相关论文: Event Discovery in Time Series
We analyze the first-year MACHO collaboration observations of microlensing towards the Galactic center using a new direct likelihood technique that is sensitive to the distribution of the events on the sky. We consider the full set of 41…
Thanks to the rise of wearable and connected devices, sensor-generated time series comprise a large and growing fraction of the world's data. Unfortunately, extracting value from this data can be challenging, since sensors report low-level…
OGLE III and MOA II are discovering 600-1000 Galactic Bulge microlens events each year. This stretches the resources available for intensive follow-up monitoring of the lightcurves in search of anomalies caused by planets near the lens…
We investigate the possibilities that pulsars act as the lens in gravitational microlensing events towards the galactic bulge or a spiral arm. Our estimation is based on expectant survey and observations of FAST (Five hundred meter Aperture…
The MACHO collaboration reports on the analysis of our first year LMC data, 9.5 million light curves with an average of 235 observations each. Automated selection procedures give 3 events consistent with microlensing. We evaluate our…
For any stream of time-stamped edges that form a dynamic network, an important choice is the aggregation granularity that an analyst uses to bin the data. Picking such a windowing of the data is often done by hand, or left up to the…
Systems are commonly monitored for health and security through collection and streaming of multivariate time series. Advances in time series forecasting due to adoption of multilayer recurrent neural network architectures make it possible…
Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are…
The current and upcoming generation of Very Large Volume Neutrino Telescopes---collecting unprecedented quantities of neutrino events---can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the…
The MACHO collaboration has been carrying out Difference Image Analysis (DIA) since 1996 with the aim of increasing the sensitivity to the detection of gravitational microlensing. This is a preliminary report on the application of DIA to…
The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. To process this information and to extract all possible knowledge, machine learning…
The microlensing parallax campaign with the $Spitzer$ space telescope aims to measure masses and distances of microlensing events seen towards the Galactic bulge, with a focus on planetary microlensing events. The hope is to measure how the…
It has been proposed that the Galaxy might contain a population of cold clouds in numbers sufficient to account for a substantial fraction of the total mass of the Galaxy. These clouds would have masses of the order of 10^{-3} Solar mass…
Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering…
Many photometric time-domain surveys are driven by specific goals, such as searches for supernovae or transiting exoplanets, which set the cadence with which fields are re-imaged. In the case of the Palomar Transient Factory (PTF), several…
Lunar occultation can be used to measure the proper motions of some of the long time scale microlensing events, $t_{e} \gsim 70$ days, now being detected toward the Galactic bulge. The long events are difficult to explain within the context…
The French collaboration EROS and the American-Australian collaboration MACHO have reported the observation of altogether $\sim$ 10 microlensing events by monitoring during several years the brightness of millions of stars in the Large…
Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…
The goal of this paper is to develop provably efficient importance sampling Monte Carlo methods for the estimation of rare events within the class of linear stochastic partial differential equations (SPDEs). We find that if a spectral gap…
Several techniques for multivariate time series anomaly detection have been proposed recently, but a systematic comparison on a common set of datasets and metrics is lacking. This paper presents a systematic and comprehensive evaluation of…