Future Time Domain Research Articles

The ELM Survey South. II. Two Dozen New Low-mass White Dwarf Binaries

We present the results from our ongoing spectroscopic survey targeting low-mass white dwarf binaries, focusing on the southern sky. We used a Gaia DR2- and eDR3-based selection and identified 28 new binaries, including 19 new extremely low-mass (ELM) white dwarfs, one short period, likely eclipsing, DABZ, and two potential LISA binaries. We present the orbital and atmospheric parameters for each new binary based on our spectroscopic follow up. Four of our new binaries show periodic photometric variability in TESS 2 minutes cadence data, including one new eclipsing double-lined spectroscopic binary. Three others show periodic photometric variability in ZTF, including one new eclipsing binary. We provide estimates for the inclinations and scaled component radii for these ZTF variables, based on light-curve modeling of our high-speed photometric follow-up observations. Our observations have increased the sample of ELM Survey binaries identified in the southern sky to 41, an increase of 64%. Future time domain surveys, such as BlackGEM and the Vera C. Rubin Observatory Legacy Survey of Space and Time, will efficiently identify photometric variables in the southern sky and significantly increase the population of southern sky low-mass white dwarf binaries, leading to a more complete all-sky population of these systems.

On possible proxies of AGN light-curves cadence selection in future time domain surveys

ABSTRACT Motivated by upcoming photometric and spectroscopic surveys [Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), Manuakea Spectroscopic Explorer], we design the statistical proxies to measure the cadence effects on active galactic nuclei (AGNs) variability-observables [time-lags, periodicity, and structure-function (SF)]. We constructed a multiple-regression model to statistically identify the cadence-formal error pattern knowing AGN time-lags and periodicity from different surveys. We defined the simple metric for the SF’s properties, accounting for the ‘observed’ SF’s deviation relative to those obtained from the homogenously sampled light curves. We tested the regression models on different observing strategies: the optical data set of long light curves of eight AGN with peculiarities and the artificial data sets based on several idealized and LSST-like cadences. The SFs metric is assessed on synthetic data sets. The regression models (for both data types) predict similar cadences for time-lags and oscillation detection, whereas for light curves with low variability (${\sim}10{{\ \rm per\ cent}}$), cadences for oscillation detection differ. For higher variability (${\sim}20{{\ \rm per\ cent}}$), predicted cadences are larger than for $F_{var}\sim 10{{\ \rm per\ cent}}$. The predicted cadences are decreasing with redshift. SFs with dense and homogenous cadences are more likely to behave similarly. SFs with oscillatory signals are sensitive to the cadences, possibly impacting LSST-like operation strategy. The proposed proxies can help to select spectroscopic and photometric-surveys cadence strategies, and they will be tested further in larger samples of objects.

GPU-accelerated periodic source identification in large-scale surveys: measuring P and P

ABSTRACT Many inspiraling and merging stellar remnants emit both gravitational and electromagnetic radiation as they orbit or collide. These gravitational wave events together with their associated electromagnetic counterparts provide insight about the nature of the merger, allowing us to further constrain properties of the binary. With the future launch of the Laser Interferometer Space Antenna (LISA), follow-up observations and models are needed of ultracompact binary (UCB) systems. Current and upcoming long baseline time domain surveys will observe many of these UCBs. We present a new fast periodic object search tool capable of searching for generic periodic signals based on the conditional entropy algorithm. This new implementation allows for a grid search over both the period (P) and the time derivative of the period ($\dot{P}$). To demonstrate the usage of this tool, we use a small, hand-picked subset of a UCB population generated from the population synthesis code cosmic , as well as a custom catalogue for varying periods at fixed intrinsic parameters. We simulate light curves as likely to be observed by future time domain surveys by using an existing eclipsing binary light-curve model accounting for the change in orbital period due to gravitational radiation. We find that a search with $\dot{P}$ values is necessary for detecting binaries at orbital periods less than ∼10 min. We also show it is useful in finding and characterizing binaries with longer periods, but at a higher computational cost. Our code is called gce (GPU-accelerated Conditional Entropy). It is available on Github (https://github.com/mikekatz04/gce).

Tačnost predvidljivosti brzine vetra sa visinom korišćenjem rekurentnih neuronskih mreža

Accurate prediction of wind speed in future time domain is critical for wind power integration into the grid. Wind speed is usually measured at lower heights while the hub heights of modern wind turbines are much higher in the range of 80-120m. This study attempts to better understand the predictability of wind speed with height. To achieve this, wind data was collected using Laser Illuminated Detection and Ranging (LiDAR) system at 20m, 40m, 50m, 60m, 80m, 100m, 120m, 140m, 160m, and 180m heights. This hourly averaged data is used for training and testing a Recurrent Neural Network (RNN) for the prediction of wind speed for each of the future 12 hours, using 48 previous values. Detailed analyses of short-term wind speed prediction at different heights and future hours show that wind speed is predicted more accurately at higher heights.For example, the mean absolute percent error decreases from 0.19 to 0.16as the height increase from 20m to 180m, respectively for the 12 th future hour prediction. The performance of the proposed method is compared with Multilayer Perceptron (MLP) method. Results show that RNN performed better than MLP for most of the cases presented here at the future 6th hour.

Decision-Making and Planning Method for Autonomous Vehicles Based on Motivation and Risk Assessment

In order to improve the real-time and computational efficiency of autonomous vehicles’ decision-making process, this paper draws on the decision-making behavior of human drivers with the motivation as the core and proposes a decision-making and planning method based on motivation and risk assessment. On the one hand, it analyzes and determines the motivations that cause the driving state to change for decision-making and planning. On the other hand, on the basis of the lateral trajectory prediction of surrounding vehicles, the longitudinal trajectory propensity prediction of different drivers is added to construct a risk assessment model that can reflect risk of the future time domain. Based on this, the motivation-based decision method is mapped into the risk assessment model, and a cost function is established to decouple the path and speed, so that the geometry and speed can be flexibly adjusted according to environmental risks. The simulation results show that the proposed method can effectively make driving behavior decisions and plan the trajectory in real time according to the current environment, which can improve the computational efficiency of the decision-making process and guarantee the safety at the same time.

Electromagnetic transients and gravitational waves from white dwarf disruptions by stellar black holes in triple systems

ABSTRACT Mergers of binaries comprising compact objects can give rise to explosive transient events, heralding the birth of exotic objects that cannot be formed through single-star evolution. Using a large number of direct N-body simulations, we explore the possibility that a white dwarf (WD) is dynamically driven to tidal disruption by a stellar-mass black hole (BH) as a consequence of the joint effects of gravitational wave (GW) emission and Lidov–Kozai oscillations imposed by the tidal field of an outer tertiary companion orbiting the inner BH–WD binary. We explore the sensitivity of our results to the distributions of natal kick velocities imparted to the BH and WD upon formation, adiabatic mass loss, semimajor axes and eccentricities of the triples, and stellar-mass ratios. We find rates of WD–tidal disruption events (TDEs) in the range 1.2 × 10−3 − 1.4 Gpc−3 yr−1 for z ≤ 0.1, rarer than stellar TDEs in triples by a factor of ∼3–30. The uncertainty in the TDE rates may be greatly reduced in the future using GW observations of Galactic binaries and triples with LISA. WD–TDEs may give rise to high-energy X-ray or gamma-ray transients of duration similar to long gamma-ray bursts but lacking the signatures of a core-collapse supernova, while being accompanied by a supernova-like optical transient that lasts for only days. WD–BH and WD–NS binaries will also emit GWs in the LISA band before the TDE. The discovery and identification of triple-induced WD–TDE events by future time domain surveys and/or GWs could enable the study of the demographics of BHs in nearby galaxies.

A metaheuristic approach to forecast the global carbon dioxide emissions

This research aims to investigate the application of a novel optimisation method called Ray Optimization (RO) for future forecasting of the CO2 emissions. Accordingly, an integrated multi-layer perceptron neural network along with the RO method is implemented. For this purpose, in the first step, two scenarios are developed to predict the socioeconomic indicators (SEIs) in a future time domain. Afterwards, the RO technique is implemented to investigate the world’s fossil fuels and primary energy consumption based on the investigated SEIs. Subsequently, the world CO2 emissions are projected based on the oil, natural gas, coal, and primary energy consumption using the RO method. Furthermore, the related data from 1980 to 2006 are used both for implementing the models (1980–1999) and for testing the models (2000–2006). Global CO2 emissions are then forecast up to the year 2025.

Cataclysmic Variables from SDSS: A Review and A Look Forward to LSST

The past and current projects of the Sloan Digital Sky Survey are reviewed in the context of applicability and results for cataclysmic variables. Ongoing and future time domain surveys that will have impact on the field are also briefly discussed.

The Integration of Artificial Neural Networks and Particle Swarm Optimization to Forecast World Green Energy Consumption

In this study an integrated particle swarm optimization (PSO) and artificial neural network (ANN) is presented for analyzing world fossil fuels, primary energy and green energy consumption. For this purpose, these steps are followed: STEP 1: In the first step, PSO is applied in order to determine world's oil, natural gas, coal and primary energy demand equations based on socioeconomic indicators. world population, gross domestic product, oil trade movement and natural gas trade movement are used as socioeconomic indicators in this study. Two scenarios are defined for forecasting each socioeconomic indicator in a future time domain: Scenario I: For each socioeconomic indicator, the polynomial trend line is fitted to the observed data with the highest correlation coefficient value and projected for a future time domain. Scenario II: for each socioeconomic indicator, a feed-forward back propagation ANN is trained and projected for a future time domain. STEP 2: In the second step, world green energy consumption is projected based on oil, natural gas, coal, and primary energy consumption using PSO. The best results of the first step are used for future forecasting of world green energy consumption. World green energy consumption is forecasted up to the year 2040.

An Analysis of Wind Speed Prediction Using Artificial Neural Networks: A Case Study in Manjil, Iran

In this study, air temperature, relative humidity, and vapor pressure data collected from Manjil station between 1993–2004, were used for wind speed predictions in a future time domain using artificial neural networks. The following combinations of data are considered for this study: (i) month of the year, monthly mean daily air temperature, and relative humidity as inputs, and monthly mean daily wind speed as output; (ii) month of the year, monthly mean daily air temperature, relative humidity, and vapor pressure as inputs, and monthly mean daily wind speed as output. The generalized regression neural networks, multilayer perceptron, and radial basis function neural networks were used in this study. The measured data between 1993 and 2003 is applied for training and the data for 2004 is used for testing. The data for testing were not applied for training the neural networks. Obtained results show that neural networks are well capable of estimating wind speed from simple meteorological data. These results indicate that using vapor pressure along with the month of the year, monthly mean daily air temperature, and relative humidity based on a multilayer perceptron network has better performance than the other cases with the mean absolute percentage error of 7.03% (2004).

Using Bees Algorithm and Artificial Neural Network to Forecast World Carbon Dioxide Emission

In this study, an integrated multi-layer perceptron neural network and Bees Algorithm is presented for analyzing world CO2 emissions. For this purpose, the following steps are done: STEP 1: In the first step, the Bees Algorithm is applied in order to determine the world's fossil fuels and primary energy demand equations based on socio-economic indicators. The world's population, gross domestic product, oil trade movement, and natural gas trade movement are used as socio-economic indicators in this study. The following scenarios are designed for forecasting each socio-economic indicator in a future time domain: Scenario I: For each socio-economic indicator, several polynomial trend lines are fitted to the observed data and the best fitted polynomial (highest correlation coefficient (R2) value) for each socio-economic indicator is used for future forecasting. Scenario II: For each socio-economic indicator, several neural networks are trained and the best trained network for each socio-economic indicator is used for future forecasting. STEP 2: In the second step, world CO2 emission is projected based on the oil, natural gas, coal, and primary energy consumption using Bees Algorithm. The related data from 1980 to 2006 are used, partly for installing the models (1980–1999) and partly for testing the models (2000–2006). World CO2 emission is forecasted up to year 2040.

Canonical conservative state/signal shift realizations of passive discrete time behaviors

A passive linear discrete time invariant s/s (state/signal) system Σ = ( V ; X , W ) consists of a Hilbert (state) space X , a Kreĭn (signal) space W , a maximal nonnegative (generating) subspace V of the Kreĭn space K : = − X [ ∔ ] X [ ∔ ] W . The sets of trajectories ( x ( ⋅ ) ; w ( ⋅ ) ) generated by V on the discrete time intervals I ⊂ Z are defined by ( x ( n + 1 ) ; x ( n ) ; w ( n ) ) ∈ V , n ∈ I . This system is forward conservative, or backward conservative, or conservative if V ⊂ V [ ⊥ ] , V [ ⊥ ] ⊂ V , or V [ ⊥ ] = V , respectively. The set W + Σ of all signal components w ( ⋅ ) of trajectories ( x ( ⋅ ) ; w ( ⋅ ) ) of Σ on I = Z + with x ( 0 ) = 0 and w ( ⋅ ) ∈ ℓ 2 ( Z + ; W ) is called the future time domain behavior of Σ. The Fourier transform W ˆ + Σ of W + Σ is called the future frequency domain behavior of Σ. This set is a maximal nonnegative right-shift invariant subspace in the Kreĭn space K 2 ( D ; W ) that as a topological vector space coincides with the usual Hardy space H 2 ( D ; W ) , but has the indefinite Kreĭn space inner product inherited from W . A subspace of K 2 ( D ; W ) with the above properties is called a passive future frequency domain behavior on W . It has been shown earlier by the present authors that every passive future frequency domain behavior W ˆ + on W may be realized as the future frequency domain behavior of some passive s/s system Σ, and that it is possible to require, in addition, that Σ is (a) controllable and forward conservative, (b) observable and backward conservative, or (c) simple and conservative. These three types of realizations are determined by W ˆ + up to unitary similarity. Canonical functional shift realizations of the types (a) and (b) have been obtained earlier by the present authors, and their connection to the classical de Branges–Rovnyak models have been discussed. Here we present analogous results for a realization of the type (c).

Estimation of Diffuse Fraction of Global Solar Radiation Using Artificial Neural Networks

Measured daily mean air temperature and relative humidity values between 1998 and 2002 for Abha city in Saudi Arabia were used for predicting diffuse fraction of solar radiation in future time domain using artificial neural networks method. The estimations of diffuse solar radiation were made using four combinations of data sets, viz. (i) day of the year and daily maximum air temperature as inputs and diffuse solar radiation as output, (ii) day of the year and daily minimum air temperature as inputs and diffuse solar radiation as output, (iii) day of the year and daily mean air temperature as inputs and diffuse solar radiation as output, and (iv) time day of the year, daily mean air temperature, and relative humidity as inputs and diffuse solar radiation as output. The measured data between 1998 and 2001 was used for training the neural networks while the remaining 250 days' data from 2002 was used as testing data. The testing data was not used in training the neural networks. Obtained results show that neural networks are well capable of estimating diffuse solar radiation from temperature and relative humidity. This can be used for estimating diffuse solar radiation for locations where only temperature and humidity data are available.

Artificial neural network estimation of global solar radiation using air temperature and relative humidity

Measured air temperature and relative humidity values between 1998 and 2002 for Abha city in Saudi Arabia were used for the estimation of global solar radiation (GSR) in future time domain using artificial neural network method. The estimations of GSR were made using three combinations of data sets namely: (i) day of the year and daily maximum air temperature as inputs and GSR as output, (ii) day of the year and daily mean air temperature as inputs and GSR as output and (iii) time day of the year, daily mean air temperature and relative humidity as inputs and GSR as output. The measured data between 1998 and 2001 were used for training the neural networks while the remaining 240 days’ data from 2002 as testing data. The testing data were not used in training the neural networks. Obtained results show that neural networks are well capable of estimating GSR from temperature and relative humidity. This can be used for estimating GSR for locations where only temperature and humidity data are available.

Predicting underwater ocean noise with genetic algorithms

In this Letter we employ recent developments on nonlinear physics and time series prediction to study the physical characteristics of measured underwater ambient noise. Specifically, we examine the predictability of a sample of ocean ambient noise recorded in the Strait of Sicily, Italy. To do that, an approach based on genetic algorithms has been employed. Results indicate that, while showing a complex time variability, the recorded signal is highly predictable due to the probable contribution of shipping noise. More general, the Letter addresses the possible development of future time domain noise mitigation strategies, very relevant to underwater communications, based on methodologies of nonlinear physics.

Stability condition for the explicit algorithms of the time domain analysis of Maxwell's equations

This letter presents the derivation of the stability condition for various types of time domain algorithms used in the solution of linear hyperbolic differential equations that arise in the investigation of transient electromagnetic fields. The stability condition of the algorithm is derived by investigating the properties of operators in suitably defined Hilbert spaces. Compared to the classical von Neumann stability analysis, the functional analysis approach gives more general results that can be easily applied to some recent and possible future time domain schemes. >