Related papers: SCONE: Supernova Classification with a Convolution…

Type Ia supernovae (SNe Ia) are the largest thermonuclear explosions in the Universe. Their light output can be seen across great distances and has led to the discovery that the expansion rate of the Universe is accelerating. Despite 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…

While the spectroscopic classification scheme for Stripped envelope supernovae (SESNe) is clear, and we know that they originate from massive stars that lost some or all their envelopes of Hydrogen and Helium, the photometric evolution of…

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data of low-redshift Type Ia supernovae (SNe Ia) by the Carnegie Supernova Project including 604 previously unpublished spectra of 93 SNe Ia.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…

We present a new photometric identification technique for SN 1991bg-like type Ia supernovae (SNe Ia), i.e. objects with light-curve characteristics such as later primary maxima and absence of secondary peak in redder filters. This method is…

The second data release of Type Ia supernovae (SNe Ia) observed by the Zwicky Transient Facility has provided a homogeneous sample of 3628 SNe Ia with photometric and spectral information. This unprecedented sample size enables us to better…

We present multi-band optical photometry of 94 spectroscopically-confirmed Type Ia supernovae (SN Ia) in the redshift range 0.0055 to 0.073, obtained between 2006 and 2011. There are a total of 5522 light curve points. We show that our…

In the past few years, convolutional neural networks (CNNs) have achieved impressive results in computer vision tasks, which however mainly focus on photos with natural scene content. Besides, non-sensor derived images such as…

This paper presents spectroscopy of supernovae discovered in the first season of the Sloan Digital Sky Survey-II Supernova Survey. This program searches for and measures multi-band light curves of supernovae in the redshift range z = 0.05 -…

We present a spatio-temporal AI framework that concurrently exploits both the spatial and time-variable features of gravitationally lensed supernovae in optical images to ultimately aid in future discoveries of such exotic transients in…

To measure the supernova (SN) rates at intermediate redshift we performed the Southern inTermediate Redshift ESO Supernova Search (STRESS). Unlike most of the current high redshift SN searches, this survey was specifically designed to…

We present the spectra of 36 Supernovae (SNe) of various types, obtained by the European Supernova Collaboration. Because of the spectral classification and the phase determination at their discovery the SNe did not warrant further study,…

Supernova surveys can be used to study a variety of subjects such as: (i) cosmology through type Ia supernovae (SNe), (ii) star-formation rates through core-collapse SNe, and (iii) supernova properties and their connection to host galaxy…

Image Quality Assessment (IQA) has long been a research hotspot in the field of image processing, especially No-Reference Image Quality Assessment (NR-IQA). Due to the powerful feature extraction ability, existing Convolution Neural Network…

We perform model-independent distance measurements on four Type Ia supernovae (SNe Ia) compilations (Pantheon, Pantheon+, DES-Dovekie, Union3) and compress each dataset into the values of $\log r_p(z)$ at eleven redshift knots, where…

Type Ia supernovae (SNe Ia) have been essential for probing the nature of dark energy; however, most SN analyses rely on the same low-redshift sample, which may lead to shared systematics. In a companion paper (arXiv:2508.10878), we…

We describe catalog-level simulations of Type Ia supernova (SN~Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN), and in low-redshift samples from the Center for Astrophysics (CfA) and the Carnegie Supernova Project…

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 new diagnostic tools for distinguishing supernova remnants (SNRs) from HII regions. Up to now, sources with flux ratio [S II]/H$\rm{\alpha}$ higher than 0.4 have been considered as SNRs. Here, we present the combinations of three…

Type Ia supernovae (SNe Ia) are a prime tool in observational cosmology. A relation between their peak luminosities and the shapes of their light curves allows to infer their intrinsic luminosities and to use them as distance indicators.…