Related papers: Contrastive self-supervised convolutional autoenco…
In classification problems, supervised machine-learning methods outperform traditional algorithms, thanks to the ability of neural networks to learn complex patterns. However, in two-class classification tasks like anomaly or fraud…
The next Galactic core-collapse supernova (CCSN) presents a once-in-a-lifetime opportunity to make astrophysical measurements using neutrinos, gravitational waves, and electromagnetic radiation. CCSNe local to the Milky Way are extremely…
Density estimation, compression and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these goals. Here, we present a novel class of generative models,…
The aim of this paper is to formalise the task of continual semi-supervised anomaly detection (CSAD), with the aim of highlighting the importance of such a problem formulation which assumes as close to real-world conditions as possible.…
Currently, the sub-60 Hz sensitivity of gravitational-wave (GW) detectors like Advanced LIGO is limited by the control noises from auxiliary degrees of freedom, which nonlinearly couple to the main GW readout. One particularly promising way…
We analyze the directional dependence of the gravitational wave (GW) emission from 15 3D neutrino radiation hydrodynamic simulations of core-collapse supernovae. Using spin weighted spherical harmonics, we develop a new analytic technique…
Given the incomplete sampling of spatial frequencies by radio interferometers, achieving precise restoration of astrophysical information remains challenging. To address this ill-posed problem, compressive sensing(CS) provides a robust…
With the advent of modern neutrino and gravitational wave detectors, the promise of multi-messenger detections of the next galactic core-collapse supernova has become very real. Such detections will give insight into the core-collapse…
Electroencephalography (EEG) is a prominent non-invasive neuroimaging technique providing insights into brain function. Unfortunately, EEG data exhibit a high degree of noise and variability across subjects hampering generalizable signal…
The increasing amount of data in astronomy provides great challenges for machine learning research. Previously, supervised learning methods achieved satisfactory recognition accuracy for the star-galaxy classification task, based on…
To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC). Our key finding is to learn such representations by separating…
Variational autoencoders (VAEs) are one of the powerful likelihood-based generative models with applications in many domains. However, they struggle to generate high-quality images, especially when samples are obtained from the prior…
We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the…
In this work, we apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from LIGO detectors. As novel contribution, we adopted a resampling…
RES-NOVA is a new proposed experiment for the investigation of astrophysical neutrino sources with archaeological Pb-based cryogenic detectors. RES-NOVA will exploit Coherent Elastic neutrino-Nucleus Scattering (CE$\nu$NS) as detection…
Core-collapse supernovae (CCSNe) are the explosive end-points of stellar evolution for $M_{ZAMS} \gtrsim 8$ $M_\odot$ stars. The cores of these stars collapse to neutron stars, a process in which high neutrino luminosity drives off the…
A few times a century, a core collapse supernova (CCSN) occurs in our galaxy. When such galactic CCSNe happen, over 99\% of its gravitational binding energy is released in the form of neutrinos. Over a period of tens of seconds, a powerful…
When applying the foreground removal methods to uncover the faint cosmological signal from the epoch of reionization (EoR), the foreground spectra are assumed to be smooth. However, this assumption can be seriously violated in practice…
Variational autoencoders (VAEs) are fundamental for generative modeling and image reconstruction, yet their performance often struggles to maintain high fidelity in reconstructions. This study introduces a hybrid model, quantum variational…
Type Ia supernovae (SNe Ia) are among preeminent distance ladders for precision cosmology due to their intrinsic brightness, which allows them to be observable at high redshifts. Their usefulness as unbiased estimators of absolute…