Related papers: Kernel PCA for type Ia supernovae photometric clas…

Measurements of the equation of state of dark energy from surveys of thousands of Type Ia Supernovae (SNe Ia) will be limited by spectroscopic follow-up and must therefore rely on photometric identification, increasing the chance that the…

Spectroscopic and photometric evidence indicates that Type Ia supernovae (SNe Ia) are the thermonuclear explosions of accreting white dwarfs. However, the progenitor binary systems and hydrodynamical models for SNe Ia are still…

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time…

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 an analysis of the final data release of the Carnegie Supernova Project I, focusing on the absolute calibration of the luminosity-decline-rate relation for Type Ia supernovae (SNeIa) using new intrinsic color relations with…

We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing ADI and LOCI for increasing the contrast achievable next to a bright star. The stellar PSF is…

Type Ia supernovae (SNe Ia) play a crucial role as standardizable candles in measurements of the Hubble constant and dark energy. Increasing evidence points towards multiple possible explosion channels as the origin of normal SNe Ia, with…

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data of low-redshift Type Ia supernovae (SNe Ia) by the Carnegie Supernova Project including 604 previously unpublished spectra of 93 SNe Ia.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

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that…

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…

Machine learning and quantum computing are being progressively explored to shed light on possible computational approaches to deal with hitherto unsolvable problems. Classical methods for machine learning are ubiquitous in pattern…

This work includes all the technical details of the Sequential Principal Curves Analysis (SPCA) in a single document. SPCA is an unsupervised nonlinear and invertible feature extraction technique. The identified curvilinear features can be…

Current and future Type Ia Supernova (SN Ia) surveys will need to adopt new approaches to classifying SNe and obtaining their redshifts without spectra if they wish to reach their full potential. We present here a novel approach that uses…

Supernova (SN) explosions enrich the intra-cluster medium (ICM) both by creating and dispersing metals. We introduce a method to measure the number of SNe and relative contribution of Type Ia supernovae (SNe Ia) and core-collapse supernovae…

The use of Type Ia supernovae (SNe Ia) as cosmological standard candles is a key to solving the mystery of dark energy. Improving the calibration of SNe Ia increases their power as cosmological standard candles. We find tentative evidence…

We apply Principal Component Analysis (PCA) to ~100,000 stellar spectra obtained by the Sloan Digital Sky Survey (SDSS). In order to avoid strong non-linear variation of spectra with effective temperature, the sample is binned into 0.02 mag…

Principal component analysis (PCA) is a widely used method for dimension reduction. In high dimensional data, the "signal" eigenvalues corresponding to weak principal components (PCs) do not necessarily separate from the bulk of the "noise"…

Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the cellular level. By providing data on gene expression for each individual cell, scRNA-seq generates large datasets with thousands of…

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…

Using data from the Sloan Digital Sky Supernova Survey-II, we measure the rate of Type Ia Supernovae (SNe Ia) as a function of galaxy properties at intermediate redshift. A sample of 342 SNe Ia with 0.05<z<0.25 is constructed. Using…