Related papers: Statefinder Revisited
State-space models are a popular statistical framework for analysing sequential data. Within this framework, particle filters are often used to perform inference on non-linear state-space models. We introduce a new method, StateMixNN, that…
We present methods to (i) graphically identify robust redshifts using emission lines in the (sub)mm regime, (ii) evaluate the capabilities of different (sub)mm practices for measuring spectroscopic redshifts, and (iii) optimise future…
Feature matching determines the orientation accuracy for the High Spatial Resolution (HSR) optical satellite stereos, subsequently impacting several significant applications such as 3D reconstruction and change detection. However, the…
In this article we compare the cross-correlation and breakfinder techniques applied to the measurements of redshifts from low-resolution spectra. We assume spectra obtained from multinarrowband imagery, a technique for multi-object…
Future detection of a supernova neutrino burst by large underground detectors would give important information for the explosion mechanism of collapse-driven supernovae. We studied the statistical analysis for the future detection of a…
The transition redshift (deceleration/acceleration) is discussed by expanding the deceleration parameter to first order around its present value. A detailed study is carried out by considering two different parameterizations: $q=q_0 + q_1z$…
We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are…
We present a simple mechanism which can mimic dark energy with an equation of state w < -1 as deduced from the supernova data. We imagine that the universe is accelerating under the control of a quintessence field, which is moving up a very…
Benefited from the rapid and sustainable development of synthetic aperture radar (SAR) sensors, change detection from SAR images has received increasing attentions over the past few years. Existing unsupervised deep learning-based methods…
When planning a survey for astronomical transients, many factors such as cadence, filter choice, sky coverage, and depth of observations need to be balanced in order to optimize the scientific gain of the survey. Here we present a software…
Finite-size effects on the gravitational wave signal from a neutron star merger typically manifest at high frequencies where detector sensitivity decreases. Proposed sensitivity improvements can give us access both to stronger signals and…
Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it's impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of…
Deep, ground based, optical wide-field supernova searches are capable of detecting a large number of supernovae over a broad redshift range up to z~1.5. While it is practically unfeasible to obtain spectroscopic redshifts of all the…
We present two supernovae (SNe) discovered with the Hubble Space Telescope (HST) in the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS), an HST multi-cycle treasury program. We classify both objects as Type Ia SNe…
In this work, we explore the evolution of the dark energy equation of state {\omega} by using Chevalliear-PolarskiLinder (CPL) parametrization and the binned parametrizations. For binned parametrizations, we adopt three methods to choose…
Measuring the redshift of active galactic nuclei (AGNs) requires the use of time-consuming and expensive spectroscopic analysis. However, obtaining redshift measurements of AGNs is crucial as it can enable AGN population studies, provide…
The possibility to unambiguously determine the equation-of-state of the cosmic dark energy with existing and future supernovae data is investigated. We consider four evolution laws for this equation-of-state corresponding to four…
We introduce SuperNNova, an open source supernova photometric classification framework which leverages recent advances in deep neural networks. Our core algorithm is a recurrent neural network (RNN) that is trained to classify light-curves…
In this paper, a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft is analyzed. The proposed algorithm uses both absolute and…
The paper is devoted to synthesis of recurrent algorithms for detection of stochastic signals given in state space. The structure of the algorithms synthesized is shown to be close to that of the Kalman filter. Analysis of one of the…