Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.Comment: 27 pages, 8 figures, 1 tabl

The effects of obstructive jaundice on the brain: An experimental study

The study aims to evaluate the alterations in the brain due to oxidative stress and lipid peroxidation resulting from obstructive jaundice. Forty-one Wistar albino rats were used in this study. Simple laparotomy was performed in the sham group (n = 5). In the remaining 36 rats, the common bile duct (CBD) was found and ligated. They were divided into six groups. Group I, Group II, and Group III were sacrificed at the 3rd, 7th, and 14th day of ligation, respectively. In Group Id, Group IId, and Group IIId ligated bile ducts were decompressed at the 3rd, 7th, and 14th day, respectively. One week after decompression these rats were also sacrificed and samples were taken. After the CBD ligation, serum levels of bilirubin and malondialdehyde were found to be increased progressively in parallel to the ligation time of the CBD. After decompression these values decreased. In electron microscopy evaluation, the damage was found to be irreversible depending on the length of the obstruction period. In Group II, the damage was mostly reversible after the internal drainage period of 7 days. However in Group III, the tissue damage was found to be irreversible despite the decreased values of oxidative stress and bilirubin. Ultrastructural changes in brain tissue including damage in the glial cells and neurons, were found to be irreversible if the CBD ligation period was >7 days and did not regress even after decompression. It is unreliable to trace these changes using blood levels of bilirubin and free radicals. Therefore, timing is extremely critical for medical therapies and drainage