相关论文: STACCATO: A Novel Solution to Supernova Photometri…

Image classifiers are information-discarding machines, by design. Yet, how these models discard information remains mysterious. We hypothesize that one way for image classifiers to reach high accuracy is to first zoom to the most…

Type Ia supernovae (SNe Ia) are a key probe in modern cosmology, as they can be used to measure luminosity distances at gigaparsec scales. Models of their light-curves are used to project heterogeneous observed data onto a common basis for…

Although recent years have witnessed significant advancements in medical image segmentation, the pervasive issue of domain shift among medical images from diverse centres hinders the effective deployment of pre-trained models. Many…

We present an updated version of the Multicolor Light Curve Shape method to measure distances to type Ia supernovae (SN Ia), incorporating new procedures for K-correction and extinction corrections. We also develop a simple model to…

In the era of AI at the edge, self-driving cars, and climate change, the need for energy-efficient, small, embedded AI is growing. Spiking Neural Networks (SNNs) are a promising approach to address this challenge, with their event-driven…

The Hubble constant ($H_0$) is one of the fundamental parameters in cosmology, but there is a heated debate around the $>$4$\sigma$ tension between the local Cepheid distance ladder and the early Universe measurements. Strongly lensed Type…

Observed data is often contaminated by undiscovered interlopers, leading to biased parameter estimation. Here we present BEAMS (Bayesian Estimation Applied to Multiple Species) which significantly improves on the standard maximum likelihood…

While many studies have shown a correlation between properties of the light curves of Type Ia SN (SNe Ia) and properties of their host galaxies, it remains unclear what is driving these correlations. We introduce a new direct method to…

Gravitationally lensed Type Ia supernovae may be the next frontier in cosmic probes, able to deliver independent constraints on dark energy, spatial curvature, and the Hubble constant. Measurements of time delays between the multiple images…

Weak lensing causes spatially coherent fluctuations in flux of type Ia supernovae (SNe Ia). This lensing magnification allows for weak lensing measurement independent of cosmic shear. It is free of shape measurement errors associated with…

Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events. When a neural network is instead faced with subjectively…

We propose a robust, quantitative method to compare the synthetic light curves of a Type Ia Supernova (SNIa) explosion model with a large set of observed SNeIa, and derive a figure of merit for the explosion model's agreement with…

Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric…

In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multi-band light-curves is a…

We present an empirical method that uses multicolor light curve shapes (MLCS) to estimate the luminosity, distance, and total line-of-sight extinction of Type Ia supernovae (SN Ia). The empirical correlation between the MLCS and the…

We present 2603 spectra of 462 nearby Type Ia supernovae (SN Ia) obtained during 1993-2008 through the Center for Astrophysics Supernova Program. Most of the spectra were obtained with the FAST spectrograph at the FLWO 1.5m telescope and…

Graph neural networks (GNNs) are designed for semi-supervised node classification on graphs where only a subset of nodes have class labels. However, under extreme cases when very few labels are available (e.g., 1 labeled node per class),…

We present a set of new quantitative classification criteria for major subclasses of Type I Supernovae (SNe). We analyze peak spectra of 146 SNe Ia from the Berkeley Supernova Ia Program (BSNIP), 12 SNe Ib, 19 SNe Ic (including 5 SNe Ic-BL)…

Deep Neural Networks are well known for efficiently fitting training data, yet experiencing poor generalization capabilities whenever some kind of bias dominates over the actual task labels, resulting in models learning "shortcuts". In…

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…