Search for: All records

Binary stars undergo a variety of interactions and evolutionary phases, critical for predicting and explaining observations. Binary population synthesis with full simulation of stellar structure and evolution is computationally expensive, requiring a large number of mass-transfer sequences. The recently developed binary population synthesis codePOSYDONincorporates grids ofMESAbinary star simulations that are interpolated to model large-scale populations of massive binaries. The traditional method of computing a high-density rectilinear grid of simulations is not scalable for higher-dimension grids, accounting for a range of metallicities, rotation, and eccentricity. We present a new active learning algorithm,psy-cris, which uses machine learning in the data-gathering process to adaptively and iteratively target simulations to run, resulting in a custom, high-performance training set. We testpsy-crison a toy problem and find the resulting training sets require fewer simulations for accurate classification and regression than either regular or randomly sampled grids. We further applypsy-cristo the target problem of building a dynamic grid ofMESAsimulations, and we demonstrate that, even without fine tuning, a simulation set of only ∼1/4 the size of a rectilinear grid is sufficient to achieve the same classification accuracy. We anticipate further gains when algorithmic parameters are optimized for the targeted application. We find that optimizing for classificationmore »only may lead to performance losses in regression, and vice versa. Lowering the computational cost of producing grids will enable new population synthesis codes such asPOSYDONto cover more input parameters while preserving interpolation accuracies.

« less

Abstract

<p>This dataset contains machine learning and volunteer classifications from the Gravity Spy project. It includes glitches from observing runs O1, O2, O3a and O3b that received at least one classification from a registered volunteer in the project. It also indicates glitches that are nominally retired from the project using our default set of retirement parameters, which are described below. See more details in the Gravity Spy Methods paper. </p> <p>When a particular subject in a citizen science project (in this case, glitches from the LIGO datastream) is deemed to be classified sufficiently it is &#34;retired&#34; from the project. For the Gravity Spy project, retirement depends on a combination of both volunteer and machine learning classifications, and a number of parameterizations affect how quickly glitches get retired. For this dataset, we use a default set of retirement parameters, the most important of which are: </p> <ol><li>A glitches must be classified by at least 2 registered volunteers</li><li>Based on both the initial machine learning classification and volunteer classifications, the glitch has more than a 90% probability of residing in a particular class</li><li>Each volunteer classification (weighted by that volunteer&#39;s confusion matrix) contains a weight equal to the initial machine learning score when determining the final probability</li></ol> <p>The choice of these and other parameterization will affect the accuracy of the retired dataset as well as the number of glitches that are retired, and will be explored in detail in an upcoming publication (Zevin et al. in prep). </p> <p>The dataset can be read in using e.g. Pandas: <br /> &#96;&#96;&#96;<br /> import pandas as pd<br /> dataset &#61; pd.read_hdf(&#39;retired_fulldata_min2_max50_ret0p9.hdf5&#39;, key&#61;&#39;image_db&#39;)<br /> &#96;&#96;&#96;<br /> Each row in the dataframe contains information about a particular glitch in the Gravity Spy dataset. </p> <p><strong>Description of series in dataframe</strong></p> <ul><li>[&#39;1080Lines&#39;, &#39;1400Ripples&#39;, &#39;Air_Compressor&#39;, &#39;Blip&#39;, &#39;Chirp&#39;, &#39;Extremely_Loud&#39;, &#39;Helix&#39;, &#39;Koi_Fish&#39;, &#39;Light_Modulation&#39;, &#39;Low_Frequency_Burst&#39;, &#39;Low_Frequency_Lines&#39;, &#39;No_Glitch&#39;, &#39;None_of_the_Above&#39;, &#39;Paired_Doves&#39;, &#39;Power_Line&#39;, &#39;Repeating_Blips&#39;, &#39;Scattered_Light&#39;, &#39;Scratchy&#39;, &#39;Tomte&#39;, &#39;Violin_Mode&#39;, &#39;Wandering_Line&#39;, &#39;Whistle&#39;] <ul><li>Machine learning scores for each glitch class in the trained model, which for a particular glitch will sum to unity</li></ul> </li><li>[&#39;ml_confidence&#39;, &#39;ml_label&#39;] <ul><li>Highest machine learning confidence score across all classes for a particular glitch, and the class associated with this score</li></ul> </li><li>[&#39;gravityspy_id&#39;, &#39;id&#39;] <ul><li>Unique identified for each glitch on the Zooniverse platform (&#39;gravityspy_id&#39;) and in the Gravity Spy project (&#39;id&#39;), which can be used to link a particular glitch to the full Gravity Spy dataset (which contains GPS times among many other descriptors)</li></ul> </li><li>[&#39;retired&#39;] <ul><li>Marks whether the glitch is retired using our default set of retirement parameters (1&#61;retired, 0&#61;not retired)</li></ul> </li><li>[&#39;Nclassifications&#39;] <ul><li>The total number of classifications performed by registered volunteers on this glitch</li></ul> </li><li>[&#39;final_score&#39;, &#39;final_label&#39;] <ul><li>The final score (weighted combination of machine learning and volunteer classifications) and the most probable type of glitch</li></ul> </li><li>[&#39;tracks&#39;] <ul><li>Array of classification weights that were added to each glitch category due to each volunteer&#39;s classification</li></ul> </li></ul> <p> </p> <p>&#96;&#96;&#96;<br /> For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see Gravity Spy Machine Learning Classifications on Zenodo</p> <p>For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo.</p> <p>For detailed information on the training set used for the original Gravity Spy machine learning paper, please see Machine learning for Gravity Spy: Glitch classification and dataset on Zenodo. </p>
More>>

Long-duration gamma-ray bursts are thought to be associated with the core-collapse of massive, rapidly spinning stars and the formation of black holes. However, efficient angular momentum transport in stellar interiors, currently supported by asteroseismic and gravitational-wave constraints, leads to predominantly slowly-spinning stellar cores. Here, we report on binary stellar evolution and population synthesis calculations, showing that tidal interactions in close binaries not only can explain the observed subpopulation of spinning, merging binary black holes but also lead to long gamma-ray bursts at the time of black-hole formation. Given our model calibration against the distribution of isotropic-equivalent energies of luminous long gamma-ray bursts, we find that ≈10% of the GWTC-2 reported binary black holes had a luminous long gamma-ray burst associated with their formation, with GW190517 and GW190719 having a probability of ≈85% and ≈60%, respectively, being among them. Moreover, given an assumption about their average beaming fraction, our model predicts the rate density of long gamma-ray bursts, as a function of redshift, originating from this channel. For a constant beaming fraction f B  ∼ 0.05 our model predicts a rate density comparable to the observed one, throughout the redshift range, while, at redshift z  ∈ [0, 2.5], a tentative comparison with the metallicitymore »distribution of observed LGRB host galaxies implies that between 20% to 85% of the observed long gamma-ray bursts may originate from progenitors of merging binary black holes. The proposed link between a potentially significant fraction of observed, luminous long gamma-ray bursts and the progenitors of spinning binary black-hole mergers allows us to probe the latter well outside the horizon of current-generation gravitational wave observatories, and out to cosmological distances.« less

We describe the public release of the Cluster Monte Carlo (CMC) code, a parallel, star-by-starN-body code for modeling dense star clusters.CMCtreats collisional stellar dynamics using Hénon’s method, where the cumulative effect of many two-body encounters is statistically reproduced as a single effective encounter between nearest-neighbor particles on a relaxation timescale. The star-by-star approach allows for the inclusion of additional physics, including strong gravitational three- and four-body encounters, two-body tidal and gravitational-wave captures, mass loss in arbitrary galactic tidal fields, and stellar evolution for both single and binary stars. The public release ofCMCis pinned directly to theCOSMICpopulation synthesis code, allowing dynamical star cluster simulations and population synthesis studies to be performed using identical assumptions about the stellar physics and initial conditions. As a demonstration, we present two examples of star cluster modeling: first, we perform the largest (N= 10⁸) star-by-starN-body simulation of a Plummer sphere evolving to core collapse, reproducing the expected self-similar density profile over more than 15 orders of magnitude; second, we generate realistic models for typical globular clusters, and we show that their dynamical evolution can produce significant numbers of black hole mergers with masses greater than those produced from isolated binary evolution (such as GW190521, amore »recently reported merger with component masses in the pulsational pair-instability mass gap).

« less

Long gamma-ray bursts are associated with the core-collapse of massive, rapidly spinning stars. However, the believed efficient angular momentum transport in stellar interiors leads to predominantly slowly-spinning stellar cores. Here, we report on binary stellar evolution and population synthesis calculations, showing that tidal interactions in close binaries not only can explain the observed sub-population of spinning, merging binary black holes, but also lead to long gamma-ray bursts at the time of black-hole formation, with rates matching the empirical ones. We find that ≈10% of the GWTC-2 reported binary black holes had a long gamma-ray burst associated with their formation, with GW190517 and GW190719 having a probability of ≈85% and ≈60%, respectively, being among them.

Abstract

<p>This data set contains all classifications that the Gravity Spy Machine Learning model for LIGO glitches from the first three observing runs (O1, O2 and O3, where O3 is split into O3a and O3b). Gravity Spy classified all noise events identified by the Omicron trigger pipeline in which Omicron identified that the signal-to-noise ratio was above 7.5 and the peak frequency of the noise event was between 10 Hz and 2048 Hz. To classify noise events, Gravity Spy made Omega scans of every glitch consisting of 4 different durations, which helps capture the morphology of noise events that are both short and long in duration.</p> <p>There are 22 classes used for O1 and O2 data (including No_Glitch and None_of_the_Above), while there are two additional classes used to classify O3 data.</p> <p>For O1 and O2, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Chirp, Extremely_Loud, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle</p> <p>For O3, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, <strong>Blip_Low_Frequency</strong>, Chirp, Extremely_Loud, <strong>Fast_Scattering</strong>, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle</p> <p>If you would like to download the Omega scans associated with each glitch, then you can use the gravitational-wave data-analysis tool GWpy. If you would like to use this tool, please install anaconda if you have not already and create a virtual environment using the following command</p> <p>&#96;&#96;&#96;conda create --name gravityspy-py38 -c conda-forge python&#61;3.8 gwpy pandas psycopg2 sqlalchemy&#96;&#96;&#96;</p> <p>After downloading one of the CSV files for a specific era and interferometer, please run the following Python script if you would like to download the data associated with the metadata in the CSV file. We recommend not trying to download too many images at one time. For example, the script below will read data on Hanford glitches from O2 that were classified by Gravity Spy and filter for only glitches that were labelled as Blips with 90% confidence or higher, and then download the first 4 rows of the filtered table.</p> <p>&#96;&#96;&#96;</p> <p>from gwpy.table import GravitySpyTable</p> <p>H1_O2 &#61; GravitySpyTable.read(&#39;H1_O2.csv&#39;)</p> <p>H1_O2[(H1_O2[&#34;ml_label&#34;] &#61;&#61; &#34;Blip&#34;) &amp; (H1_O2[&#34;ml_confidence&#34;] &gt; 0.9)]</p> <p>H1_O2[0:4].download(nproc&#61;1)</p> <p>&#96;&#96;&#96;</p> <p>Each of the columns in the CSV files are taken from various different inputs: </p> <p>[‘event_time’, ‘ifo’, ‘peak_time’, ‘peak_time_ns’, ‘start_time’, ‘start_time_ns’, ‘duration’, ‘peak_frequency’, ‘central_freq’, ‘bandwidth’, ‘channel’, ‘amplitude’, ‘snr’, ‘q_value’] contain metadata about the signal from the Omicron pipeline. </p> <p>[‘gravityspy_id’] is the unique identifier for each glitch in the dataset. </p> <p>[‘1400Ripples’, ‘1080Lines’, ‘Air_Compressor’, ‘Blip’, ‘Chirp’, ‘Extremely_Loud’, ‘Helix’, ‘Koi_Fish’, ‘Light_Modulation’, ‘Low_Frequency_Burst’, ‘Low_Frequency_Lines’, ‘No_Glitch’, ‘None_of_the_Above’, ‘Paired_Doves’, ‘Power_Line’, ‘Repeating_Blips’, ‘Scattered_Light’, ‘Scratchy’, ‘Tomte’, ‘Violin_Mode’, ‘Wandering_Line’, ‘Whistle’] contain the machine learning confidence for a glitch being in a particular Gravity Spy class (the confidence in all these columns should sum to unity). </p> <p>[‘ml_label’, ‘ml_confidence’] provide the machine-learning predicted label for each glitch, and the machine learning confidence in its classification. </p> <p>[‘url1’, ‘url2’, ‘url3’, ‘url4’] are the links to the publicly-available Omega scans for each glitch. ‘url1’ shows the glitch for a duration of 0.5 seconds, ‘url2’ for 1 seconds, ‘url3’ for 2 seconds, and ‘url4’ for 4 seconds.</p> <p>&#96;&#96;&#96;</p> <p>For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo.</p> <p>For detailed information on the training set used for the original Gravity Spy machine learning paper, please see Machine learning for Gravity Spy: Glitch classification and dataset on Zenodo. </p>
More>>