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.
Photometric decomposition is often used to study galaxy structure by modelling surface brightness distribution in its components. However, most of the relevant codes are designed for single-band analysis. I present IMFIT-MW: a lightweight, open source Python tool that extends IMFIT capabilities to simultaneously model multiwavelength images. IMFIT-MW is able to set shared parameters between filters and to impose smooth variation of them with wavelength. IMFIT-MW achieves this by using IMFIT to produce model brightness distributions for given parameters, whereas hyperparameters defining their wavelength dependence are fitted by an external optimizer. The code is available on GitHub (https://github.com/IVChugunov/IMFIT-MW). I provide a few examples of use, showing that IMFIT-MW matches GALFITM in single-thread performance and can have advantage of multithreading. In a broader sense, I demonstrate the possibility to extend IMFIT features even without modifying its source code.
Quasi-Periodic Pulsations (QPPs) in solar and stellar flares are key phenomena reflecting magnetic reconnection processes. However, their non-stationary nature, low signal-to-noise ratio, and other characteristics make reliable detection highly challenging. Traditional detection methods (such as Fourier transform and wavelet analysis) rely on manual parameter setting, making it difficult to achieve high-precision automated detection. This study proposes a deep learning method based on a ResNet-Transformer hybrid model to achieve automated detection of quasi-periodic pulsations in solar flares. This model combines the local feature extraction capability of Residual Networks (ResNet) with the advantage of Transformer structures in modeling long-range dependencies and introduces Fast Fourier Transform to enhance the recognition of periodic features. The detection accuracy on the synthetic dataset achieves 90.1%, which is higher than that of the Fully Convolutional Network (FCN), demonstrating its potential as an effective tool for the automated detection of QPPs in solar and stellar flares.
