相关论文: Artificial Intelligence Assisted Inversion (AIAI) …

Following our previous study of Artificial Intelligence Assisted Inversion (AIAI) of supernova analyses (Chen et al. 2020), we train a set of deep neural networks based on the one-dimensional radiative transfer code TARDIS (Kerzendorf & Sim…

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…

We construct and train an artificial neural network called the back-propagation neural network to describe the evolution of the type Ia supernova spectrum by using the data from the CfA Supernova Program. This network method has many…

We report near infrared (NIR) spectroscopic observations of twelve ``Branch-normal'' Type Ia supernovae (SNe Ia) which cover the wavelength region from 0.8-2.5 microns. Our sample more than doubles the number of SNe Ia with published NIR…

We present a method of extrapolating the spectroscopic behavior of Type Ia supernovae (SNe Ia) in the near-infrared (NIR) wavelength regime up to 2.30 $\mu$m using optical spectroscopy. Such a process is useful for accurately estimating…

We study the spectral diversity of Type Ia supernovae (SNe Ia) at maximum light using high signal-to-noise spectrophotometry of 173 SNe Ia from the Nearby Supernova Factory. We decompose the diversity of these spectra into different…

We present SNIascore, a deep-learning based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R $\sim100$) data. The goal of SNIascore is fully automated classification of SNe Ia with…

Context. Spectroscopic analyses to interpret the spectra of the brightest supernovae from the UV to the near-IR provide a powerful tool with great astrophysical potential for the determination of the physical state of the ejecta, their…

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…

This is the first release of optical spectroscopic data of low-redshift Type Ia supernovae (SNe Ia) by the Carnegie Supernova Project including 604 previously unpublished spectra of 93 SNe Ia. The observations cover a range of phases from…

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…

Ultraviolet (UV) observations of Type Ia supernovae (SNe Ia) probe the outermost layers of the explosion, and UV spectra of SNe Ia are expected to be extremely sensitive to differences in progenitor composition and the details of the…

We derive the rates of Type Ia supernovae (SNIa) over a wide range of redshifts using a complete sample from the IfA Deep Survey. This sample of more than 100 SNIa is the largest set ever collected from a single survey, and therefore…

Type Ia Supernovae (SNe Ia) have become the most precise distance indicators in astrophysics due to their incredible observational homogeneity. Increasing discovery rates, however, have revealed multiple sub-populations with spectroscopic…

The spectral energy distribution (SED) sequence for type Ia supernovae (SN Ia) is modeled by an artificial neural network. The SN Ia luminosity is characterized as a function of phase, wavelength, a color parameter and a decline rate…

We compare models for Type Ia supernova (SN Ia) light curves and spectra with an extensive set of observations. The models come from a recent survey of 44 two-dimensional delayed-detonation models computed by Kasen, Roepke & Woosley (2009),…

We present the current status of our construction of synthetic spectra for type Ia supernovae. These properly take into account the effects of NLTE and an adequate representation of line blocking and blanketing. The models are based on a…

We describe a research program to improve the understanding of Type Ia Supernovae (SNe Ia) by modeling and observing near infrared (NIR) spectra of these events. The NIR between 0.9 microns and 2.5 microns is optimal for examining certain…

In this first paper in a series we present 1298 low-redshift (z\leq0.2) optical spectra of 582 Type Ia supernovae (SNe Ia) observed from 1989 through 2008 as part of the Berkeley SN Ia Program (BSNIP). 584 spectra of 199 SNe Ia have…

Multiple explosion mechanisms have been proposed to explain type Ia supernovae (SNe Ia). Empirical modelling tools have also been developed that allow for fast, customised modelling of individual SNe and direct comparisons between…