arXiv subjects

S. Soni

Publications and source records attributed to S. Soni.

6 recordsLinked to original sources

arXiv2026

LIGO Detector Characterization in the Second and Third Parts of the Fourth Observing Run

LIGO detector characterization efforts enabled the confident detection of gravitational waves from hundreds of compact binary coalescences during the fourth observing run. Reliable production of high quality detector data and rapid noise mitigation efforts allow the extraction of the most in-depth knowledge of gravitational wave sources and their progenitors. In this paper we describe LIGO detector characterization activities during the second and third parts of O4-O4b and O4c. We summarize changes in detector configuration and performance at the LIGO Hanford and LIGO Livingston Observatories between the end of the first part of O4a and the end of O4c, including upgrades made during the commissioning break preceding O4b and during repairs performed in O4c. We describe instrumental investigations carried out at both sites designed to understand and subsequently mitigate the effect on detector sensitivity of transient glitches, narrowband spectral lines, and vibration-driven noise, among other data quality concerns. We then review the tools and procedures used to validate gravitational wave candidates and the data quality products thus supplied to searches for gravitational waves from compact binary coalescences and unmodeled transients, continuous gravitational waves, and the stochastic gravitational wave background. The efforts of the detector characterization group are essential for maintaining and improving the sensitivity and reliability of the LIGO detectors especially as observing runs lengthen and more events are detected. We conclude with prospects for LIGO detector characterization activities in future observing runs.

astro-ph.IM↗

arXiv2024

LIGO Detector Characterization in the first half of the fourth Observing run

Progress in gravitational-wave astronomy depends upon having sensitive detectors with good data quality. Since the end of the LIGO-Virgo-KAGRA third Observing run in March 2020, detector-characterization efforts have lead to increased sensitivity of the detectors, swifter validation of gravitational-wave candidates and improved tools used for data-quality products. In this article, we discuss these efforts in detail and their impact on our ability to detect and study gravitational-waves. These include the multiple instrumental investigations that led to reduction in transient noise, along with the work to improve software tools used to examine the detectors data-quality. We end with a brief discussion on the role and requirements of detector characterization as the sensitivity of our detectors further improves in the future Observing runs.

astro-ph.IM↗

arXiv2022

Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications

Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time-frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of Advanced LIGO. We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.

gr-qc↗

arXiv2021

Explainable predictions of different machine learning algorithms used to predict Early Stage diabetes

Machine Learning and Artificial Intelligence can be widely used to diagnose chronic diseases so that necessary precautionary treatment can be done in critical time. Diabetes Mellitus which is one of the major diseases can be easily diagnosed by several Machine Learning algorithms. Early stage diagnosis is crucial to prevent dangerous consequences. In this paper we have made a comparative analysis of several machine learning algorithms viz. Random Forest, Decision Tree, Artificial Neural Networks, K Nearest Neighbor, Support Vector Machine, and XGBoost along with feature attribution using SHAP to identify the most important feature in predicting the diabetes on a dataset collected from Sylhet Hospital. As per the experimental results obtained, the Random Forest algorithm has outperformed all the other algorithms with an accuracy of 99 percent on this particular dataset.

cs.LG↗

arXiv2021

LIGO Detector Characterization in the Second and Third Observing Runs

The characterization of the Advanced LIGO detectors in the second and third observing runs has increased the sensitivity of the instruments, allowing for a higher number of detectable gravitational-wave signals, and provided confirmation of all observed gravitational-wave events. In this work, we present the methods used to characterize the LIGO detectors and curate the publicly available datasets, including the LIGO strain data and data quality products. We describe the essential role of these datasets in LIGO-Virgo Collaboration analyses of gravitational-waves from both transient and persistent sources and include details on the provenance of these datasets in order to support analyses of LIGO data by the broader community. Finally, we explain anticipated changes in the role of detector characterization and current efforts to prepare for the high rate of gravitational-wave alerts and events in future observing runs.

astro-ph.IM↗

arXiv2021

Environmental Noise in Advanced LIGO Detectors

The sensitivity of the Advanced LIGO detectors to gravitational waves can be affected by environmental disturbances external to the detectors themselves. Since the transition from the former initial LIGO phase, many improvements have been made to the equipment and techniques used to investigate these environmental effects. These methods have aided in tracking down and mitigating noise sources throughout the first three observing runs of the advanced detector era, keeping the ambient contribution of environmental noise below the background noise levels of the detectors. In this paper we describe the methods used and how they have led to the mitigation of noise sources, the role that environmental monitoring has played in the validation of gravitational wave events, and plans for future observing runs.

astro-ph.IM↗