925 research outputs found

    Using long-term millisecond pulsar timing to obtain physical characteristics of the bulge globular cluster Terzan 5

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    Over the past decade the discovery of three unique stellar populations and a large number of confirmed pulsars within the globular cluster Terzan 5 has raised questions over its classification. Using the long-term radio pulsar timing of 36 millisecond pulsars in the cluster core, we provide new measurements of key physical properties of the system. As Terzan 5 is located within the galactic bulge, stellar crowding and reddening make optical and near infrared observations difficult. Pulsar accelerations, however, allow us to study the intrinsic characteristics of the cluster independent of reddening and stellar crowding and probe the mass density profile without needing to quantify the mass to light ratio. Relating the spin and orbital periods of each pulsar to the acceleration predicted by a King model, we find a core density of 1.58×1.58\times106^6 M_\odot pc3^{-3}, a core radius of 0.16 pc, a pulsar density profile nr3.14n\propto r^{-3.14}, and a total mass of MT_{\rm T}(R<R_\perp<1.0 pc)3.0×\simeq3.0\times105^5 M_\odot assuming a cluster distance of 5.9 kpc. Using this information we argue against Terzan 5 being a disrupted dwarf galaxy and discuss the possibility of Terzan 5 being a fragment of the Milky Way's proto-bulge. We also discuss whether low-mass pulsars were formed via electron capture supernovae or exist in a core full of heavy white dwarfs and hard binaries. Finally we provide an upper limit for the mass of a possible black hole at the core of the cluster of 3.0×\times104^4 M_\odot.Comment: 27 pages, 20 figures, 5 tables, thesis research, accepte

    Enabling real-time multi-messenger astrophysics discoveries with deep learning

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    Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics

    Order out of Randomness : Self-Organization Processes in Astrophysics

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    Self-organization is a property of dissipative nonlinear processes that are governed by an internal driver and a positive feedback mechanism, which creates regular geometric and/or temporal patterns and decreases the entropy, in contrast to random processes. Here we investigate for the first time a comprehensive number of 16 self-organization processes that operate in planetary physics, solar physics, stellar physics, galactic physics, and cosmology. Self-organizing systems create spontaneous {\sl order out of chaos}, during the evolution from an initially disordered system to an ordered stationary system, via quasi-periodic limit-cycle dynamics, harmonic mechanical resonances, or gyromagnetic resonances. The internal driver can be gravity, rotation, thermal pressure, or acceleration of nonthermal particles, while the positive feedback mechanism is often an instability, such as the magneto-rotational instability, the Rayleigh-B\'enard convection instability, turbulence, vortex attraction, magnetic reconnection, plasma condensation, or loss-cone instability. Physical models of astrophysical self-organization processes involve hydrodynamic, MHD, and N-body formulations of Lotka-Volterra equation systems.Comment: 61 pages, 38 Figure

    Gravitation Theory Based Model for Multi-Label Classification

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    The past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. In the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. In light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (ITDGM). The algorithm replaces the interaction between two objects with the attraction between two particles. The author carries out a series of experiments on five multi-label datasets. The experimental results show that the ITDGM performs better than some well-known multi-label classification algorithms. The effect of the proposed model is assessed by the example-based F1-Measure and Label-based micro F1-measure

    Tidal disruption, global mass function and structural parameters evolution in star clusters

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    [abridged] We present a unified picture for the evolution of star clusters on the two-body relaxation timescale. We use direct N-body simulations of star clusters in a galactic tidal field starting from different multi-mass King models, up to 10% of primordial binaries and up to Ntot=65536 particles. An additional run also includes a central Intermediate Mass Black Hole. We find that for the broad range of initial conditions we have studied the stellar mass function of these systems presents a universal evolution which depends only on the fractional mass loss. The structure of the system, as measured by the core to half mass radius ratio, also evolves toward a universal state, which is set by the efficiency of heating on the visible population of stars induced by dynamical interactions in the core of the system. Interactions with dark remnants are dominant over the heating induced by a moderate population of primordial binaries (3-5%), especially under the assumption that most of the neutron stars and black holes are retained in the system. All our models without primordial binaries undergo a deep gravothermal collapse in the radial mass profile. However their projected light distribution can be well fitted by medium concentration King models (with parameter W0 ~ 8), even though there tends to be an excess over the best fit for the innermost points of the surface brightness. This excess is consistent with a shallow cusp in the surface brightness (mu(R) ~ R^{-v} with v ~ 0.4-0.7), like it has been observed for many globular clusters from high-resolution HST imaging. Classification of core-collapsed globular clusters based on their surface brightness profile is likely to fail in systems that have already bounced back to lower concentrations.Comment: 33 pages, 11 figures, ApJ accepte

    Low-Energy Neutrino Astrophysics with Super-Kamiokande

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    Core-Collapse Supernovae (CCSNe) are among the most powerful events in the cosmos where neutrinos play key role carrying away about 99% of the energy available. Despite the progress in the numerical studies the full details of the explosion mechanism are still unknown and only the novel observation of the neutrino burst from the next nearby CCSN will enable us to shed light on this phenomenon. The Super-Kamiokande(SK) experiment is an underground water Cherenkov neutrino detector consisting in a very large volume cylinder filled with ultrapure water (50 kton) readout by about 11,000 photomultipliers and being located under 1,000m overburden of rock (2,700 m.w.e.). SK is part of the Supernova Early Warning System (SNEWS), network which is coordinating most of the large-scale neutrino telescopes around the globe for the detection of such bursts, with a short latency. The current system at SK is designed to send normal warning if more than 25 events are detected within 20 seconds, which allows the efficient detection of the CCSNe up to the Small Magellanic Cloud. Offline analyses are also performed where only 7-8 events are required, allowing the extension of the search horizon up to few hundreds of kpc. The main aim of the Thesis consists in exploring the differences between the time profiles of the SN neutrino signal and the standard poissonian background trend, in order to lower the current multiplicity thresholds of the real-time monitor, providing fast alerts within fixed false positive rate (1 false alarm per century). To this purpose, new original statistical methods have been developed introducing additional cuts based not only on the absolute number of events but also on the characteristic time scale of the candidate SN clusters. The performances with different clustering algorithms have been tested as well

    Power System Stability Analysis using Neural Network

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    This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio

    POSYDON: A General-Purpose Population Synthesis Code with Detailed Binary-Evolution Simulations

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    Most massive stars are members of a binary or a higher-order stellar systems, where the presence of a binary companion can decisively alter their evolution via binary interactions. Interacting binaries are also important astrophysical laboratories for the study of compact objects. Binary population synthesis studies have been used extensively over the last two decades to interpret observations of compact-object binaries and to decipher the physical processes that lead to their formation. Here, we present POSYDON, a novel, binary population synthesis code that incorporates full stellar-structure and binary-evolution modeling, using the MESA code, throughout the whole evolution of the binaries. The use of POSYDON enables the self-consistent treatment of physical processes in stellar and binary evolution, including: realistic mass-transfer calculations and assessment of stability, internal angular-momentum transport and tides, stellar core sizes, mass-transfer rates and orbital periods. This paper describes the detailed methodology and implementation of POSYDON, including the assumed physics of stellar- and binary-evolution, the extensive grids of detailed single- and binary-star models, the post-processing, classification and interpolation methods we developed for use with the grids, and the treatment of evolutionary phases that are not based on pre-calculated grids. The first version of POSYDON targets binaries with massive primary stars (potential progenitors of neutron stars or black holes) at solar metallicity.Comment: 60 pages, 33 figures, 8 tables, referee's comments addressed. The code and the accompanying documentations and data products are available at https:\\posydon.or

    Doctor of Philosophy

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    dissertationType Ia supernovae (SNe Ia) have been used as standard candles to measure cosmological distances. The initial discovery of the accelerated expansion of the universe was performed using ∼ 50 SNe Ia. Large SNe surveys have increased the number of spectroscopically-confirmed SNe Ia to over a thousand with redshift coverage beyond z = 1. We are now in the age of abundant photometry without the ability for full follow-up spectroscopy of all SN candidates. SN cosmology using these large samples will increasingly rely on robust photometric classification of SN candidates. Photometric classification will increase the sample by including faint SNe as these are preferentially not observed with follow-up spectroscopy. The primary concern with using photometrically classified SNe Ia in cosmology is when a core-collapse SNe is incorrectly classified as an SN Ia. This can be mitigated by obtaining the host galaxy redshift of each SN candidate and using this information as a prior in the photometric classification, removing one degree of freedom. To test the impact of redshift on photometric classification, I have performed an assessment on photometric classification of candidates from the Sloan Digital Sky Survey-II (SDSS-II) SN Survey. I have tested the classification with and without redshift priors by looking at the change of photometric classification, the effect of data quality on photometric classification, and the effect of SN light curve properties on photometric classification. Following our suggested classification scheme, there are a total of 1038 photometrically classified SNe Ia when using a flat redshift prior and 1002 SNe Ia with the spectroscopic redshift. For 912 (91.0%) candidates classified as likely SNe Ia without redshift information, the classification is unchanged when adding the host galaxy redshift. Finally, I investigate the differences in the interpretation of the light curve properties with and without knowledge of the redshift. When using the SALT2 light curve fitter, I find a 17% increase in the number of fits that converge when using the spectroscopic redshift. Without host galaxy redshifts, I find that SALT2 light curve fits are systematically biased towards lower photometric redshift estimates and redder colors in the limit of low signal-to-noise data
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