In this paper, we estimate binary compact object merger detection rates for LIGO, including the potentially significant contribution from binaries that are produced in elliptical galaxies near the ...epoch of peak star formation. Specifically, we convolve hundreds of model realizations of elliptical- and spiral-galaxy population syntheses with a model for elliptical- and spiral-galaxy star formation history as a function of redshift. Our results favor local merger rate densities of 4 x 10{sup -3} Mpc{sup -3} Myr{sup -1} for binary black holes (BHs), 3 x 10{sup -2} Mpc{sup -3} Myr{sup -1} for binary neutron stars (NSs), and 10{sup -2} Mpc{sup -3} Myr{sup -1} for BH-NS binaries. We find that mergers in elliptical galaxies are a significant fraction of our total estimate for BH-BH and BH-NS detection rates; NS-NS detection rates are likely dominated by the contribution from spiral galaxies. Limiting attention to elliptical-galaxy plus only those spiral-galaxy models that reproduce current observations of Galactic NS-NS, we find slightly higher rates for NS-NS and largely similar ranges for BH-NS and BH-BH binaries. Assuming a detection signal-to-noise ratio threshold of 8 for a single detector (in practice as part of a network, to reduce its noise), corresponding to radii D {sub bns} of the effective volume inside of which a single LIGO detector could observe the inspiral of two 1.4 M {sub sun} NSs of 14 Mpc and 197 Mpc, for initial and advanced LIGO, we find event rates of any merger type of 2.9 x 10{sup -2}-0.46 and 25-400 yr{sup -1} (at 90% confidence level), respectively. We also find that the probability P {sub detect} of detecting one or more mergers with this single detector can be approximated by (1) P {sub detect} {approx_equal} 0.4 + 0.5 log(T/0.01 yr), assuming D {sub bns} = 197 Mpc and it operates for T yr, for T between 2 days and 0.1 yr, or by (2) P {sub detect} {approx_equal} 0.5 + 1.5 log(D {sub bns}/32 Mpc), for 1 yr of operation and for D {sub bns} between 20 and 70 Mpc.
•Non-cosmic noises known as glitches appear in data of Advanced Laser Interferometer Gravitational wave Observatory (aLIGO).•Gravity Spy is a project that combines crowdsourcing with machine learning ...to help to categorize the glitches.•Present the Gravity Spy dataset, a collection of images of glitches and their associated metadata, for machine learning tasks.•The purpose of glitch classification is to understand glitches origin, which facilitates their removal from the LIGO detector.
The detection of gravitational waves with ground-based laser-interferometric detectors requires sensitivity to changes in distance much smaller than the diameter of atomic nuclei. Though sophisticated machinery and techniques have been developed over the past few decades to isolate such instruments from non-astrophysical noise, the detectors are still susceptible to instrumental and environmental noise transients known as “glitches,” which hinder searches for transient gravitational waves. The Gravity Spy project is an effort to comprehensively classify the glitches that afflict gravitational wave detectors into morphological families by combining the strengths of machine learning algorithms and citizen scientists.
This paper presents the initial Gravity Spy dataset used for citizen scientist and machine learning classification – a static, accessible, documented dataset for testing machine learning supervised classification. Previous versions of this dataset used in 8, 53 did not include all current classes and also for some of the classes, some samples were pruned and added. This set consists of time–frequency images of LIGO glitches and their associated metadata. These glitches are organized by time–frequency morphology into 22 classes for which descriptions and representative images are presented. Results from the application of state-of-the-art supervised classification methods to this dataset are presented in order to provide baselines for future glitch classification work. Standard splitting for training, validation, and testing sets are also presented to facilitate the comparison between different machine learning methods. The baseline methods are selected from both traditional and more recent deep learning approaches. An ensemble framework is developed that demonstrates that combining various classifiers can yield a more accurate model for classification. The ensemble classifier, trained with the standard training set, achieves 98.21% accuracy on the standard test set.
With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative ...means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.
We investigate the characteristics of young (<20 Myr) and bright (L X > 1 X 1036 erg s--1) high-mass X-ray binaries (HMXBs) and find the population to be strongly metallicity dependent. We separate ...the model populations among two distinct formation pathways: (1) systems undergoing active Roche lobe overflow (RLO) and (2) wind accretion systems with donors in the (super)giant stage, which we find to dominate the HMXB population. We find metallicity to primarily affect the number of systems which move through each formation pathway, rather than the observable parameters of systems which move through each individual pathway. We discuss the most important model parameters affecting the HMXB population at both low and high metallicities. Using these results, we show that (1) the population of ultra-luminous X-ray sources can be consistently described by very bright HMXBs which undergo stable RLO with mild super-Eddington accretion and (2) the HMXB population of the bright starburst galaxy NGC 1569 is likely dominated by one extremely metal-poor starburst cluster.
The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud ...transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project Gravity Spy has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run, we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program.
Abstract
Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have ...a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.
The common-envelope (CE) phase is an important stage in the evolution of binary stellar populations. The most common way to compute the change in orbital period during a CE is to relate the binding ...energy of the envelope of the Roche-lobe filling giant to the change in orbital energy. Especially in population-synthesis codes, where the evolution of millions of stars must be computed and detailed evolutionary models are too expensive computationally, simple approximations are made for the envelope binding energy. In this study, we present accurate analytic prescriptions based on detailed stellar-evolution models that provide the envelope binding energy for giants with metallicities between Z = 10--4 and Z = 0.03 and masses between 0.8 M and 100 M , as a function of the metallicity, mass, radius, and evolutionary phase of the star. Our results are also presented in the form of electronic data tables and Fortran routines that use them. We find that the accuracy of our fits is better than 15% for 90% of our model data points in all cases, and better than 10% for 90% of our data points in all cases except the asymptotic giant branches for three of the six metallicities we consider. For very massive stars (M 50 M ), when stars lose more than ~20% of their initial mass due to stellar winds, our fits do not describe the models as accurately. Our results are more widely applicable--covering wider ranges of metallicity and mass--and are of higher accuracy than those of previous studies.
High-redshift galaxies permit the study of the formation and evolution of X-ray binary (XRB) populations on cosmological timescales, probing a wide range of metallicities and star formation rates ...(SFRs). In this paper, we present results from a large-scale population synthesis study that models the XRB populations from the first galaxies of the universe until today. We use as input to our modeling the Millennium II cosmological simulation and the updated semi-analytic galaxy catalog by Guo et al. to self-consistently account for the star formation history and metallicity evolution of the universe. Our modeling, which is constrained by the observed X-ray properties of local galaxies, gives predictions about the global scaling of emission from XRB populations with properties such as SFR and stellar mass, and the evolution of these relations with redshift. Our simulations show that the X-ray luminosity density (X-ray luminosity per unit volume) from XRBs in our universe today is dominated by low-mass XRBs, and it is only at z gap 2.5 that high-mass XRBs become dominant. We also find that there is a delay of ~1.1 Gyr between the peak of X-ray emissivity from low-mass XRBs (at z ~ 2.1) and the peak of SFR density (at z ~ 3.1). The peak of the X-ray luminosity from high-mass XRBs (at z ~ 3.9) happens ~0.8 Gyr before the peak of the SFR density, which is due to the metallicity evolution of the universe.
Ultra-luminous X-ray sources (ULX) are X-ray binaries with L sub(x) > 10 super(39) erg s super(-1). The most spectacular examples of ULX occur in starburst galaxies and are now understood to be ...young, luminous high mass X-ray binaries. The conditions under which ULX form are poorly understood, but recent evidence suggests they may be more common in low metallicity systems. Here we investigate the hypothesis that ULX form preferentially in low metallicity galaxies by searching for ULX in a sample of extremely metal poor galaxies (XMPG) observed with the Chandra X-Ray Observatory. XMPG are defined as galaxies with log(O/H) + 12 < 7.65, or less than 5% solar. These are the most metal-deficient galaxies known, and a logical place to find ULX if they favor metal poor systems. We compare the number of ULX (corrected for background contamination) per unit of star formation (N sub(ULX)(SFR)) in the XMPG sample with (N sub(ULX)(SFR) in a comparison sample of galaxies with higher metallicities taken from the Spitzer Infrared Galaxy Sample. We find that ULX occur preferentially in the metal poor sample with a formal statistical significance of 2.3sigma. We do not see strong evidence for a trend in the formation of ULX in the high metallicity sample: above 12+log(O/H) ~ 8.0 the efficiency of ULX production appears to be flat. The effect we see is strongest in the lowest metallicity bin. We discuss briefly the implications of these results for the formation of black holes in low metallicity gas.
Using Chandra, XMM-Newton, and optical photometric catalogs we study the young X-ray binary (XRB) populations of the Small Magellanic Cloud. We find that the Be/X-ray binaries (Be-XRBs) are observed ...in regions with star formation rate bursts {approx}25-60 Myr ago. The similarity of this age with the age of maximum occurrence of the Be phenomenon ({approx}40 Myr) indicates that the presence of a circumstellar decretion disk plays a significant role in the number of observed XRBs in the 10-100 Myr age range. We also find that regions with strong but more recent star formation (e.g., the Wing) are deficient in Be-XRBs. By correlating the number of observed Be-XRBs with the formation rate of their parent populations, we measure a Be-XRB production rate of {approx}1 system per 3 x 10{sup -3} M{sub sun} yr{sup -1}. Finally, we use the strong localization of the Be-XRB systems in order to set limits on the kicks imparted on the neutron star during the supernova explosion.
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