Digital Repository @ IITGNThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.https://repository.iitgn.ac.in:4432019-03-18T14:23:47Z2019-03-18T14:23:47ZTransport coefficients of hot magnetized QCD matter beyond the lowest Landau level approximationKurian, ManuMitra, SukanyaGhosh, SnigdhaChandra, Vinodhttps://repository.iitgn.ac.in/handle/123456789/42822019-03-12T05:31:49Z2019-02-01T00:00:00ZTransport coefficients of hot magnetized QCD matter beyond the lowest Landau level approximation
Kurian, Manu; Mitra, Sukanya; Ghosh, Snigdha; Chandra, Vinod
In this article, shear viscosity, bulk viscosity, and thermal conductivity of a QCD medium have been studied in the presence of a strong magnetic field. To model the quark�gluon plasma, an extended quasi-particle description of the hot QCD equation of state in the presence of the magnetic field has been adopted. The effects of higher Landau levels on the temperature dependence of viscous coefficients (bulk and shear viscosities) and thermal conductivity have been obtained by considering the�1?21?2�processes in the presence of the strong magnetic field. An effective covariant kinetic theory has been set up in (1+1)-dimensional that includes mean field contributions in terms of quasi-particle dispersions and magnetic field to describe the Landau level dynamics of quarks. The sensitivity of these parameters to the magnitude of the magnetic field has also been explored. Both the magnetic field and mean field contributions have seen to play a significant role in obtaining the temperature behaviour of the transport coefficients of the medium.
2019-02-01T00:00:00ZTesting dark energy models in the light of ?8 tensionLambiase, GaetanoMohanty, SubhendraNarang, AshishParashari, Priyankhttps://repository.iitgn.ac.in/handle/123456789/42832019-03-12T05:31:49Z2019-02-01T00:00:00ZTesting dark energy models in the light of ?8 tension
Lambiase, Gaetano; Mohanty, Subhendra; Narang, Ashish; Parashari, Priyank
It has been pointed out that there exists a tension in�?8??m?8??m�measurement between CMB and LSS observation. In this paper we show that�?8??m?8??m�observations can be used to test the dark energy theories. We study two models, (1) Hu�Sawicki (HS) Model of�f(R) gravity and (2) Chavallier�Polarski�Linder (CPL) parametrization of dynamical dark energy (DDE), both of which satisfy the constraints from supernovae. We compute�?8?8�consistent with the parameters of these models. We find that the well known tension in�?8?8�between Planck CMB and large scale structure (LSS) observations is (1) exacerbated in the HS model and (2) somewhat alleviated in the DDE model. We illustrate the importance of the�?8?8�measurements for testing modified gravity models. Modified gravity models change the matter power spectrum at cluster scale which also depends upon the neutrino mass. We present the bound on neutrino mass in the HS and DDE model.
2019-02-01T00:00:00ZConstraining the p-Mode�g-Mode tidal instability with GW170817Sengupta, Anandhttps://repository.iitgn.ac.in/handle/123456789/42842019-03-12T05:31:49Z2019-02-01T00:00:00ZConstraining the p-Mode�g-Mode tidal instability with GW170817
Sengupta, Anand
We analyze the impact of a proposed tidal instability coupling�p�modes and�g�modes within neutron stars on GW170817. This nonresonant instability transfers energy from the orbit of the binary to internal modes of the stars, accelerating the gravitational-wave driven inspiral. We model the impact of this instability on the phasing of the gravitational wave signal using three parameters per star: an overall amplitude, a saturation frequency, and a spectral index. Incorporating these additional parameters, we compute the Bayes factor (lnBpg!pg) comparing our�p?g�model to a standard one. We find that the observed signal is consistent with waveform models that neglect�p?g�effects, with�lnBpg!pg=0.03+0.70?0.58(maximum�a�posteriori�and 90% credible region). By injecting simulated signals that do not include�p?geffects and recovering them with the�p?g�model, we show that there is a�?50%�probability of obtaining similar�lnBpg!pg�even when�p?g�effects are absent. We find that the�p?g�amplitude for�1.4??M?�neutron stars is constrained to less than a few tenths of the theoretical maximum, with maxima�a�posteriori�near one-tenth this maximum and�p?g�saturation frequency�?70??Hz. This suggests that there are less than a few hundred excited modes, assuming they all saturate by wave breaking. For comparison, theoretical upper bounds suggest�?103�modes saturate by wave breaking. Thus, the measured constraints only rule out extreme values of the�p?g�parameters. They also imply that the instability dissipates�?1051??erg�over the entire inspiral, i.e., less than a few percent of the energy radiated as gravitational waves.
2019-02-01T00:00:00ZPrediction of the disease controllability in a complex network using machine learning algorithmsTripathi, RichaReza, AmitGarg, Dineshhttps://repository.iitgn.ac.in/handle/123456789/42882019-03-12T05:31:49Z2019-02-01T00:00:00ZPrediction of the disease controllability in a complex network using machine learning algorithms
Tripathi, Richa; Reza, Amit; Garg, Dinesh
The application of machine learning (ML) techniques spans a vast spectrum of applications ranging from speech, face and character recognition to medical diagnosis to anomaly detection in data and the general classification, prediction and regression problems. In the present work, we demonstrate the application of regression-based state-of-art machine learning techniques to a prediction of disease controllability on complex networks. The complex network models determine the space for distribution of a population of individuals and their interactions with each other. There is numerous epidemic spreading models such as SI (Susceptible-Infected), SIR (Susceptible-Infected-Recovered), SEIR (Susceptible-Exposed-Infected-Recovered), etc., that govern the disease spreading dynamics over time and the stationary state of the disease. We simulate the disease spreading dynamics on a large number of complex networks examples of standard model networks, and determine the basic reproduction number (R 0 ) for each case. R_0 is a metric that determines whether the disease-free epidemic or an endemic state is asymptotically stable. In other words, it determines whether an infectious disease can spread in the population or will die out in the long run and hence indicates the disease controllability on a population. We aim to predict this quantity (R 0 ), based on the importance of complex networks structural properties using the regression techniques of ML, irrespective of the network type. The prediction is possible because of two facts (a) The structure of complex networks plays an essential role in the spreading processes on networks. (b) Availability of non-linear regression techniques with excellent accuracy for prediction of a quantity even for a data which is highly non-linear.
2019-02-01T00:00:00Z