Related papers: EOVSA Implementation of a Spectral Kurtosis Correl…
The real-space Green's function code FEFF has been extensively developed and used for calculations of x-ray and related spectra, including x-ray absorption (XAS), x-ray emission (XES), inelastic x-ray scattering, and electron energy loss…
One of the major priorities of international radio astronomy is to study the early universe through the detection of the 21 cm HI line from the epoch of reionisation (EoR). Due to the weak nature of the 21 cm signal, an important part in…
The feature based spatial verification method SAL is applied to cloud data, i.e. two-dimensional spatial fields of total cloud cover and spectral radiance. Model output is obtained from the COSMO-DE forward operator SynSat and compared to…
We present ORACLE, the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable astrophysical phenomena. ORACLE is a recurrent neural network with Gated Recurrent Units (GRUs), and has…
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of pattern recognition and classification tasks. In this work, we consider extending classic SVMs with quantum kernels and applying them to…
Quasars (QSOs) are extremely luminous active galatic nuclei currently observed up to redshift $z=7.642$. As such, they have the potential to be the next rung of the cosmic distance ladder beyond SNe Ia, if they can reliably be used as…
The analysis of optical spectra - emission or absorption -- has been arguably the most powerful approach for discovering and understanding matters. The invention and development of many kinds of spectrometers have equipped us with versatile…
Spectra derived from fast Fourier transform (FFT) analysis of time-domain data intrinsically contain statistical fluctuations whose distribution depends on the number of accumulated spectra contributing to a measurement. The tail of this…
This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency. The key technical challenge is to extract actionable insights from the dependency tensor…
The Fast Fourier Transform (FFT) is a numerical operation that transforms a function into a form comprised of its constituent frequencies and is an integral part of scientific computation and data analysis. The objective of our work is to…
The advent of large-scale surveys requires efficient ML techniques to exploit the information of massive datasets. We present OJALA, a transformer-based autoregressive foundation model designed to simultaneously classify astronomical…
Multi-photon correlations from quantum emitters coupled to vibrational environments lie beyond the reach of standard tools such as the quantum regression theorem (QRT). Here, we introduce a Markovian framework for computing…
The Adaptive Optics Facility (AOF) project envisages transforming one of the VLT units into an adaptive telescope and providing its ESO (European Southern Observatory) second generation instruments with turbulence corrected wavefronts. For…
Frequency-dependent correlations, such as the spectral function and the dynamical structure factor, help understand condensed matter experiments. Within the density matrix renormalization group (DMRG) framework, an accurate method for…
The SKA will build upon early detections of the EoR by precursor instruments, such as MWA, PAPER, and LOFAR, and planned instruments, such as HERA, to make the first high signal-to-noise measurements of fluctuations in the 21 cm brightness…
Fourier analysis plays a major role in the analysis and understanding of many phenomena in physics and contemporary engineering. However, students, who have often discovered this notion through numerical tools, do not necessarily understand…
The SKA will be transformational for many areas of science, but in particular for the study of neutron stars and their usage as tools for fundamental physics in the form of radio pulsars. Since the last science case for the SKA, numerous…
Synthetic Aperture Radar (SAR) plays a vital role in remote sensing due to its ability to capture high-resolution images regardless of weather conditions or daylight. However, to transform the raw SAR signals into interpretable imagery,…
We report on a calculation to show that the Fourier transform of the Deeply Virtual Compton Scattering (DVCS) amplitude with respect to the skewness variable \zeta at fixed invariant momentum transfer squared t gives results that are…
This work investigates symbolic regression (SR) as an interpretable alternative to black-box machine learning for the classification of stars, galaxies, and quasars in the Sloan Digital Sky Survey Data Release 17 (SDSS DR17). We conduct a…