scholarly journals TOROS optical follow-up of the advanced LIGO–VIRGO O2 second observational campaign

2020 ◽  
Vol 493 (2) ◽  
pp. 2207-2214
Author(s):  
Rodolfo Artola ◽  
Martin Beroiz ◽  
Juan Cabral ◽  
Richard Camuccio ◽  
Moises Castillo ◽  
...  
Keyword(s):  
Random Forest ◽  
Gravitational Wave ◽  
Astronomical Observatory ◽  
Field Of View ◽  
Random Forest Algorithm ◽  
The South ◽  
Difference Imaging

ABSTRACT We present the methods and results of the optical follow-up, conducted by the Transient Optical Robotic Observatory of the South Collaboration, of gravitational wave events detected during the Advanced LIGO–Virgo second observing run (2016 November–2017 August). Given the limited field of view (∼100 arcmin) of our observational instrumentation, we targeted galaxies within the area of high localization probability that were observable from our sites. We analysed the observations using difference imaging, followed by a random forest algorithm to discriminate between real and spurious transients. Our observations were conducted using telescopes at Estación Astrofísica de Bosque Alegre, Cerro Tololo Inter-American Observatory, the Dr. Cristina V. Torres Memorial Astronomical Observatory, and an observing station in Salta, Argentina.

scholarly journals Observations of the first electromagnetic counterpart to a gravitational wave source by the TOROS collaboration

2017 ◽  
Vol 13 (S338) ◽  
pp. 80-83
Author(s):  
Lucas M. Macri ◽  
Mario C. Díaz ◽  
Diego Garcia Lambas ◽  
Keyword(s):  
Gravitational Wave ◽  
Light Curves ◽  
The South ◽  
Wave Source ◽  
Fast Fading

AbstractWe present the results of prompt optical follow-up of the electromagnetic counterpart of GW170817 by the Transient Optical Robotic Observatory of the South Collaboration (TOROS). We detected highly significant dimming in the light curves of the counterpart over the course of only 80 minutes of observations obtained ~35 hr after the trigger with the T80-South telescope. A second epoch of observations, obtained ~59 hr after the event with the EABA 1.5m telescope, confirms the fast fading nature of the transient. The observed colors of the counterpart suggest that this event was a “blue kilonova” relatively free of lanthanides.

scholarly journals Tiling strategies for optical follow-up of gravitational-wave triggers by telescopes with a wide field of view

2016 ◽  
Vol 592 ◽  
pp. A82 ◽  
Author(s):  
Shaon Ghosh ◽  
Steven Bloemen ◽  
Gijs Nelemans ◽  
Paul J. Groot ◽  
Larry R. Price
Keyword(s):  
Gravitational Wave ◽  
Field Of View ◽  
Wide Field ◽  
Wide Field Of View

scholarly journals Study of nucleosynthesis in neutron star merger with optical-infrared follow-up observations of gravitational wave sources

Impact ◽  
2020 ◽  
Vol 2020 (5) ◽  
pp. 30-32
Author(s):  
Michitoshi Yoshida
Keyword(s):  
Neutron Star ◽  
Gravitational Wave ◽  
Gamma Ray ◽  
Astronomical Observatory ◽  
Gamma Ray Bursts ◽  
Research Approach ◽  
Electro Magnetic ◽  
The Universe

Professor Michitoshi Yoshida, who is based at Subaru Telescope of National Astronomical Observatory of Japan, is a lead scientist with J-GEM (the Japanese Collaboration for Gravitational- Wave Electro-Magnetic Follow-up) and throughout the course of his career in galactic study, has become increasingly interested in the active phenomena of the universe, such as gamma ray bursts (GRB). J-GEM is embarking on a research approach called multi-messenger astronomy, this method is based on the coordination between classical electromagnetic astronomy, new GW astronomy and particle astronomy, and is opening new opportunities for humans to investigate the Universe.

Prediction of Repeated Self-Harm in Six Months: Comparison of Traditional Psychometrics With Random Forest Algorithm

2021 ◽  
pp. 003022282110605
Author(s):  
Shu-Chin Chen ◽  
Hui-Chun Huang ◽  
Shen-Ing Liu ◽  
Sue-Huei Chen
Keyword(s):  
Random Forest ◽  
Suicide Risk ◽  
Predictive Ability ◽  
Random Forest Algorithm ◽  
The Past ◽  
Self Harm ◽  
Two Phases ◽  
Screening Scale ◽  
Potential Tool

Suicidal risk has been a significant mental health problem. However, the predictive ability for repeated self-harm (SH) has not improved over the past decades. This study thus aimed to explore a potential tool with theoretical accommodation and clinical application by employing traditional logistic regression (LR) and newly developed machine learning, random forest algorithm (RF). Starting with 89 items from six commonly used scales (i.e., proximal suicide risk factors) as preliminary predictors, both LR and RF resulted in a better solution with much fewer items in two phases of item selections and analyses, with prediction accuracy 88.6% and 79.8%, respectively. A combination with 12 selected items, named LR-12, well predicted repeated self-harm in 6-month follow-up with satisfactory performance (AUC = 0.84, 95% CI: 0.76–0.92; cut-off point by 1/2 with sensitivity 81.1% and specificity 74.0%). The psychometrically appealing LR-12 could be used as a screening scale for suicide risk assessment.

scholarly journals The updated DESGW processing pipeline for the third LIGO/VIRGO observing run

2020 ◽  
Vol 245 ◽  
pp. 01008
Author(s):  
Kenneth Herner ◽  
James Annis ◽  
Alyssa Garcia ◽  
Marcelle Soares-Santos ◽  
Dillon Brout ◽  
...  
Keyword(s):  
Black Hole ◽  
Neutron Star ◽  
Gravitational Wave ◽  
Data Transfer ◽  
Rapid Identification ◽  
Binary Neutron Star ◽  
Performance Improvements ◽  
Processing Pipeline ◽  
Difference Imaging

The DESGW group seeks to identify electromagnetic counterparts of gravitational wave events seen by the LIGO-VIRGO network, such as those expected from binary neutron star mergers or neutron star-black hole mergers. DESGW was active throughout the first two LIGO observing seasons, following up several binary black hole mergers and the first binary neutron star merger, GW170817. This work describes the modifications to the observing strategy generation and image processing pipeline between the second (ending in August 2017) and third (beginning in April 2019) LIGO observing seasons. The modifications include a more robust observing strategy generator, further parallelization of the image reduction software and difference imaging processing pipeline, data transfer streamlining, and a web page listing identified counterpart candidates that updates in real time. Taken together, the additional parallelization steps enable the identification of potential electromagnetic counterparts within fully calibrated search images in less than one hour, compared to the 3-5 hours it would typically take during the first two seasons. These performance improvements are critical to the entire EM follow-up community, as rapid identification (or rejection) of candidates enables detailed and rapid spectroscopic follow-up by multiple instruments, leading to more information about the environment immediately following such gravitational wave events.

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

scholarly journals Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

2021 ◽  
Vol 20 ◽  
pp. 153303382110246
Author(s):  
Jihwan Park ◽  
Mi Jung Rho ◽  
Hyong Woo Moon ◽  
Jaewon Kim ◽  
Chanjung Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Random Forest ◽  
Radical Prostatectomy ◽  
Prediction Model ◽  
Biochemical Recurrence ◽  
Clinical Decision ◽  
Outcome Variable ◽  
Tertiary Hospitals ◽  
Input Variables

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

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