Bulletin of the American Physical Society
APS April Meeting 2021
Volume 66, Number 5
Saturday–Tuesday, April 17–20, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session X05: Data Analysis in AstrophysicsInvited Live
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Sponsoring Units: FGSA Chair: Daniel Palumbo, Harvard University, Event Horizon Telescope |
Tuesday, April 20, 2021 10:45AM - 11:12AM Live |
X05.00001: The Great Challenges of Gravitational-wave Analysis Invited Speaker: Christopher Berry Gravitational-wave astronomy opens up the possibility to study a new side our Universe. To fulfil the potential of gravitational-wave astronomy requires us to (i) identify signals in our data, (ii) characterise the properties of the source, and (iii) combine multiple observations to infer the parameters that describe the population of sources. Each of these steps presents unique computational challenges. We will highlight some of these in the context of the current ground-based detector network, where the rapidly increasing rate of detection demands that analysis is performed efficiency, and future detectors, such as for the space-based Laser Interferometer Space Antenna, where the huge number of overlapping signals will present new problems to solve. [Preview Abstract] |
Tuesday, April 20, 2021 11:12AM - 11:39AM Live |
X05.00002: Data Analysis for Cosmic Explorer Invited Speaker: Duncan Brown Data-analysis software is an integral enabler of observation, experiment, theory, and computation and a primary modality for realizing the discoveries and innovations of astrophysics. I will discuss how data-analysis software enables gravitational-wave astronomy, the free and open source toolkit PyCBC for gravitational-wave astrophysics, and the challenges that the community will face in realizing data analysis for Cosmic Explorer—the proposed U.S. contribution to the third-generation gravitational-wave detector network. [Preview Abstract] |
Tuesday, April 20, 2021 11:39AM - 12:06PM Live |
X05.00003: Data Analysis of Gravitational Waves and Model Development Invited Speaker: Tejaswi Venumadhav Nerella The last few years have seen gravitational wave astronomy mature from its nascent stage with the first direct detections, to a stage where large catalogues of detections enable us to systematically survey the population of merging compact binary sources in the Universe. Both detection and source-characterization rely on sophisticated signal processing and noise mitigation, since the raw signals are buried under detector noise which is non-stationary and non-Gaussian in nature. Understanding and correcting for this is crucial both to maximize the sensitivity and to properly interpret the results of search pipelines. We have performed the first completely independent searches of the LIGO data, in which we improved the modeling of the detector noise, and consequently the reach of public data from the previous runs. We rediscovered all of the official LVC events, as well as several new binary black hole mergers (effectively doubling the population known from the O1 and O2 runs). The new events have provided glimpses into the the underlying complexity of the binary black hole population, with an implied diversity in spins and masses. I will present an overview of the methods and results. [Preview Abstract] |
Tuesday, April 20, 2021 12:06PM - 12:33PM Live |
X05.00004: Feature Extraction on Synthetic Black Hole Images with Neural Networks Invited Speaker: Joshua Yao-Yu Lin The Event Horizon Telescope (EHT) recently released the first horizon-scale images of the black hole in M87. Combined with other astronomical data, these images constrain the mass and spin of the black hole as well as the accretion rate and magnetic flux trapped on the black hole. An important question for EHT is how well key parameters such as spin and trapped magnetic flux can be extracted from present and future EHT data alone. Here we explore parameter extraction using a neural network trained on high resolution synthetic images drawn from state-of-the-art simulations. We find that the neural network is able to recover spin and flux with high accuracy. We are particularly interested in interpreting the neural network output and understanding which features are used to identify, e.g., black hole spin. Using feature maps, we find that the network keys on low surface brightness features in particular. [Preview Abstract] |
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