigm.

Pulsar Timing Array experiments probe the presence of possible scalar or pseudoscalar ultralight dark matter particles through decade-long timing of an ensemble of galactic millisecond radio pulsars. With the second data release of the European Pulsar Timing Array, we focus on the most robust scenario, in which dark matter interacts only gravitationally with ordinary baryonic matter. Our results show that ultralight particles with masses 10^{-24.0} eV≲m≲10^{-23.3} eV cannot constitute 100% of the measured local dark matter density, but can have at most local density ρ≲0.3 GeV/cm^{3}.

Constraining Cosmological Phase Transitions with the Parkes Pulsar Timing Array.

A cosmological first-order phase transition is expected to produce a stochastic gravitational wave background. If the phase transition temperature is on the MeV scale, the power spectrum of the induced stochastic gravitational waves peaks around nanohertz frequencies, and can thus be probed with high-precision pulsar timing observations. We search for such a stochastic gravitational wave background with the latest data set of the Parkes Pulsar Timing Array. We find no evidence for a Hellings-Downs spatial correlation as expected for a stochastic gravitational wave background. Therefore, we present constraints on first-order phase transition model parameters. Our analysis shows that pulsar timing is particularly sensitive to the low-temperature (T∼1-100 MeV) phase transition with a duration (ß/H_{*})^{-1}∼10^{-2}-10^{-1} and therefore can be used to constrain the dark and QCD phase transitions.

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)x(LiNbO3)100-x Nanocomposite Memristors.

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100-x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.