Jet Geometry and Rate Estimate of Coincident Gamma Ray Burst and Gravitational Wave Observations

Short Gamma-Ray Burst (SGRB) progenitors have long been thought to be coalescing binary systems of two Neutron Stars (NSNS) or a Neutron Star and a Black Hole (NSBH). The August 17$^{\rm th}$, 2017 detection of the GW170817 gravitational-wave signal by Advanced LIGO and Advanced Virgo in coincidence with the electromagnetic observation of the SGRB GRB 170817A confirmed this scenario and provided new physical information on the nature of these astronomical events. We use SGRB observations by the Neil Gehrels Swift Observatory Burst Alert Telescope and GW170817/GRB 170817A observational data to estimate the detection rate of coincident gravitational-wave and electromagnetic observations by a gravitational-wave detector network and constrain the physical parameters of the SGRB jet structure. We estimate the rate of gravitational-wave detections coincident with SGRB electromagnetic detections by the Fermi Gamma-ray Burst Monitor to be between $\sim$ 0.1 and $\sim$ 0.6 yr$^{-1}$ in the third LIGO-Virgo observing run and between $\sim$ 0.3 and $\sim$ 1.8 yr$^{-1}$ for the LIGO-Virgo-KAGRA network at design sensitivity. Assuming a structured model with a uniform ultra-relativistic jet surrounded by a region with power-law decay emission, we find the jet half-opening angle and the power-law decay exponent to be $\theta_c\sim 7\,{}^\circ$ -- $22\,{}^\circ$ and $s\sim 5$ -- $30$ at 1$\sigma$ confidence level, respectively.Comment: 20 pages, 10 figure

Improving the data quality in gravitation-wave detectors by mitigating transient noise artifacts

“The existence of gravitational waves (GWs), small perturbations in spacetime produced by accelerating massive objects was first predicted in 1916 as solutions of Einstein’s Theory of General Relativity (Einstein, 1916). Detecting and analyzing GWs produced by sources allows us to probe astrophysical phenomena. The era of GW astronomy began from the first direct detection of the coalescence of a binary black hole in 2015 by the collaboration of the advanced Laser Interferometer Gravitational-wave Observatory (LIGO) (Aasi et al., 2015) and advanced Virgo (Abbott et al., 2016a). Since 2015, LIGO-Virgo detected about 50 confident transient events of GW signals (Abbott et al., 2019d, 2021b). To detect GW signals, the detectors must be extremely sensitive, causing them to be susceptible to instrumental and environmental noise. Particularly, excess transient noise artifacts, or glitches significantly impair the quality of detector data. Identification of the source of these glitches is a crucial point for the improvement of GW signal detectability and a better estimate of source parameters. However, glitches are the product of short-lived linear and non-linear couplings among the interrelated detector-control systems that include optic alignment systems and mitigation systems of ground motions, generally making it difficult to find their origin. We present a new software called PyChChoo (Mogushi, 2021a) which uses time series recorded in the instrumental control systems and environmental sensors around times when glitches are present in the detector’s output read-out to reveal essential clues about their origin. Using these time series, we subtract glitches using a machine learning algorithm. We find that our method reduces 20-70% of excess power due to the presence of glitches. For low-latency operations, we present another machine-learning based algorithm called NNETFIX (Mogushi et al., 2021) to estimate the data containing a GW signal that is partially removed due to the presence of an overlapping glitch”--Abstract, page iv

NNETFIX: An artificial neural network-based denoising engine for gravitational-wave signals

Instrumental and environmental transient noise bursts in gravitational-wave detectors, or glitches, may impair astrophysical observations by adversely affecting the sky localization and the parameter estimation of gravitational-wave signals. Denoising of detector data is especially relevant during low-latency operations because electromagnetic follow-up of candidate detections requires accurate, rapid sky localization and inference of astrophysical sources. NNETFIX is a machine learning-based algorithm designed to remove glitches detected in coincidence with transient gravitational-wave signals. NNETFIX uses artificial neural networks to estimate the portion of the data lost due to the presence of the glitch, which allows the recalculation of the sky localization of the astrophysical signal. The sky localization of the denoised data may be significantly more accurate than the sky localization obtained from the original data or by removing the portion of the data impacted by the glitch. We test NNETFIX in simulated scenarios of binary black hole coalescence signals and discuss the potential for its use in future low-latency LIGO-Virgo-KAGRA searches. In the majority of cases for signals with a high signal-to-noise ratio, we find that the overlap of the sky maps obtained with the denoised data and the original data is better than the overlap of the sky maps obtained with the original data and the data with the glitch removed.Comment: 26 pages, 10 figures, 10 table

iCOD : an integrated clinical omics database based on the systems-pathology view of disease

<p>Abstract</p> <p>Background</p> <p>Variety of information relating between genome and the pathological findings in disease will yield a wealth of clues to discover new function, the role of genes and pathways, and future medicine. In addition to molecular information such as gene expression and genome copy number, detailed clinical information is essential for such systematic omics analysis.</p> <p>Results</p> <p>In order to provide a basic platform to realize a future medicine based on the integration of molecular and clinico-pathological information of disease, we have developed an integrated clinical omics database (iCOD) in which comprehensive disease information of the patients is collected, including not only molecular omics data such as CGH (Comparative Genomic Hybridization) and gene expression profiles but also comprehensive clinical information such as clinical manifestations, medical images (CT, X-ray, ultrasounds, etc), laboratory tests, drug histories, pathological findings and even life-style/environmental information. The iCOD is developed to combine the molecular and clinico-pathological information of the patients to provide the holistic understanding of the disease. Furthermore, we developed several kinds of integrated view maps of disease in the iCOD, which summarize the comprehensive patient data to provide the information for the interrelation between the molecular omics data and clinico-pathological findings as well as estimation for the disease pathways, such as three layer-linked disease map, disease pathway map, and pathome-genome map.</p> <p>Conclusions</p> <p>With these utilities, our iCOD aims to contribute to provide the omics basis of the disease as well as to promote the pathway-directed disease view. The iCOD database is available online, containing 140 patient cases of hepatocellular carcinoma, with raw data of each case as supplemental data set to download. The iCOD and supplemental data can be accessed at</p> <p><url>http://omics.tmd.ac.jp/icod_pub_eng</url></p

Upregulation of Protein Tyrosine Phosphatase Type IVA Member 3 (PTP4A3/PRL-3) is Associated with Tumor Differentiation and a Poor Prognosis in Human Hepatocellular Carcinoma

BACKGROUND: Protein tyrosine phosphatase type IVA member 3 (PTP4A3/PRL-3), a metastasis-associated phosphatase, plays multiple roles in cancer metastasis. We investigated PTP4A3/PRL-3 expression and its correlation with the clinicopathological features and prognosis in hepatocellular carcinoma (HCC). METHODS: Gene expression profiles of PTP4A3/PRL-3 were obtained in poorly differentiated HCC tissues. The results were validated independently by TaqMan gene expression assays and immunohistochemical analysis. RESULTS: According to the microarray profiles, PTP4A3/PRL-3 was upregulated in patients with poorly differentiated disease compared to patients with well-differentiated disease with hepatic backgrounds associated with hepatitis B or C. Validation analysis showed that the PTP4A3/PRL-3 mRNA and protein levels were significantly associated with poor differentiation (P < 0.0001), high serum α-fetoprotein (P < 0.01), high serum protein induced by vitamin K absence/antagonist-II (PIVKA-II), and hepatic vascular invasion (P < 0.05). The expression of PTP4A3/PRL-3 protein was also correlated with advanced cancer stages (P < 0.01); this resulted in a significantly poorer prognosis in both overall (P = 0.0024) and recurrence-free survival (P = 0.0227). According Cox regression univariate analysis, the positive expression of PTP4A3/PRL-3 was a poor risk prognostic factor (OS, P = 0.0031; recurrence-free survival, P = 0.0245). Cox regression multivariate analysis indicated that high PTP4A3/PRL-3 expression was an independent, unfavorable prognostic factor for overall survival (hazard ratio 0.542; P = 0.048). CONCLUSIONS: PTP4A3/PRL-3 might be closely associated with HCC progression, invasion, and metastasis. Its high expression had a negative impact on the prognosis of HCC patients. This strongly suggests that PTP4A3/PRL-3 should be considered as a prognostic factor. Further analysis should be pursued to evaluate it as a novel prognostic target. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1245/s10434-012-2395-2) contains supplementary material, which is available to authorized users

First narrow-band search for continuous gravitational waves from known pulsars in advanced detector data

Spinning neutron stars asymmetric with respect to their rotation axis are potential sources of continuous gravitational waves for ground-based interferometric detectors. In the case of known pulsars a fully coherent search, based on matched filtering, which uses the position and rotational parameters obtained from electromagnetic observations, can be carried out. Matched filtering maximizes the signalto- noise (SNR) ratio, but a large sensitivity loss is expected in case of even a very small mismatch between the assumed and the true signal parameters. For this reason, narrow-band analysis methods have been developed, allowing a fully coherent search for gravitational waves from known pulsars over a fraction of a hertz and several spin-down values. In this paper we describe a narrow-band search of 11 pulsars using data from Advanced LIGO’s first observing run. Although we have found several initial outliers, further studies show no significant evidence for the presence of a gravitational wave signal. Finally, we have placed upper limits on the signal strain amplitude lower than the spin-down limit for 5 of the 11 targets over the bands searched; in the case of J1813-1749 the spin-down limit has been beaten for the first time. For an additional 3 targets, the median upper limit across the search bands is below the spin-down limit. This is the most sensitive narrow-band search for continuous gravitational waves carried out so far

Identification and mitigation of narrow spectral artifacts that degrade searches for persistent gravitational waves in the first two observing runs of Advanced LIGO

Searches are under way in Advanced LIGO and Virgo data for persistent gravitational waves from continuous sources, e.g. rapidly rotating galactic neutron stars, and stochastic sources, e.g. relic gravitational waves from the Big Bang or superposition of distant astrophysical events such as mergers of black holes or neutron stars. These searches can be degraded by the presence of narrow spectral artifacts (lines) due to instrumental or environmental disturbances. We describe a variety of methods used for finding, identifying and mitigating these artifacts, illustrated with particular examples. Results are provided in the form of lists of line artifacts that can safely be treated as non-astrophysical. Such lists are used to improve the efficiencies and sensitivities of continuous and stochastic gravitational wave searches by allowing vetoes of false outliers and permitting data cleaning