Related papers: Event Discovery in Time Series
We study the statistics and properties of microlensing events that can be detected by the Transiting Exoplanet Survey Satellite(TESS) based on Monte Carlo simulations. We simulate potential microlensing events from a sample of the TESS…
While microlensing is very rare, occurring on average once per million stars observed, current and near-future surveys are coming online with the capability of providing photometry of almost the entire visible sky to depths up to R ~ 22 mag…
This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. The main novelty in our approach is that instead of modeling…
The MACHO project is a search for dark matter in the form of massive compact halo objects (MACHOs). The project has photometrically monitored tens of millions of stars in the Large Magellanic Cloud (LMC), Small Magellanic Cloud (SMC), and…
The nature and the location of the lenses discovered in the microlensing surveys done so far towards the LMC remain unclear. Motivated by these questions we compute the optical depth and particularly the number of expected events for…
We discuss the results of the MEGA microlensing campaign towards M31. Our analysis is based on an analytical evaluation of the microlensing rate, taking into account the observational efficiency as given by the MEGA collaboration. In…
In the domain of time series analysis, particularly in event detection tasks, current methodologies predominantly rely on segmentation-based approaches, which predict the class label for each individual timesteps and use the changepoints of…
There is an increasing number of large, digital, synoptic sky surveys, in which repeated observations are obtained over large areas of the sky in multiple epochs. Likewise, there is a growth in the number of (often automated or robotic)…
The statistical distribution of the masses of planets about stars between the Sun and the center of the galaxy is constrained to within a factor of three by an intensive search for planets during microlensing events. Projected separations…
Microlensing has a unique advantage for detecting dark objects in the Milky Way, such as free-floating planets, neutron stars, and stellar-mass black holes. Most microlensing surveys focus on the Galactic bulge, where higher stellar density…
We discuss the prospect of deblending microlensing events by observing astrometric shifts of the lensed stars. Since microlensing searches are generally performed in very crowded fields, it is expected that stars will be confusion limited…
Recently developed survival analysis methods improve upon existing approaches by predicting the probability of event occurrence in each of a number pre-specified (discrete) time intervals. By avoiding placing strong parametric assumptions…
Extracted event data from information systems often contain a variety of process executions making the data complex and difficult to comprehend. Unlike current research which only identifies the variability over time, we focus on other…
We estimate the rate of near-field microlensing events expected from all-sky surveys and investigate the properties of these events. Under the assumption that all lenses are composed of stars, our estimation of the event rate ranges from…
Microlensing experiments today have a tantalizing result; they have detected an excess of microlensing events beyond what is expected from known stellar populations. These events could be due to a possible form of Halo dark matter. However,…
Streaming computation plays an important role in large-scale data analysis. The sliding window model is a model of streaming computation which also captures the recency of the data. In this model, data arrives one item at a time, but only…
Multivariate time series data come as a collection of time series describing different aspects of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a challenging problem yet with numerous applications in…
Detecting rare events, those defined to give rise to high impact but have a low probability of occurring, is a challenge in a number of domains including meteorological, environmental, financial and economic. The use of machine learning to…
Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of…
Gravitational microlensing events of high magnification provide exceptional sensitivity to the presence of low-mass planets orbiting the lens star, including planets with masses as low as that of Earth. The essential requirement for the…