Photo-Induced Current Measurements in Chlamydomonas Cell Suspensions

In order to fully understand the principles behind phototaxis in flagellate algae, it is necessary to measure the electric currents these cells create when processing light stimuli. Many different techniques have been developed to do this. One of these techniques, measuring from cell suspensions, has a number of advantages over the others that makes it highly desirable. However, the lab that first developed this method of recording did not describe the setup very well. The result is that in the thirteen years since it was first reported, only one other independent lab has been able to reproduce the results despite many attempts by others to do so. Therefore, the primary purpose of this project was to replicate the setup and reproduce the reported results so that others may utilize it. This was accomplished and so the setup is described in detail in Part II of the paper. With the successful reproduction of this cell suspension method, many experiments, involving new and untested stimuli, have been able to be performed. Previously unused analysis methods, which represent more of a physical rather than a biological approach to the data, have been applied. Though many of the results are in the preliminary stages of analysis, some of the newest and most interesting data is presented at the end of the paper

Binary black hole spectroscopy : A no-hair test of GW190814 and GW190412

Gravitational waves provide a window to probe general relativity (GR) under extreme conditions. The recent observations of GW190412 and GW190814 are unique high-mass-ratio mergers that enable the observation of gravitational-wave harmonics beyond the dominant (ℓ,m)=(2,2) mode. Using these events, we search for physics beyond GR by allowing the source parameters measured from the subdominant harmonics to deviate from that of the dominant mode. All results are consistent with GR. We constrain the chirp mass as measured by the (ℓ,m)=(3,3) mode to be within 0-3+5% of the dominant mode when we allow both the masses and spins of the subdominant modes to deviate. If we allow only the mass parameters to deviate, we constrain the chirp mass of the (3,3) mode to be within ±1% of the expected value from GR. © 2020 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the "https://creativecommons.org/licenses/by/4.0/"Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Open access publication funded by the Max Planck Society

Investigating the noise residuals around the gravitational wave event GW150914

We use the Pearson cross-correlation statistic proposed by Liu and Jackson, and employed by Creswell et al., to look for statistically significant correlations between the LIGO Hanford and Livingston detectors at the time of the binary black hole merger GW150914. We compute this statistic for the calibrated strain data released by LIGO, using both the residuals provided by LIGO and using our own subtraction of a maximum-likelihood waveform that is constructed to model binary black hole mergers in general relativity. To assign a significance to the values obtained, we calculate the cross-correlation of both simulated Gaussian noise and data from the LIGO detectors at times during which no detection of gravitational waves has been claimed. We find that after subtracting the maximum likelihood waveform there are no statistically significant correlations between the residuals of the two detectors at the time of GW150914.Comment: 14 pages, 7 figures. Minor text and figure changes in final v3. Notebooks for generating the results are available at https://github.com/gwastro/gw150914_investigatio

Using gravitational waves to distinguish between neutron stars and black holes in compact binary mergers

In August 2017, the first detection of a binary neutron star merger, GW170817, made it possible to study neutron stars in compact binary systems using gravitational waves. Despite being the loudest gravitational wave event detected to date (in terms of signal-to-noise ratio), it was not possible to unequivocally determine that GW170817 was caused by the merger of two neutron stars instead of two black holes from the gravitational-wave data alone. That distinction was primarily due to the accompanying electromagnetic counterpart. This raises the question: under what circumstances can gravitational-wave data alone, in the absence of an electromagnetic signal, be used to distinguish between different types of mergers? Here, we study whether a neutron star-black hole binary merger can be distinguished from a binary black hole merger using gravitational-wave data alone. We build on earlier results using chiral effective field theory to explore whether the data from LIGO and Virgo, LIGO A+, LIGO Voyager, the Einstein Telescope, or Cosmic Explorer could lead to such a distinction. The results suggest that the present LIGO-Virgo detector network will most likely be unable to distinguish between these systems even with the planned near-term upgrades. However, given an event with favorable parameters, third-generation instruments such as Cosmic Explorer will be capable of making this distinction. This result further strengthens the science case for third-generation detectors.Comment: 16 pages, 7 figures, 13 table

Potential Gravitational-wave and Gamma-ray Multi-messenger Candidate from 2015 October 30

We present a search for binary neutron star (BNS) mergers that produced gravitational waves during the first observing run of the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), and gamma-ray emission seen by either the Swift-Burst Alert Telescope (BAT) or the Fermi-Gamma-ray Burst Monitor (GBM), similar to GW170817 and GRB 170817A. We introduce a new method using a combined ranking statistic to detect sources that do not produce significant gravitational-wave or gamma-ray burst candidates individually. The current version of this search can increase by 70% the detections of joint gravitational-wave and gamma-ray signals. We find one possible candidate observed by LIGO and Fermi-GBM, 1-OGC 151030, at a false alarm rate of 1 in 13 yr. If astrophysical, this candidate would correspond to a merger at Mpc with source-frame chirp mass of . If we assume that the viewing angle must be <30° to be observed by Fermi-GBM, our estimate of the distance would become Mpc. By comparing the rate of BNS mergers to our search-estimated rate of false alarms, we estimate that there is a 1 in 4 chance that this candidate is astrophysical in origin

Genetic-algorithm-optimized neural networks for gravitational wave classification

Gravitational-wave detection strategies are based on a signal analysis technique known as matched filtering. Despite the success of matched filtering, due to its computational cost, there has been recent interest in developing deep convolutional neural networks (CNNs) for signal detection. Designing these networks remains a challenge as most procedures adopt a trial and error strategy to set the hyperparameter values. We propose a new method for hyperparameter optimization based on genetic algorithms (GAs). We compare six different GA variants and explore different choices for the GA-optimized fitness score. We show that the GA can discover high-quality architectures when the initial hyperparameter seed values are far from a good solution as well as refining already good networks. For example, when starting from the architecture proposed by George and Huerta, the network optimized over the 20-dimensional hyperparameter space has 78% fewer trainable parameters while obtaining an 11% increase in accuracy for our test problem. Using genetic algorithm optimization to refine an existing network should be especially useful if the problem context (e.g. statistical properties of the noise, signal model, etc) changes and one needs to rebuild a network. In all of our experiments, we find the GA discovers significantly less complicated networks as compared to the seed network, suggesting it can be used to prune wasteful network structures. While we have restricted our attention to CNN classifiers, our GA hyperparameter optimization strategy can be applied within other machine learning settings.Comment: 25 pages, 8 figures, and 2 tables; Version 2 includes an expanded discussion of our hyperparameter optimization mode