Optimization of electromagnetic follow up observations and localization of gravitational wave signals from compact binary coalescences

Many gravitational wave sources will produce electromagnetic signals as they emit gravitational waves. An important example is binary neutron star mergers. The joint observations and discoveries of the electromagnetic signatures of these gravitational wave sources can produce substantial scientific benefits in physics, astrophysics and cosmology. To maximize the scientific outcomes of such gravitational events as much as possible, the detections of their electromagnetic signatures are necessary. The first detection of the inspiral signals from binary neutron stars by LIGO and VIRGO, and the observations of the associated electromagnetic counterparts throughout the electromagnetic spectrum have served an excellent example. These detections and discoveries have also ushered in a new era of both gravitational wave astronomy and multi-messenger astronomy. However, using gravitational wave interferometric detectors, the sky location estimates of the gravitational wave signals from binary neutron star can span a few hundreds square degrees, unless there are three or more detectors observing the event simultaneously. The large sky localization error poses a challenge for astronomers scanning the localization error to look for the electromagnetic signals of these gravitational wave events. The electromagnetic counterparts may also not be readily detectable depending on the distance and orientation of the sources, which presents further difficulties in detecting their signals. To alleviate the situation, we develop an algorithm to maximize the detection probability of the electromagnetic counterparts of gravitational wave events. The algorithm we develop is able to generate an observing strategy that optimizes the probability of successful electromagnetic follow-up observations given limited observational resources. This is achieved by using a greedy algorithm for tiling the sky location error and Lagrange multiplier for assigning observation times to observation fields. The analysis with the algorithm also allows an estimate of the detection probability. In Chapter 3, we present a proof-of-concept demonstration of this algorithm to four telescopes Subaru-HyperSuprimeCam, CTIO-Dark Energy Camera, Palomar Transient Factory and Pan-Starrs, for three different simulated binary neutron star events, assuming kilonova to be the target electromagnetic counterpart. By applying the algorithm to telescopes with arbitrary field of view and sensitivity within a range, we provide an insight into the potential of future telescopes and other telescopes not directly included in our analysis. Moreover, the algorithm is applied to the design of a space based mission, the Einstein Probe, to find the optimal combination of the size of field of view and the sensitivity. The localization of gravitational wave sources, which is determined both by the gravitational wave signals and the detectors, is an important factor to the success of electromagnetic follow-up observations. We investigate the localization of binary neutron star mergers detected with the Einstein Telescope and Cosmic Explorer. Compared to the existing detectors, the improvement in the sensitivity of the Einstein Telescope and Cosmic Explorer in the low frequency band has many important implications. One of them is the considerable increase in the length of the in-band of the signals from binary neutron stars, which is useful in localizing the sources. In Chapter 4, using a Fisher matrix approach, we estimate the sky localization error of binary neutron stars as a population and distributed at various distances. As the extended in-band duration of signals also increases the possibility of identifying and releasing the presence of a signal prior to merger, known as early warning, we investigate the prospect for early warning of binary neutron star merger events with these detectors. While the Einstein Telescope and Cosmic Explorer hold promising future for gravitational wave astronomy, they are not likely to be operative until the 2030s. In the literature, detectors designed with more advanced technologies than LIGO and VIRGO are proposed to fill the gap in time. We estimate the localization of binary black holes with two such detectors in Australia and China and seconds generation detectors such as LIGO, LIGO India, VIRGO and KAGRA. In chapter 5, we study electromagnetic observations of binary neutron star mergers with the Large Synoptic Survey Telescope. The Large Synoptic Survey Telescope is a telescope designed with large size of field of view and excellent sensitivity in its observing bands. Such a telescope provides a promising prospect for multimessenger astronomy with gravitational waves. With its sensitivity and field of view, the telescope is expected to enable electromagnetic follow-up observations with shorter exposure time and fewer observation fields than many existing telescopes. We define a simple procedure for electromagnetic follow-up observations triggered by gravitational waves using the telescope. Taking advantages of the Fisher matrix approach in Chapter 4 for the sky location estimates, we quantify the observation time necessary for the telescope to perform electromagnetic follow-up observation of binary neutron star mergers detected with different networks of gravitational wave detectors

Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach

We demonstrate the application of a convolutional neural network to the gravitational wave signals from core collapse supernovae. Using simulated time series of gravitational wave detectors, we show that based on the explosion mechanisms, a convolutional neural network can be used to detect and classify the gravitational wave signals buried in noise. For the waveforms used in the training of the convolutional neural network, our results suggest that a network of advanced LIGO, advanced VIRGO and KAGRA, or a network of LIGO A+, advanced VIRGO and KAGRA is likely to detect a magnetorotational core collapse supernovae within the Large and Small Magellanic Clouds, or a Galactic event if the explosion mechanism is the neutrino-driven mechanism. By testing the convolutional neural network with waveforms not used for training, we show that the true alarm probabilities are 52% and 83% at 60 kpc for waveforms R3E1AC and R4E1FC L. For waveforms s20 and SFHx at 10 kpc, the true alarm probabilities are 70% and 93% respectively. All at false alarm probability equal to 10%

Binary Neutron Star Mergers and Third Generation Detectors: Localization and Early Warning

For third generation gravitational wave detectors, such as the Einstein Telescope, gravitational wave signals from binary neutron stars can last up to a few days before the neutron stars merge. To estimate the measurement uncertainties of key signal parameters, we develop a Fisher matrix approach which accounts for effects on such long duration signals of the time-dependent detector response and the earths rotation. We use this approach to characterize the sky localization uncertainty for gravitational waves from binary neutron stars at 40, 200, 400, 800 and 1600Mpc, for the Einstein Telescope and Cosmic Explorer individually and operating as a network. We find that the Einstein Telescope alone can localize the majority of detectable binary neutron stars at a distance of $\leq200$Mpc to within $100\text{deg}^2$ with 90% confidence. A network consisting of the Einstein Telescope and Cosmic Explorer can enhance the sky localization performance significantly - with the 90% credible region of $\mathcal{O}(1) \text{deg}^2$ for most sources at $\leq200$Mpc and $\leq100\text{deg}^2$ for most sources at $\leq1600$Mpc. We also investigate the prospects for third generation detectors identifying the presence of a signal prior to merger. To do this, we require a signal to have a network signal-to-noise ratio of $\geq12$ and $\geq5.5$ for at least two interferometers, and to have a 90% credible region for the sky localization that is no larger than $100 \text{deg}^2$. We find that the Einstein Telescope can send out such "early-warning" detection alerts 1 - 20 hours before merger for 100% of detectable binary neutron stars at 40Mpc and for $\sim58\%$ of sources at 200Mpc. For sources at a distance of 400Mpc, a network of the Einstein telescope and Cosmic Explorer can produce detection alerts up to $\sim 3$ hours prior to merger for 98% of detectable binary neutron stars

Explaining the GWSkyNet-Multi machine learning classifier predictions for gravitational-wave events

GWSkyNet-Multi is a machine learning model developed for classification of candidate gravitational-wave events detected by the LIGO and Virgo observatories. The model uses limited information released in the low-latency Open Public Alerts to produce prediction scores indicating whether an event is a merger of two black holes, a merger involving a neutron star, or a non-astrophysical glitch. This facilitates time sensitive decisions about whether to perform electromagnetic follow-up of candidate events during LIGO-Virgo-KAGRA (LVK) observing runs. However, it is not well understood how the model is leveraging the limited information available to make its predictions. As a deep learning neural network, the inner workings of the model can be difficult to interpret, impacting our trust in its validity and robustness. We tackle this issue by systematically perturbing the model and its inputs to explain what underlying features and correlations it has learned for distinguishing the sources. We show that the localization area of the 2D sky maps and the computed coherence versus incoherence Bayes factors are used as strong predictors for distinguishing between real events and glitches. The estimated distance to the source is further used to discriminate between binary black hole mergers and mergers involving neutron stars. We leverage these findings to show that events misclassified by GWSkyNet-Multi in LVK's third observing run have distinct sky area, coherence factor, and distance values that influence the predictions and explain these misclassifications. The results help identify the model's limitations and inform potential avenues for further optimization.Comment: 22 pages, 11 figures, submitted to Ap

Contribution of discourse and morphosyntax skills to reading comprehension in Chinese dyslexic and typically developing children

This study aimed at identifying important skills for reading comprehension in Chinese dyslexic children and their typically developing counterparts matched on age (CA controls) or reading level (RL controls). The children were assessed on Chinese reading comprehension, cognitive, and reading-related skills. Results showed that the dyslexic children performed significantly less well than the CA controls but similarly to RL controls in most measures. Results of multiple regression analyses showed that word-level reading-related skills like oral vocabulary and word semantics were found to be strong predictors of reading comprehension among typically developing junior graders and dyslexic readers of senior grades, whereas morphosyntax, a text-level skill, was most predictive for typically developing senior graders. It was concluded that discourse and morphosyntax skills are particularly important for reading comprehension in the non-inflectional and topic-prominent Chinese system

The Asia‐Pacific Biodiversity Observation Network : 10‐year achievements and new strategies to 2030.

The Asia-Pacific Biodiversity Observation Network (APBON) was launched in 2009, in response to the establishment of the Biodiversity Observation Network under the Group on Earth Observations in 2008. APBON's mission is to increase exchange of knowledge and know-how between institutions and researchers concerning biodiversity science research in the Asia-Pacific (AP) region and thereby contribute to evidence-based decision-making and policy-making. Here we summarize APBON activities and achievements in its first 10 years. We review how APBON has developed networks, facilitated communication for sharing knowledge, and built capacity of researchers and stakeholders through workshops and publications as well as discuss the network plan. Key findings by APBON members include descriptions of species new to science, mapping tropical forest cover change, evaluating impacts of hydropower dams and climate change on fish species diversity in the Mekong, and mapping “Ecologically and Biologically Significant Areas” in the oceans. APBON has also contributed to data collection, sharing, analysis, and synthesis for regional and global biodiversity assessment. A highlight was contributing to the “Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services” regional report. New strategic plans target the development of national-level BONs and interdisciplinary research to address the data and knowledge gaps and increase data accessibility for users and for meeting societal demands. Strengthening networks in AP region and capacity building through APBON meetings will continue. By promoting monitoring and scientific research and facilitating the dialogue with scientists and policymakers, APBON will contribute to the implementation of conservation and sustainable use of biodiversity in the entire AP region.publishedVersio

Cognitive skills and literacy performance of Chinese adolescents with and without dyslexia

The present study sought to identify cognitive abilities that might distinguish Hong Kong Chinese adolescents with dyslexia and to assess how these abilities were associated with Chinese word reading, word dictation, and reading comprehension. The cognitive skills of interest were morphological awareness, visual-orthographic knowledge, rapid naming, and verbal working memory. A total of 90 junior secondary school students, 30 dyslexic, 30 chronological age controls, and 30 reading level controls was tested on a range of cognitive and literacy tasks. Dyslexic students were less competent than the control students in all cognitive and literacy measures. The regression analyses also showed that verbal working memory, rapid naming, morphological awareness, and visual-orthographic knowledge were significantly associated with literacy performance. Findings underscore the importance of these cognitive skills for Chinese literacy acquisition. Overall, this study highlights the persistent difficulties of Chinese dyslexic adolescents who seem to have multiple causes for reading and spelling difficulties

Uncoupling Protein-4 (UCP4) Increases ATP Supply by Interacting with Mitochondrial Complex II in Neuroblastoma Cells

Mitochondrial uncoupling protein-4 (UCP4) protects against Complex I deficiency as induced by 1-methyl-4-phenylpyridinium (MPP+), but how UCP4 affects mitochondrial function is unclear. Here we investigated how UCP4 affects mitochondrial bioenergetics in SH-SY5Y cells. Cells stably overexpressing UCP4 exhibited higher oxygen consumption (10.1%, p<0.01), with 20% greater proton leak than vector controls (p<0.01). Increased ATP supply was observed in UCP4-overexpressing cells compared to controls (p<0.05). Although state 4 and state 3 respiration rates of UCP4-overexpressing and control cells were similar, Complex II activity in UCP4-overexpressing cells was 30% higher (p<0.05), associated with protein binding between UCP4 and Complex II, but not that of either Complex I or IV. Mitochondrial ADP consumption by succinate-induced respiration was 26% higher in UCP4-overexpressing cells, with 20% higher ADP:O ratio (p<0.05). ADP/ATP exchange rate was not altered by UCP4 overexpression, as shown by unchanged mitochondrial ADP uptake activity. UCP4 overexpression retained normal mitochondrial morphology in situ, with similar mitochondrial membrane potential compared to controls. Our findings elucidate how UCP4 overexpression increases ATP synthesis by specifically interacting with Complex II. This highlights a unique role of UCP4 as a potential regulatory target to modulate mitochondrial Complex II and ATP output in preserving existing neurons against energy crisis