Related papers: Performance analysis of the Karhunen-Lo\`{e}ve Tra…
We present the optimal reconstruction of the thermal Sunyaev-Zel'dovich (tSZ) effect signal based on the combination of a heterogeneous dataset consisting of ACT and Planck data, with different numbers of channels, angular resolutions and…
We report results from numerical simulations assessing astrometry measurements with the Multiconjugate Adaptive Optics Relay for ELT Observations (MORFEO) instrument on the Extremely Large Telescope (ELT). Using the Advanced Exposure Time…
In this paper a homomorphic filtering scheme is proposed to improve the estimation of the planet/star radius ratio in astronomical transit signals. The idea is to reduce the effect of the short-term earth atmosphere variations. A two-step…
Levenshtein transformer (LevT) is a non-autoregressive machine translation model with high decoding efficiency and comparable translation quality in terms of bleu score, due to its parallel decoding and iterative refinement procedure. Are…
This study tackles the efficient estimation of Kullback-Leibler (KL) Divergence in Dirichlet Mixture Models (DMM), crucial for clustering compositional data. Despite the significance of DMMs, obtaining an analytically tractable solution for…
We present a new matched filter algorithm for direct detection of point sources in the immediate vicinity of bright stars. The stellar Point Spread Function (PSF) is first subtracted using a Karhunen-Lo\'eve Image Processing (KLIP)…
KMOS (K-Band Multi Object Spectrograph) is a novel integral field spectrograph installed in the VLT's ANTU unit. The instrument offers an ability to observe 24 2.8"$\times$2.8" sub-fields positionable within a 7.2' patrol field, each…
CALET (CALorimetric Electron Telescope) is a high energy astroparticle physics experiment planned for a long exposure mission aboard the International Space Station (ISS) by the Japanese Aerospace Exploration Agency, in collaboration with…
With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine…
Exoplanet imaging is a major challenge in astrophysics due to the need for high angular resolution and high contrast. We present a multi-scale statistical model for the nuisance component corrupting multivariate image series at high…
Future Square Kilometre Array (SKA) surveys are expected to generate huge datasets of 21cm maps on cosmological scales from the Epoch of Reionization (EoR). We assess the viability of exploiting machine learning techniques, namely,…
Cosmological variations in the fine structure constant, alpha, can be probed through precise velocity measurements of metallic absorption lines from intervening gas clouds seen in spectra of distant quasars. Data from the Keck/HIRES…
The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data. In this work, a denoising method is proposed based on a…
We present the current status of the analysis of about 1.7 billion KSKL pair events collected at DAFNE with the KLOE detector to determine the branching ratio of KS -> pi e nu decay and the lepton charge asymmetry. This sample is ~4 times…
The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data,…
The MIEZE (Modulation of Intensity with Zero Effort) technique is a variant of neutron resonance spin echo (NRSE), which has proven to be a unique neutron scattering technique for measuring with high energy resolution in magnetic fields.…
This paper discusses the role of Transfer Learning (TL) and transformers in cancer detection based on image analysis. With the enormous evolution of cancer patients, the identification of cancer cells in a patient's body has emerged as a…
Deep learning techniques have been well explored in the transiting exoplanet field; however, previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well proven object…
Multi-scale decomposition has been an invaluable tool for the processing of physiological signals. Much focus in multi-scale decomposition for processing such signals have been based on scale-space theory and wavelet transforms. In this…
We use wavelet and curvelet transforms to extract signals of cosmic strings from cosmic microwave background (CMB) temperature anisotropy maps, and to study the limits on the cosmic string tension which various ongoing CMB temperature…