Related papers: Semi-supervised Learning for Photometric Supernova…
Accurate and precise photometric redshifts (photo-z's) of Type Ia supernovae (SNe Ia) can enable the use of SNe Ia, measured only with photometry, to probe cosmology. This dramatically increases the science return of supernova surveys…
The use of type Ia supernovae (SNe Ia) as cosmological standard candles is fundamental in modern observational cosmology. In this letter, we derive a simple empirical photometric redshift estimator for SNe Ia using a training set of SNe Ia…
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…
Current cosmological analyses which use Type Ia supernova (SN Ia) observations combine SN samples to expand the redshift range beyond that of a single sample and increase the overall sample size. The inhomogeneous photometric calibration…
We present the first measurement of the rate of Type Ia supernovae at high redshift. The result is derived using a large subset of data from the Supernova Cosmology Project as described in more detail at this meeting by Perlmutter et al.…
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…
The upcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to discover nearly a million Type Ia supernovae (SNeIa), offering an unprecedented opportunity to constrain dark energy. The vast majority of these…
We introduce a method for identifying "twin" Type Ia supernovae, and using them to improve distance measurements. This novel approach to Type Ia supernova standardization is made possible by spectrophotometric time series observations from…
Semi-supervised learning techniques are gaining popularity due to their capability of building models that are effective, even when scarce amounts of labeled data are available. In this paper, we present a framework and specific tasks for…
Hyperspectral imaging is a rich source of data, allowing for multitude of effective applications. However, such imaging remains challenging because of large data dimension and, typically, small pool of available training examples. While…
The use of Type Ia Supernovae (SNe Ia) to measure cosmological parameters has grown significantly over the past two decades. However, there exists a significant diversity in the SN Ia population that is not well understood. Over-luminous SN…
We present the first measurement of the rate of Type Ia supernovae at high redshift. The result is derived using a large subset of data from the Supernova Cosmology Project. Three supernovae were discovered in a surveyed area of 1.7 square…
The introduction of new generation hyperspectral satellite sensors, combined with advancements in deep learning methodologies, has significantly enhanced the ability to discriminate detailed land-cover classes at medium-large scales.…
The coming era of large photometric wide-field surveys will increase the detection rate of supernovae by orders of magnitude. Such numbers will restrict spectroscopic follow-up in the vast majority of cases, and hence new methods based…
Type Ia supernovae have played a crucial role in the discovery of the dark energy, via the measurement of their light curves and the determination of the peak brightness via fitting templates to the observed lightcurve shape. Two…
We present measurements of the rates of high-redshift Type Ia supernovae derived from the Subaru/XMM-Newton Deep Survey (SXDS). We carried out repeat deep imaging observations with Suprime-Cam on the Subaru Telescope, and detected 1040…
Self-training is a simple semi-supervised learning approach: Unlabelled examples that attract high-confidence predictions are labelled with their predictions and added to the training set, with this process being repeated multiple times.…
Cosmological parameter estimation techniques that robustly account for systematic measurement uncertainties will be crucial for the next generation of cosmological surveys. We present a new analysis method, superABC, for obtaining…
We present a novel method to produce empirical generative models of all kinds of astronomical transients from datasets of unlabeled light curves. Our hybrid model, that we call ParSNIP, uses a neural network to model the unknown intrinsic…
Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine…