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The M\"ossbauer technique is proposed as an alternative experimental procedure to be used in the detection of Coherent Elastic $\nu$-Nucleus Scattering (CENNS). The $Z_{0}$-boson exchange is considered as a perturbation on the nuclear…
Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to obtain profiles of metabolites dissolved in biofluids such as cell supernatants. Methods for estimating metabolite concentrations from these spectra are presently…
In particle physics, experiments generate substantial amounts of data that can be difficult to process without preliminary scaling. To avoid losing potentially crucial data, experimental collaborations are studying novel techniques for…
In comparing the behavior of an energy spectrum to the predictions of random matrix theory one must transform the spectrum such that the averaged level spacing is constant, a procedure known as unfolding. Once energy spectrums belong to an…
The ASPECT-BET (An sdd-SPECTrometer for BETa decay studies) project aims to develop a novel technique for the precise measurement of forbidden $\beta$ spectra in the 10 keV - 1 MeV range. This technique uses a Silicon Drift Detector (SDD)…
Accounting for selection effects in supernova type Ia (SN Ia) cosmology is crucial for unbiased cosmological parameter inference -- even more so for the next generation of large, mostly photometric-only surveys. The conventional "bias…
Observations of type Ia supernovae (SNe Ia) have led to suggestions of multiple progenitor and explosion scenarios. Distinguishing between scenarios and tying specific SNe Ia to individual scenarios however has so far been challenging.…
We propose the use of isotopically highly enriched detectors for the precise study of coherent-elastic neutrino-nucleus scattering (CEvNS). CEvNS has been measured for the first time in CsI and recently confirmed with a liquid argon…
Underwater image enhancement (UIE) is a practically important yet underexplored application of spiking neural networks (SNNs), where the dominant degradations are large-scale and low-frequency, such as wavelength-dependent colour casts and…
Deep learning has revolutionized the early detection of breast cancer, resulting in a significant decrease in mortality rates. However, difficulties in obtaining annotations and huge variations in distribution between training sets and real…
In this paper we study the effect of, well-known, higher order corrections to the allowed beta decay spectrum on the determination of anti-neutrino spectra resulting from the decays of fission fragments. In particular, we try to estimate…
Monocular Depth and Surface Normals Estimation (MDSNE) is crucial for tasks such as 3D reconstruction, autonomous navigation, and underwater exploration. Current methods rely either on discriminative models, which struggle with transparent…
High-sensitivity clutter filtering is a fundamental step in ultrasound microvascular imaging. Singular value decomposition (SVD) and robust principal component analysis (rPCA) are the main clutter filtering strategies. However, both…
A new model for the nuclear elastic scattering of protons below 250 MeV has been recently included in FLUKA v4-4.0, motivated by the evaluation of radiation effects in electronics. Nonetheless, proton nuclear elastic scattering plays a…
Supernova models with a full spectral treatment of the neutrino transport are presented, employing the Prometheus/Vertex neutrino-hydrodynamics code with a ``ray-by-ray plus'' approximation for treating two- (or three-) dimensional…
A method for spatial deconvolution of spectra is presented. It follows the same fundamental principles as the ``MCS image deconvolution algorithm'' (Magain, Courbin, Sohy, 1998) and uses information contained in the spectrum of a reference…
We proposed a novel dense line spectrum super-resolution algorithm, the DMRA, that leverages dynamical multi-resolution of atoms technique to address the limitation of traditional compressed sensing methods when handling dense point-source…
Deep neural networks (DNNs) play an important role in machine learning due to its outstanding performance compared to other alternatives. However, DNNs are not suitable for safety-critical applications since DNNs can be easily fooled by…
Elastic scattering on nucleons, \nu N -> N \nu, is the dominant supernova (SN) opacity source for \mu and \tau neutrinos. The dominant energy- and number-changing processes were thought to be \nu e^- -> e^- \nu and \nu\bar \nu <-> e^+ e^-…
Resolving the small length-scale of thermonuclear detonation waves (TNDWs) in supernovae is currently not possible in multidimensional full-star simulations. Additionally, multidimensional simulations usually use small, oversimplistic…