T. Prestegard

The Impact of Star Formation and Gamma-Ray Burst Rates at High Redshift on Cosmic Chemical Evolution and Reionization

Recent observations in the total luminosity density have led to significant progress in establishing the star formation rate (SFR) at high redshift. Concurrently observed gamma-ray burst rates have also been used to extract the SFR at high redshift. The SFR in turn can be used to make a host of predictions concerning the ionization history of the Universe, the chemical abundances, and supernova rates. We compare the predictions made using a hierarchical model of cosmic chemical evolution based on three recently proposed SFRs: two based on extracting the SFR from the observed gamma-ray burst rate at high redshift, and one based on the observed galaxy luminosity function at high redshift. Using the WMAP/Planck data on the optical depth and epoch of reionization, we find that only the SFR inferred from gamma-ray burst data at high redshift suffices to allow a single mode (in the initial mass function) of star formation which extends from z = 0 to redshifts > 10. For the case of the more conservative SFR based on the observed galaxy luminosity function, the reionization history of the Universe requires a bimodal IMF which includes at least a coeval high (or intermediate) mass mode of star formation at high redshift (z> 10). Therefore, we also consider here a more general bimodal case which includes an early-forming high mass mode as a fourth model to test the chemical history of the Universe. We compute the abundances of several trace elements, as well as the expected supernova rates, the stellar mass density and the specific SFR, sSFR, as a function of redshift for each of the four models considered. We conclude that observational constraints on the global metallicity and optical depth at high redshift favor unseen faint but active star forming galaxies as pointed out in many recent studies.

Long gravitational-wave transients and associated detection strategies for a network of terrestrial interferometers

Searches for gravitational waves (GWs) traditionally focus on persistent sources (e.g., pulsars or the stochastic background) or on transients sources (e.g., compact binary inspirals or core-collapse supernovae), which last for time scales of milliseconds to seconds. We explore the possibility of long GW transients with unknown waveforms lasting from many seconds to weeks. We propose a novel analysis technique to bridge the gap between short O(s) “burst” analyses and persistent stochastic analyses. Our technique utilizes frequency-time maps of GW strain cross power between two spatially separated terrestrial GW detectors. The application of our cross power statistic to searches for GW transients is framed as a pattern recognition problem, and we discuss several pattern-recognition techniques. We demonstrate these techniques by recovering simulated GW signals in simulated detector noise. We also recover environmental noise artifacts, thereby demonstrating a novel technique for the identification of such artifacts in GW interferometers. We compare the efficiency of this framework to other techniques such as matched filtering.

Model of the stochastic gravitational-wave background due to core collapse to black holes

Superposition of gravitational waves generated by astrophysical sources is expected to give rise to the stochastic gravitational-wave background. We focus on the background generated by the ring-down of black holes produced in the stellar core collapse events, which is one of several mechanisms for gravitational wave production in the stellar core collapse process. We systematically study the parameter space in this model, including the most recent information about the star formation rate and about the population of black holes as a function of redshift and of metallicity. We find that the upcoming second and third generation gravitational-wave detectors will be able to observe this stochastic background if the black hole ring-down efficiency at producing gravitational waves is sufficiently high, namely ∼10−4 and ∼10−6 of the black hole rest energy, respectively.

Systematic study of the stochastic gravitational-wave background due to stellar core collapse

Stellar core collapse events are expected to produce gravitational waves via several mechanisms, most of which are not yet fully understood due to the current limitations in the numerical simulations of these events. In this paper, we begin with an empirical functional form that fits the gravitational-wave spectra from existing simulations of stellar core collapse and integrate over all collapse events in the Universe to estimate the resulting stochastic gravitational-wave background. We then use a Gaussian functional form to separately fit and model a low-frequency peak in the core-collapse strain spectra, which likely occurs due to prompt convection. We systematically study the parameter space of both models, as well as the combined case, and investigate their detectability by upcoming gravitational-wave detectors, such as Advanced LIGO and the Einstein Telescope. Assuming realistic formation rates for progenitors of core-collapse supernovae, our results indicate that both models are 2--4 orders of magnitude below the expected sensitivity of Advanced LIGO, and 1--2 orders of magnitude below that of the Einstein Telescope.

Identification of noise artifacts in searches for long-duration gravitational-wave transients

We present an algorithm for the identification of transient noise artifacts (glitches) in cross-correlation searches for long gravitational-wave (GW) transients lasting seconds to weeks. The algorithm utilizes the auto-power in each detector as a discriminator between well-behaved stationary noise (possibly including a GW signal) and non-stationary noise transients. We test thealgorithmwithbothMonteCarlonoiseandtime-shifteddatafromtheLIGO S5 science run and find that it removes a significant fraction of glitches while keeping the vast majority (99.6%) of the data. We show that this cleaned data can be used to observe GW signals at a significantly lower amplitude than can otherwise be achieved. Using an accretion disk instability signal model, we estimate that the algorithm is accidentally triggered at a rate of less than 10 −5 % by realistic signals, and less than 3% even for exceptionally loud signals. We conclude that the algorithm is a safe and effective method for cleaning the cross-correlation data used in searches for long GW transients.

A 3D Broadband Seismometer Array Experiment at the Homestake Mine

Seismometer deployments are often confined to near the Earth’s surface for practical reasons, despite the clear advantages of deeper seismometer installations related to lower noise levels and more homogeneous conditions. Here, we describe a 3D broadband seismometer array deployed at the inactive Homestake Mine in South Dakota, which takes advantage of infrastructure originally setup for mining and is now used for a range of scientific experiments. The array consists of 24 stations, of which 15 were underground, with depths ranging from 300 ft (91 m) to 4850 ft (1478 m), and with a 3D aperture of ∼1.5 km in each direction, thus spanning a 3D volume of about 3.4 km^3. We describe unique research opportunities and challenges related to the 3D geometry, including the generally low ambient noise levels, the strong coherency between observed event waveforms across the array, and the technical challenges of running the network. This article summarizes preliminary results obtained using data acquired by the Homestake array, illustrating the range of possible studies supported by the data.