165 research outputs found
Constraining the Origins of Binary Black Holes using Multiple Formation Pathways
No full textDataset for Zevin et al. 2021. The codebase AMAZE used in this work can be found on Github.
"models.tar.gz" contains and hdf5 file with all the astrophysical models, organized in the hierarchical structure used in the code AMAZE (*channel*/*param1*/*param2*/...). This is used in the main analysis for inference on branching fractions and physical prescriptions.
The .hdf5 file contains systems from each of the 5 formation channels considered: Common Envelope (CE), Chemically Homogeneous Evolution (CHE), Globular Clusters (GC), Nuclear Star Clusters (NSC), and Stable Mass Transfer (SMT). Each formation channel has 5 variations on the natal spin of black holes: natal spins of 0.0 (chi00), 0.1 (chi01), 0.2 (chi02), and 0.5 (chi05). For the common envelope channel, there are also 5 variations of the common envelope efficiency: 0.2 (alpha02), 0.5 (alpha05), 1.0 (alpha10), 2.0 (alpha20), and 5.0 (alpha50).
After downloading the file, one can use the following code to investigate, for example, the CE channel with natal spins of 0.2 and common envelope efficiency of 1.0, as well as the GC channel with natal spins of 0.0.
First, unzip the tarball using the command line:
tar -xzvf models.tar.gz
Next, in python read in the models as a Pandas dataframe
import pandas as pd
CE_chi02_alpha10 = pd.read_hdf('models_reduced.hdf5', key='CE/chi02/alpha10')
GC_chi00 = pd.read_hdf('models_reduced.hdf5', key='GC/chi00')
These dataframes hold information to the systems in each population model. The keys in the dataframe are:
'mchirp', 'q', 'chieff', 'z' : the chirp mass, mass ratio (secondary mass divided by primary mass), effective spin, and redshift of merger. These are the 4 parameters used in the analysis of Zevin et al. 2021.
'm1', 'm2', 's1x', 's1y', 's1z', 's2x', 's2y', 's2z' : the component masses and component dimensionless spin vectors. These construct the parameters above, though are not explicitly used in the analysis of Zevin et al. 2021.
'weight' : the relative weight of each system in the population model. This includes the astrophysical weight (i.e., system 1's probability of being formed in a specific channel compared to system 2) as well as the cosmological weight of this system being detected by GW detectors (accounting for the volume of the universe at a particular merger redshift and time dilation). These weights do not hold meaningful units and are not comparable across different population models; they are used to construct a weighted distribution for each channel to provide the properties of systems we would see for that channel if we had infinitely-sensitive GW detectors.
'pdet_midhighlatelow_network', 'snropt_midhighlatelow_network', 'pdet_midhighlatelow', 'snropt_midhighlatelow' : the detection probabilities and optimal SNRs of each system. The values with the '_network' tag assume a 3-detector network consisting of LIGO-Hanford, LIGO-Livingston, and Virgo operating at midhighlatelow sensitivity with a SNR detection threshold of 10, whereas the values without this tag assume a single LIGO detector operating at midhighlatelow sensitivity with a SNR detection threshold of 8. These are used, for example, to construct detection-weighted distributions for each channel.
"gw_events.tar.gz" contains the data for all binary black hole events from GWTC1 and GWTC2, processed to have the parameters used in this project. These use the publicly-available samples from the previous links, using the 'Combined' and 'PublicationSamples' posterior samples for GWTC-1 and GWTC-2, respectively. The Jupyter notebook that processes the various LVK data releases to get the GW event posteriors in the proper format can be found on the public git repository. In addition to the event-level parameter posteriors, prior weights for each posterior sample are also included (labeled `p_theta` in the files), since these are needed to get draws from the likelihood that is needed in hierarchical inference. Note that this notebook has been updated to also process the data from GWTC-2.1 and GWTC-3, though these catalogs were not used in Zevin et al. 2021.
"beta_prior.tar.gz" contains prior samples for the branching fractions in the 2-channel and 5-channel case. This was used to create the prior distribution in Figure 5 of Zevin et al. 2021.
"trails.tar.gz" contains all the inference output used in the study. Both underlying samples (key='model_selection/samples') and detectable samples (key='model_selection/detectable_samples') are stored as pandas dataframes in each hdf5 file. The trailing number in each hdf5 file is the trial number, each with a different random seed. These output files were used to create Figure 2-8 of Zevin et al. 2021.
IF USING THE ASTROPHYSICAL MODELS IN `models.tar.gz`, please cite the relevant work:
CE/SMT models: Bavera et al. 2021
CHE models: du Buisson et al. 2020
GC models: Rodriguez et al. 2019
NSC models: Antonini et al. 2019
In addition, one should cite Zevin et al. 2021 (this work) with any use of this dataset.This version addresses a bug that was found in the code that handled population model processing, which led to incorrect calculations of chirp masses. This was a systematic issue that affected all models by causing a slight decrease in the chirp mass of each system due to a typo in the denominator of the chirp mass formula. All analyses were rerun following the identification of this typo, and no results significantly changed. This version of the data release has the population models with the corrected chirp masses to limit confusion for future studies
Constraining the Origins of Binary Black Holes using Multiple Formation Pathways
No full textDataset for Zevin et al. 2020. The codebase AMAZE used in this work can be found on Github.
"models.tar.gz" contains and hdf5 file with all the astrophysical models, organized in the hierarchical structure used in the code AMAZE (*channel*/*param1*/*param2*/...).
"gw_events.tar.gz" contains the data for all binary black hole events from GWTC1 and GWTC2, processed to have the parameters used in this project. These use the publically-available samples from the previous links, using the 'Combined' and 'PublicationSamples' posterior samples for GWTC1 and GWTC2, respectively.
"beta_prior.tar.gz" contains prior samples for the branching fractions in the 2-channel and 5-channel case.
"trails.tar.gz" contains all the inference output used in the study. Both underlying samples (key='model_selection/samples') and detectable samples (key='detectable_samples') are stored as pandas dataframes in each hdf5 file. The trailing number in each hdf5 file is the trial number, each with a different random seed
What You Don't Know Can Hurt You: Use and Abuse of Astrophysical Models in Gravitational-wave Population Analyses
No full textOne of the goals of gravitational-wave astrophysics is to infer the number
and properties of the formation channels of binary black holes (BBHs); to do
so, one must be able to connect various models with the data. We explore
benefits and potential issues with analyses using models informed by population
synthesis. We consider 5 possible formation channels of BBHs, as in Zevin et
al. (2021b). First, we confirm with the GWTC-3 catalog what Zevin et al.
(2021b) found in the GWTC-2 catalog, i.e. that the data are not consistent with
the totality of observed BBHs forming in any single channel. Next, using
simulated detections, we show that the uncertainties in the estimation of the
branching ratios can shrink by up to a factor of as the catalog size
increases from to , within the expected number of BBH detections in
LIGO-Virgo-KAGRA's fourth observing run. Finally, we show that this type of
analysis is prone to significant biases. By simulating universes where all
sources originate from a single channel, we show that the influence of the
Bayesian prior can make it challenging to conclude that one channel produces
all signals. Furthermore, by simulating universes where all 5 channels
contribute but only a subset of channels are used in the analysis, we show that
biases in the branching ratios can be as large as with
detections. This suggests that caution should be used when interpreting the
results of analyses based on strongly modeled astrophysical sub-populations.Comment: 24 pages, 14 figures, comments are welcom
Exploring the lower mass gap and unequal mass regime in compact binary evolution
No full textOn 2019 August 14, the LIGO and Virgo detectors observed GW190814, a gravitational-wave signal originating from the merger of a black hole (BH) with a compact object. GW190814's compact-binary source is atypical both in its highly asymmetric masses and in its lower-mass component lying between the heaviest known neutron star (NS) and lightest known BH in a compact-object binary. If formed through isolated binary evolution, the mass of the secondary is indicative of its mass at birth. We examine the formation of such systems through isolated binary evolution across a suite of assumptions encapsulating many physical uncertainties in massive-star binary evolution. We update how mass loss is implemented for the neutronization process during the collapse of the proto-compact object to eliminate artificial gaps in the mass spectrum at the transition between NSs and BHs. We find it challenging for population modeling to match the empirical rate of GW190814-like systems while simultaneously being consistent with the rates of other compact binary populations inferred from gravitational-wave observations. Nonetheless, the formation of GW190814-like systems at any measurable rate requires a supernova engine model that acts on longer timescales such that the proto-compact object can undergo substantial accretion immediately prior to explosion, hinting that if GW190814 is the result of massive-star binary evolution, the mass gap between NSs and BHs may be narrower or nonexistent
sGRB Progenitor Constraints
No full textThis dataset contains all the input data, submission files, and output data used in Zevin et al. 2020 ("Forward Modeling of Double Neutron Stars: Insights from Highly-Offset Short Gamma-Ray Bursts"). The accompanying code for all the analyses and plotting is available on Github.
"prospector_results.zip" contains the output from Prospector for the two host galaxies considered in this project, which give us the stellar mass and population age for the hosts.
"galaxy_models.zip" contains the pickled time-dependent galaxy models generated using Galpy.
"galaxy_model_interpolations.zip" contains interpolations of these models, used to speed up the kinematic evolution of the tracer particles.
"output_files.zip" contains the output of the kinematic evolution, with information such as the projected offset and birth time of double neutron star tracers.
"population_model.zip" contains the population of double neutron star tracers generated with COSMIC that are used for convolving the kinematic information with the population properties.
"submission_files.zip" contains all the submission scripts and printed output files for all production runs
The Learning Trajectory Of Musical Memory: From Schematic Processing Of Novel Melodies To Robust Musical Memory Representations
No full textThis dissertation utilizes a multi-method approach to investigate the processes underlying musical learning and memory. Particular emphasis is placed on schematic processing, musical structure, temporal aspects of learning, statistics-based predictive models, efficiency, and the role of musical expertise. We employed a set of behavioral change detection studies with musician and nonmusician participants to test what is encoded into gist memory upon hearing unfamiliar melodies varying in musical structure. These studies demonstrate that listeners abstract a schematic representation of the melody that includes tonally and metrically salient tones. In well-structured music, change detection performance improves when a musical event does not conform to the listener's schematic expectations. Musical expertise is also shown to benefit change detection, especially when the melodies conform to the conventions of Western tonal music. In a study examining learning over a period of increasing musical exposure, we used an information theoretic approach to capture how the statistical properties of music influence listeners' musical memory. This work highlights how patterns and predictability can facilitate musical learning over time. In further investigation of what underlies this learning process, a series of neural network studies revealed that a compressed representation arose in the internal structure of a computational network as tonal and stylistic information were learned over time. Population sparsity of the SRN's hidden layer strongly predicted the sophistication of the network's musical output as rated by human listeners. Electroencephalography (EEG) methods were utilized to investigate the neural correlates of musical learning and memory, and to further explore the notion of increasing efficiency over the time-course of learning. These experiments suggest that the listener's implicit internal model of musical expectation is gradually developed and made increasingly accurate with repeated exposure to initially unfamiliar music. Both the computational and EEG experiments illustrate how efficiency accompanies successful learning over time. These findings, as well as those from the change detection and information theory studies, provide evidence that schemata are formed as the probabilities of forthcoming music are gradually learned with increasing experience. Schematic expectations dynamically guide perception and influence memory, and generally allow for more efficient musical processing
Data Release for "Avoiding a Cluster Catastrophe: Retention Efficiency and the Binary Black Hole Mass Spectrum"
No full textData release associated with "Avoiding a Cluster Catastrophe: Retention Efficiency and the Binary Black Hole Mass Spectrum"(arxiv: 2205.08549).
This contains all data used in the paper, as well as a Jupyter notebook that reproduces all figures in the paper.
The three tarballs (`Tdel_10_100.tar.gz`, `Tdel_10_1000.tar.gz`, and `Tdel_100_14000.tar.gz`) contain multiple realizations for each time delay model explored. These just need to be unzipped and are combined in the supplied notebook. These are read in as Pandas dataframes and contain information about merger trees, such as properties of the components of the merger (masses, spins, redshifts), the merger product (remnant spin, remnant mass, recoil kick velocity), and a number of Booleans that determine whether a given merger is possible when constrained by either host environment escape velocity or black hole budget.
`posterior_predictive.hdf5` contains pre-drawn systems from the Power Law + Peak model of GWTC-3; these are by default read in by the script so that the user does not need to redraw these systems each time.
`hierarchical_mergers_clean.ipynb` is a Jupyter notebook that generates all the figures in the paper using the other data products from this release
Core, Periphery, Exchange Rate Regimes, and Globalization
No full textIn this paper we focus on the different historical regime experiences of the core and the periphery. Before 1914 advanced countries adhered to gold while periphery countries either emulated the advanced countries or floated. Some peripheral countries were especially vulnerable to financial crises and debt default in large part because of their extensive external debt obligations denominated in core country currencies. This left them with the difficult choice of floating but restricting external borrowing or devoting considerable resources to maintaining an extra hard peg. Today while advanced countries can successfully float, emergers who are less financially mature and must borrow abroad in terms of advanced country currencies, are afraid to float for the same reason as their nineteenth century forbearers. To obtain access to foreign capital they may need a hard peg to the core country currencies. Thus the key distinction between core and periphery countries both then and now that we emphasize in this paper is financial maturity, evidenced in the ability to issue international securities denominated in domestic currency. Evidence in Section 2 from Feldstein-Horioka tests 1880-1997 agrees with the 'Folk' wisdom that financial integration was as high before 1914 as it is today. But the evidence suggests that it was not the exchange rate regime followed that mattered but the presence of capital controls. Moreover the financial integration observed for the recent period is largely an advanced country phenomenon Section 3 lays out the financial maturity hypothesis, presents narrative evidence for the pre-1914 period of the different experiences of the core and peripheral countries in adhering to the gold standard, and documents that for the emerging countries, plus ca change. Finally, Section 4 presents empirical evidence for core and peripheral countries 1880-1913 and today based on traditional money demand regressions suggesting a strong link between financial depth and the exchange rate regime.
A preferential growth channel for supermassive black holes in elliptical galaxies at z ≲ 2
No full textThe assembly of stellar and supermassive black hole (SMBH) mass in elliptical galaxies since z ∼ 1 can help to diagnose the origins of locally observed correlations between SMBH mass and stellar mass. We therefore construct three samples of elliptical galaxies, one at z ∼ 0 and two at 0.7 ≲ z ≲ 2.5, and quantify their relative positions in the MBH−M* plane. Using a Bayesian analysis framework, we find evidence for translational offsets in both stellar mass and SMBH mass between the local sample and both higher-redshift samples. The offsets in stellar mass are small, and consistent with measurement bias, but the offsets in SMBH mass are much larger, reaching a factor of 7 between z ∼ 1 and z ∼ 0. The magnitude of the SMBH offset may also depend on redshift, reaching a factor of ∼20 at z ∼ 2. The result is robust against variation in the high- and low-redshift samples and changes in the analysis approach. The magnitude and redshift evolution of the offset are challenging to explain in terms of selection and measurement biases. We conclude that either there is a physical mechanism that preferentially grows SMBHs in elliptical galaxies at z ≲ 2, or that selection and measurement biases are both underestimated, and depend on redshift
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