Search for gravitational-wave transients associated with magnetar bursts in advanced LIGO and advanced Virgo data from the third observing run

Gravitational waves are expected to be produced from neutron star oscillations associated with magnetar giant f lares and short bursts. We present the results of a search for short-duration (milliseconds to seconds) and longduration (∼100 s) transient gravitational waves from 13 magnetar short bursts observed during Advanced LIGO, Advanced Virgo, and KAGRA’s third observation run. These 13 bursts come from two magnetars, SGR1935 +2154 and SwiftJ1818.0−1607. We also include three other electromagnetic burst events detected by FermiGBM which were identified as likely coming from one or more magnetars, but they have no association with a known magnetar. No magnetar giant flares were detected during the analysis period. We find no evidence of gravitational waves associated with any of these 16 bursts. We place upper limits on the rms of the integrated incident gravitational-wave strain that reach 3.6 × 10−²³ Hz at 100 Hz for the short-duration search and 1.1 ×10−²² Hz at 450 Hz for the long-duration search. For a ringdown signal at 1590 Hz targeted by the short-duration search the limit is set to 2.3 × 10−²² Hz. Using the estimated distance to each magnetar, we derive upper limits upper limits on the emitted gravitational-wave energy of 1.5 × 1044 erg (1.0 × 1044 erg) for SGR 1935+2154 and 9.4 × 10^43 erg (1.3 × 1044 erg) for Swift J1818.0−1607, for the short-duration (long-duration) search. Assuming isotropic emission of electromagnetic radiation of the burst fluences, we constrain the ratio of gravitational-wave energy to electromagnetic energy for bursts from SGR 1935+2154 with the available fluence information. The lowest of these ratios is 4.5 × 103

A joint Fermi-GBM and Swift-BAT analysis of gravitational-wave candidates from the third gravitational-wave observing run

We present Fermi Gamma-ray Burst Monitor (Fermi-GBM) and Swift Burst Alert Telescope (Swift-BAT) searches for gamma-ray/X-ray counterparts to gravitational-wave (GW) candidate events identified during the third observing run of the Advanced LIGO and Advanced Virgo detectors. Using Fermi-GBM onboard triggers and subthreshold gamma-ray burst (GRB) candidates found in the Fermi-GBM ground analyses, the Targeted Search and the Untargeted Search, we investigate whether there are any coincident GRBs associated with the GWs. We also search the Swift-BAT rate data around the GW times to determine whether a GRB counterpart is present. No counterparts are found. Using both the Fermi-GBM Targeted Search and the Swift-BAT search, we calculate flux upper limits and present joint upper limits on the gamma-ray luminosity of each GW. Given these limits, we constrain theoretical models for the emission of gamma rays from binary black hole mergers

Constraints on the cosmic expansion history from GWTC–3

We use 47 gravitational wave sources from the Third LIGO–Virgo–Kamioka Gravitational Wave Detector Gravitational Wave Transient Catalog (GWTC–3) to estimate the Hubble parameter H(z), including its current value, the Hubble constant H0. Each gravitational wave (GW) signal provides the luminosity distance to the source, and we estimate the corresponding redshift using two methods: the redshifted masses and a galaxy catalog. Using the binary black hole (BBH) redshifted masses, we simultaneously infer the source mass distribution and H(z). The source mass distribution displays a peak around 34 M⊙, followed by a drop-off. Assuming this mass scale does not evolve with the redshift results in a H(z) measurement, yielding ${H}_{0}={68}_{-8}^{+12}\,\mathrm{km}\ \,\ {{\rm{s}}}^{-1}\,{\mathrm{Mpc}}^{-1}$ (68% credible interval) when combined with the H0 measurement from GW170817 and its electromagnetic counterpart. This represents an improvement of 17% with respect to the H0 estimate from GWTC–1. The second method associates each GW event with its probable host galaxy in the catalog GLADE+, statistically marginalizing over the redshifts of each event's potential hosts. Assuming a fixed BBH population, we estimate a value of ${H}_{0}={68}_{-6}^{+8}\,\mathrm{km}\ \,\ {{\rm{s}}}^{-1}\,{\mathrm{Mpc}}^{-1}$ with the galaxy catalog method, an improvement of 42% with respect to our GWTC–1 result and 20% with respect to recent H0 studies using GWTC–2 events. However, we show that this result is strongly impacted by assumptions about the BBH source mass distribution; the only event which is not strongly impacted by such assumptions (and is thus informative about H0) is the well-localized event GW190814

Machine learning and data mining methodology to predict nominal and numeric performance body weight values using Large White male turkey datasets

SUMMARY: Large biological datasets with many variables and a small number of biological replicates (“omics” sciences and industry data) are challenging to analyze with traditional inferential statistics. Statistical models can be applied to data containing more observations than variables, and they are strongly suited for this purpose. However, the power to detect actual differences is reduced when the number of comparisons exceeds the number of experimental replicates or observations. Machine learning (ML) allows researchers to evaluate treatments groups or multiple categories of variables with fewer observations. Thus, it has become a tool used to predict phenomena and evaluate relationships within datasets that are less suited for traditional statistics. Data mining (DM) helps researchers to identify the most critical variables in an ML predictive model and can be used akin to “statistical significance” for interpretation. This current effort aimed to develop ML and DM methodologies while applying them to predict Large White male turkey body weight (BW). Data from a previously reported study were used. Bird BW, weekly BW gain (BWG), feed intake (FI), feed conversion ratio (FCR), small intestine pH, cloacal temperature, density, microbiome taxa, litter content of Mn and Zn, were used as variables for the ML analysis. A total of 253 variables were used in ML and DM analysis. BW and FI at 18 wk were classified as low, objective, and high based on a 5% for BW and 3% for FI margin of the Aviagen male turkey objectives for ML analysis. The WEKA 3.8.5 Experimenter tool used various classification and regression algorithms with a 10-fold cross-validation system to predict 18 wk BW based on input data. A single algorithm made the most practical model, from 3 models constructed, with a correlation of 0.73 and a root square error of 0.26 based only on turkey 14 wk BW. In conclusion, these ML and DM tools could be applied to turkey research and production systems by analyzing large datasets to predict growth performance