Searches for gravitational waves from compact binary coalescences usually rely on template banks that assume the binaries are on nearly circular orbits. In contrast, systems assembled through dynamical capture or in very dense stellar environments can retain significant orbital eccentricity, producing signal shapes that are poorly matched by these standard “matched-filter searches”. This study investigates how orbital eccentricity alters both waveform structure and detection performance for three binary black hole systems: an equal-mass (30,30) M⊙a highly asymmetric (30,10)M⊙, and a moderately asymmetric (70,30) M⊙configuration. Time-domain gravitational wave signals are produced with the EccentricTD approximant implemented in PyCBC over a range of eccentricities, then injected into stationary Gaussian noise coloured by the aLIGO Zero Detuned High Power sensitivity curve. The noisy data are processed with matched filtering using standard quasi-circular SEOBNRv4_opt templates. Results show that higher eccentricity generates waveforms with larger peak strain, more pronounced chirp modulations, and substantially reduced durations. At large eccentricities, the matched-filter SNR shows a clear but non-monotonic drop, driven mainly by waveform dephasing and the consequent loss of overlap between eccentric signals and circular templates, with the most severe impact seen for the high-mass asymmetric configuration.A comparison with data from GW150914 serves as a qualitative sanity check for the circular waveform model used for this event and illustrates that the data-conditioning workflow maintains the essential signal morphology, while the eccentric simulations highlight how even moderate eccentricity can produce waveforms that depart sharply from the quasi-circular chirp seen in current detections.
We present a new method to estimate the spectral resolution of a radio data cube from its noise properties. We assume that the data cube is the result of the convolution of a spectrally-uncorrelated cube with a constant convolution kernel K. We demonstrate that K and the resolution, defined as K's integral, can be estimated thanks to the analytical relation between the autocorrelation of the noise spectra extracted from the cube and the power spectrum of the kernel. We discuss a number of algorithms that make this method robust in the realistic case of a finite number of spectra and channels, real signal and artefacts, and we present the results obtained for a number of datasets from the literature. We further highlight that this method is applicable to any data cube that meets our assumptions independently of the observing frequency or the exact nature of the cube's "third" axis, e.g., it could be used to measure the time resolution of transient-search data cubes. We make this method available as part of the open-source software tool spectre.
In this paper, a numerical scheme for solving coupled systems of second-order Emden–Fowler equations using the higher-order Haar wavelet method is developed. Coupled Emden–Fowler systems are most commonly found in astrophysics, nuclear physics, and fluid mechanics. These equations play an important role in mathematical biology and combustion theory. The proposed method uses a Haar wavelet to approximate the fourth-order derivative of unknown functions from the system of differential equations and convert them into an algebraic system. Subsequently, two solvers, namely the Newton–Raphson and Newton–Krylov, are used to solve the resulting nonlinear algebraic system. The Emden–Fowler equations are difficult to solve because of their strong nonlinearity and singularity. Therefore, several benchmark problems have been solved using the proposed scheme to show the effectiveness of the method. While this problem has been solved using other existing methods like the Haar wavelet collocation method and the artificial neural networks, it is to be noted that the results from the higher order Haar wavelet method are in excellent agreement and superior to the existing methods.
The sheer magnitude of data to be collected by the Square Kilometre Array (SKA) telescopes necessitates robust and dependable strategies for the effective management and processing of data. In this context, the SKA Regional Centre Network (SRCNet), a collaborative global infrastructure comprising multiple regional centres distributed worldwide, is expected to play a central role in handling and analysing the extensive data streams generated by the telescopes, thereby enabling significant advances in astronomical research and exploration. This paper introduces a semantic model for the SRCNet that is sufficiently detailed to describe its architecture, data distribution, and computing services. By explicitly defining nodes, resources, relationships, and workflows, the model provides a foundation for interoperability and efficient resource management within the distributed network. The model supports two configurations, centralised and decentralised, depending on the location of data storage. In future deployments, a service broker will be able to plan workflows efficiently by querying nodes for real-time system availability. To validate and assess the integrity of the model, consistency tests are conducted using SPARQL queries. This work advances semantic modelling in astronomy by presenting, for the first time, a semantic model tailored to the SRCNet. The proposed model serves as a precursor to a more formal mathematical representation of the network and establishes a foundational framework for the development of a future service broker.
The Qitai Radio Telescope (QTT), a 110 m fully steerable dish under construction in China, will produce extremely large volumes of observational data that must be transferred efficiently and reliably from the observatory site to remote data center. The Next Generation Archive System (NGAS) is a widely used archival system in astronomy, but it suffers from stop-and-wait flow control, Global Interpreter Lock (GIL) bottlenecks, and a lack of backpressure, which limit its throughput and scalability for next-generation facilities. We present INGAD (Improved NGAS based on Asynchronous Acknowledgment Channel Decoupling), a new data transfer architecture that fully decouples the data and acknowledgment paths. The data channel uses ZeroMQ PUSH-PULL sockets for high-speed asynchronous transfer with inherent backpressure, while the acknowledgment channel employs a persistent RabbitMQ message queue to deliver ACKs independently without blocking the sender. A bounded in-flight sliding window limits the number of unacknowledged files, preventing unbounded memory growth. Dynamic timeout estimation based on historical ACK delays, together with exponential backoff retransmission and idempotent deduplication, ensures reliable delivery under variable network conditions. We evaluate INGAD using both synthetic simulation data and real ASKAP observations over a 5 Gbit/s network. Compared to the original NGAS, INGAD improves throughput by 115%–132% for small files and 22%–28% for large and ultra-large files in the simulation campaign (with gains of 5.8%–115% over the coupled ZeroMQ baseline). In the real ASKAP data campaign, throughput gains over NGAS are 22%–27% for small files and 16%–22% for large/ultra-large files, reducing total transfer time by up to 56% for small files and 13% for ultra-large files. Theoretical modeling confirms that INGAD achieves 39%–74.5% of the 5 GbE link limit, with prediction errors below 3.2%. Memory usage remains bounded and predictable across all tests, with no out-of-memory events. INGAD provides a scalable, robust, and high-performance solution for astronomical data transfer, making it particularly well suited for the QTT and other large-scale observatories requiring efficient remote data synchronization.
