N. Mukund

Transient Classification in LIGO data using Difference Boosting Neural Network

Detection and classification of transients in data from gravitational wave detectors are crucial for efficient searches for true astrophysical events and identification of noise sources. We present a hybrid method for classification of short duration transients seen in gravitational wave data using both supervised and unsupervised machine learning techniques. To train the classifiers we use the relative wavelet energy and the corresponding entropy obtained by applying one-dimensional wavelet decomposition on the data. The prediction accuracy of the trained classifier on 9 simulated classes of gravitational wave transients and also LIGOs sixth science run hardware injections are reported. Targeted searches for a couple of known classes of non-astrophysical signals in the first observational run of Advanced LIGO data are also presented. The ability to accurately identify transient classes using minimal training samples makes the proposed method a useful tool for LIGO detector characterization as well as searches for short duration gravitational wave signals.

Towards a first design of a Newtonian-noise cancellation system for Advanced LIGO

Newtonian gravitational noise from seismic fields is predicted to be a limiting noise source at low frequency for second generation gravitational-wave detectors. Mitigation of this noise will be achieved by Wiener filtering using arrays of seismometers deployed in the vicinity of all test masses. In this work, we present optimized configurations of seismometer arrays using a variety of simplified models of the seismic field based on seismic observations at LIGO Hanford. The model that best fits the seismic measurements leads to noise reduction limited predominantly by seismometer self-noise. A first simplified design of seismic arrays for Newtonian-noise cancellation at the LIGO sites is presented, which suggests that it will be sufficient to monitor surface displacement inside the buildings.

An Information Retrieval and Recommendation System for Astronomical Observatories

We present a machine-learning-based information retrieval system for astronomical observatories that tries to address user-defined queries related to an instrument. In the modern instrumentation scenario where heterogeneous systems and talents are simultaneously at work, the ability to supply people with the right information helps speed up the tasks for detector operation, maintenance, and upgradation. The proposed method analyzes existing documented efforts at the site to intelligently group related information to a query and to present it online to the user. The user in response can probe the suggested content and explore previously developed solutions or probable ways to address the present situation optimally. We demonstrate natural language-processing-backed knowledge rediscovery by making use of the open source logbook data from the Laser Interferometric Gravitational Observatory (LIGO). We implement and test a web application that incorporates the above idea for LIGO Livingston, LIGO Hanford, and Virgo observatories.

Control strategy to limit duty cycle impact of earthquakes on the LIGO gravitational-wave detectors.

Advanced gravitational-wave detectors such as the Laser Interferometer Gravitational-Wave Observatories (LIGO) require an unprecedented level of isolation from the ground. When in operation, they are expected to observe changes in the space-time continuum of less than one thousandth of the diameter of a proton. Strong teleseismic events like earthquakes disrupt the proper functioning of the detectors, and result in a loss of data until the detectors can be returned to their operating states. An earthquake early-warning system, as well as a prediction model have been developed to help understand the impact of earthquakes on LIGO. This paper describes a control strategy to use this early-warning system to reduce the LIGO downtime by 30%. It also presents a plan to implement this new earthquake configuration in the LIGO automation system.

Limiting the effects of earthquakes on gravitational-wave interferometers

Ground-based gravitational wave interferometers such as the Laser Interferometer Gravitational-wave Observatory (LIGO) are susceptible to ground shaking from high-magnitude teleseismic events, which can interrupt their operation in science mode and significantly reduce their duty cycle. It can take several hours for a detector to stabilize enough to return to its nominal state for scientific observations. The down time can be reduced if advance warning of impending shaking is received and the impact is suppressed in the isolation system with the goal of maintaining stable operation even at the expense of increased instrumental noise. Here, we describe an early warning system for modern gravitational-wave observatories. The system relies on near real-time earthquake alerts provided by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). Preliminary low latency hypocenter and magnitude information is generally available in 5 to 20 min of a significant earthquake depending on its magnitude and location. The alerts are used to estimate arrival times and ground velocities at the gravitational-wave detectors. In general, 90% of the predictions for ground-motion amplitude are within a factor of 5 of measured values. The error in both arrival time and ground-motion prediction introduced by using preliminary, rather than final, hypocenter and magnitude information is minimal. By using a machine learning algorithm, we develop a prediction model that calculates the probability that a given earthquake will prevent a detector from taking data. Our initial results indicate that by using detector control configuration changes, we could prevent interruption of operation from 40 to 100 earthquake events in a 6-month time-period.