Related papers: SNID-SAGE: A Modern Framework for Interactive Supe…

We present the largest and most homogeneous collection of near-infrared (NIR) spectra of Type Ia supernovae (SNe Ia): 339 spectra of 98 individual SNe obtained as part of the Carnegie Supernova Project-II. These spectra, obtained with the…

Type Ia Supernovae (SNeIa) provided the first evidence of an accelerated expansion of the universe and remain a valuable probe to cosmology. They are deemed standardizable candles due to the observed correlations between its luminosity and…

We study the characteristics of a narrow band type Ia supernova survey through simulations based on the upcoming Javalambre Physics of the accelerating universe Astrophysical Survey (J-PAS). This unique survey has the capabilities of…

In high-dimensional and high-stakes contexts, ensuring both rigorous statistical guarantees and interpretability in feature extraction from complex tabular data remains a formidable challenge. Traditional methods such as Principal Component…

Line Intensity Mapping (LIM) is a new observational technique that uses low-resolution observations of line emission to efficiently trace the large-scale structure of the Universe out to high redshift. Common mm/sub-mm emission lines are…

We present new techiques for improving the efficiency of supernova (SN) classification at high redshift using 64 candidates observed at Gemini North and South during the first year of the Supernova Legacy Survey (SNLS). The SNLS is an…

We develop a hybrid framework to identify kilonovae (KNe), using single-epoch, medium-band spectral energy distributions from the 7-Dimensional Telescope (7DT). The framework integrates an unsupervised anomaly classifier (\texttt{Isolation…

Wireless time-sensitive networking (WTSN) is essential for Industrial Internet of Things. We address the problem of minimizing time slots needed for WTSN transmissions while ensuring reliability subject to interference constraints -- an…

The significant variability in cell size and shape continues to pose a major obstacle in computer-assisted cancer detection on gigapixel Whole Slide Images (WSIs), due to cellular heterogeneity. Current CNN-Transformer hybrids use static…

We present a data-driven method based on long short-term memory (LSTM) neural networks to analyze spectral time series of Type Ia supernovae (SNe Ia). The dataset includes 3091 spectra from 361 individual SNe Ia. The method allows for…

The Sloan Digital Sky Survey II (SDSS-II) Supernova Survey discovered Type Ia supernovae (SNe Ia) in an almost unexplored intermediate redshift range of $0.05 < z < 0.4$ and provided densely sampled multi-color light curves for SN…

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ…

We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. The main goal of this work is to explore the potential of adaptive learning…

In this paper we present the detailed formalism at the core of the Spectral Line Identification and Modelling (SLIM) within the MAdrid Data CUBe Analysis (MADCUBA) package and their main data handling functionalities. These tools have been…

An analysis leveraging 170 optical spectra of 35 stripped-envelope (SE) core-collapse supernovae observed by the Carnegie Supernova Project-I and published in a companion paper is presented. Mean template spectra are constructed for the SNe…

Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of…

Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to…

In recent years, Artificial Intelligence has become a powerful partner for complex tasks such as data analysis, prediction, and problem-solving, yet its lack of transparency raises concerns about its reliability. In sensitive domains such…

Spiking Neural Networks (SNNs) have emerged as an attractive alternative to traditional deep learning frameworks, since they provide higher computational efficiency in event driven neuromorphic hardware. However, the state-of-the-art (SOTA)…

To improve the estimate of SN rates for all types as a function of redshift has been proposed and accepted a three years SN search with the VST telescope. To help planning an optimal strategy for the search, we have developed a simulation…