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16 results for “search times”
Search for merger ejecta emission in Short Gamma Ray Bursts from very late time radio observations
<p>Coalescence of inspiral binary neutron stars (BNS) system, giving rise to short Gamma Ray Bursts (GRBs), are one of the most probable candidates for Gravitational Waves (GWs). If the resultant product of the merger is a millisecond magnetar, a significant proportion of the rotational energy deposited to emerging ejecta that produce late time radio brightening from the interaction with the surrounding ambient medium. Detection of this late-time radio emission from short GRBs can have profound implications for understanding the physics of the progenitor. This study presents the deepest and an extensive search for radio emission at late times following a short GRB to date incorporating proper frequency regime, wider observation span and relativistic correction. Five short GRBs were observed with the Giant Meter Wave Radio Telescope (GMRT) at 1250, 610, and 325 MHz band $\sim$ 2 - 11 years since the burst to search for radio emission from the merger ejecta. The estimated upper limits at the burst location are used to constrain the parameters of the burst and its surrounding environment. The magnetar model, with appropriate modifications, constrains the number density of the ambient medium for these bursts to be between $10^{-4}$ - $10^{-2}$ $cm^{-3}$. Our analysis rules out a stable magnetar with an energy of $10^{53}$ erg for four out of the five GRBs in our sample.</p>
Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"
<p>This repository contains the datasets used for training and testing during the work discussed in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad40f0" target="_blank" rel="noopener">Neural network time-series classifiers for gravitational-wave searches in single-detector periods</a>". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data:</p> <p><em>A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0.</em></p> <p>In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing.</p> <p>Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components:</p> <ol> <li>GPS start of the file from which this segment comes</li> <li>starting GPS time of this segment</li> <li>duration of the segment [s]</li> <li>mass1 [solar masses]</li> <li>mass2 [solar masses]</li> <li>spin1z</li> <li>spin2z</li> <li>inclination [radians]</li> <li>coalescence phase [radians]</li> <li>distance [Mpc]</li> <li>right_ascension [radians]</li> <li>declination [radians]</li> <li>polarization [radians]</li> <li>SNR (signal to noise ratio)</li> <li>shift of the signal w.r.t. the timeseries [s]</li> <li>length of the signal [s]</li> <li>fraction of the signal contained in the time window</li> </ol> <p>Number of samples:</p> <ul> <li>80000 for the file glitch_test.npz</li> <li>69998 for the file glitch_train.npz</li> <li>500000 for the file noise_test.npz</li> <li>250000 for the file noise_train.npz</li> <li>500000 for the file signal_test.npz</li> <li>250000 for the file signal_train.npz</li> </ul> <p>An example of few lines of python code to read each file is:</p> <pre><code>import numpy as np f = np.load("filename.npz") X = f['X'] Y = f['Y'] m = f['metadata'] </code></pre> <p>For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3]. </p> <p>This research has made use of data or software obtained from the Gravitational Wave Open Science Center (<a href="https://gwosc.org/" target="_blank" rel="noopener">gwosc.org</a>), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan.</p> <p>[1] https://gwpy.github.io<br>[2] https://pycbc.org<br>[3] https://lscsoft.docs.ligo.org/lalsuite</p>
The second data release from the European Pulsar Timing Array III. Search for gravitational wave signals
<p>We present the results of the search for an isotropic stochastic gravitational wave background (GWB) at nanohertz frequencies using the second data release of the European Pulsar Timing Array (EPTA) for 25 millisecond pulsars and a combination with the first data release of the Indian Pulsar Timing Array (InPTA). A robust GWB detection is conditioned upon resolving the Hellings-Downs angular pattern in the pairwise cross-correlation of the pulsar timing residuals. Additionally, the GWB is expected to yield the same (common) spectrum of temporal correlations across pulsars, which is used as a null hypothesis in the GWB search. Such a common-spectrum process has already been observed in pulsar timing data. We analysed (i) the full 24.7-year EPTA data set, (ii) its 10.3-year subset based on modern observing systems, (iii) the combination of the full data set with the first data release of the InPTA for ten commonly timed millisecond pulsars, and (iv) the combination of the 10.3-year subset with the InPTA data. These combinations allowed us to probe the contributions of instrumental noise and interstellar propagation effects. With the full data set, we find marginal evidence for a GWB, with a Bayes factor of four and a false alarm probability of 4%. With the 10.3-year subset, we report evidence for a GWB, with a Bayes factor of 60 and a false alarm probability of about 0.1% (≳ 3σ significance). The addition of the InPTA data yields results that are broadly consistent with the EPTA-only data sets, with the benefit of better noise modelling. Analyses were performed with different data processing pipelines to test the consistency of the results from independent software packages. The latest EPTA data from new generation observing systems show non-negligible evidence for the GWB. At the same time, the inferred spectrum is rather uncertain and in mild tension with the common signal measured in the full data set. However, if the spectral index is fixed at 13/3, the two data sets give a similar amplitude of (2.5 ± 0.7) × 10−15 at a reference frequency of 1 yr−1 . Further investigation of these issues is required for reliable astrophysical interpretations of this signal. By continuing our detection efforts as part of the International Pulsar Timing Array (IPTA), we expect to be able to improve the measurement of spatial correlations and better characterise this signal in the coming years.</p>
Computation Time, Searched Nodes and Path Length for Navigation Using Improved A-Star, Directional ORCA and FLC-ORCA
<p>Naivation simulation using Improved A*, Directional ORCA and Novel FLC-ORCA method. The computational times, searched nodes and path length are recorded for comparison. A simulation study for Robotic Navigation Aid (RNA)</p>
High time resolution search for prompt radio emission from the long GRB 210419A with the Murchison Widefield Array
<p>The time series of Stokes parameters in the PSRFITS format formed from the MWA data at the position of GRB 210419A.</p>
A search for planetary companions around 800 pulsars from the Jodrell Bank pulsar timing programme
<p>####### Nitu et al 2022 #######<br> #### Supplementary material ####</p> <p>This folder contains the summarised mass limits plots ['allPSRs_masslims.pdf'], (linearised) mass posterior distributions, as well as the 95% mass limit, for each period bin of each pulsar.</p> <p>In each $PSR folder there are 9 $period-bin folders,<br> corresponding to the ranges in Table 1 of Nitu et al (2022) as follows:<br> 'p21to42'<br> 'p42to85'<br> 'p85to170'<br> 'p170to340'<br> 'p340to390'<br> 'p390to780'<br> 'p780to1560'<br> 'p1560to3120'</p> <p>In each of the $period-bin folders, there is:<br> - a plot of linearised mass posterior: 'all_masses_linprior.pdf'<br> - a masslim_werr_linprior.npy file containing values, in order, for:<br> [mPeriod(days) sPeriod(days) Masslimit(Me) errMasslimit(Me) Detection(3sigma)]<br> -- where the period range is [mPeriod-sPeriod, mPeriod+sPeriod]<br> -- Masslimit is the value of the mass limit, in Earth masses for that period bin<br> -- errMasslimit is NOT a complete uncertainty, but just due to binning the posterior,<br> and should not be used for anything except checking the binning is appropriate<br> -- Detection is whether our analysis flagged a detection, i.e. whether mean > 3*sigma<br> Note: to read .npy files, use numpy.load(filename) in python<br> - [not all] a masslim_linprior.txt with the same information as the .npy file</p>
IQRM: real-time adaptive RFI masking for radio transient and pulsar searches
<p>This is repository of the data that were used for the analysis and results presented in "IQRM: real-time adaptive RFI masking for radio transient and pulsar searches" submitted to the Monthly Notices for the Royal Astronomical Society for publication. The readme file along with the data contains all the necessary information one would need to process the data. The data will be made available to everyone once the paper has been accepted for publication. If you are using data from this repository, please do not forget to cite the following paper:</p> <p>"IQRM: real-time adaptive RFI masking for radio transient and pulsar searches", V M Morello, K M Rajwade and B W Stappers, 2021, Monthly Notices of the Royal Astronomical Society, in press.</p>
Data from: Searching for common threads in threadfins: phylogeography of Australian Polynemids in space and time
Open the record for dataset details and reuse information.
Artifact for paper "Monte Carlo Tree Search for Priced Timed Automata"
<p>Artifact for paper "Monte Carlo Tree Search for Priced Timed Automata"</p>
Supplementary material 1 from: Cerri J, Carnevali L, Monaco A, Genovesi P, Bertolino S (2022) Blacklists do not necessarily make people curious about invasive alien species. A case study with Bayesian structural time series and Wikipedia searches about invasive mammals in Italy. NeoBiota 71: 113-128. https://doi.org/10.3897/neobiota.71.69422
Supplementary information
Data from: The effect of mood state on visual search times for detecting a target in noise: an application of smartphone technology
The study of visual perception has largely been completed without regard to the influence that an individual's emotional status may have on their performance in visual tasks. However, there is a growing body of evidence to suggest that mood may affect not only creative abilities and interpersonal skills but also the capacity to perform low-level cognitive tasks. Here, we sought to determine whether rudimentary visual search processes are similarly affected by emotion. Specifically, we examined whether an individual's perceived happiness level affects their ability to detect a target in noise. To do so, we employed pop-out and serial visual search paradigms, implemented using a novel smartphone application that allowed search times and self-rated levels of happiness to be recorded throughout each twenty-four-hour period for two weeks. This experience sampling protocol circumvented the need to alter mood artificially with laboratory-based induction methods. Using our smartphone application, we were able to replicate the classic visual search findings, whereby pop-out search times remained largely unaffected by the number of distractors whereas serial search times increased with increasing number of distractors. While pop-out search times were unaffected by happiness level, serial search times with the maximum numbers of distractors (n = 30) were significantly faster for high happiness levels than low happiness levels (p = 0.02). Our results demonstrate the utility of smartphone applications in assessing ecologically valid measures of human visual performance. We discuss the significance of our findings for the assessment of basic visual functions using search time measures, and for our ability to search effectively for targets in real world settings.
Searching for Masses and Calcifications at the Same Time in Breast Cancer Screening
ClinicalTrials.gov study NCT05975736. IPD Sharing: YES. Countries: 1. Publications: 0.
Use of the Aortic Time-velocity Integral Via Suprasternal Ultrasound to Search Preload Dependence in Paediatric Surgery : Kid's Fluid Management (FM)
ClinicalTrials.gov study NCT07099664. IPD Sharing: NO. Countries: 0. Publications: 0.
Data from: The effect of mood state on visual search times for detecting a target in noise: an application of smartphone technology
Open the record for dataset details and reuse information.
Multivariate Time Series Search
Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.
Fast and Flexible Multivariate Time Series Subsequence Search
Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.