arXiv subjects

Muhammad Waqas

Publications and source records attributed to Muhammad Waqas.

45 records · Page 3Linked to original sources

arXiv2020

Energy scan/dependence of kinetic freeze-out scenarios of multi-strange and other identified particles in central nucleus-nucleus collisions

The transverse momentum (mass) spectra of the multi-strange and non-multi-strange (i.e. other identified) particles in central gold-gold (Au-Au), lead-lead (Pb-Pb), argon-muriate (Ar-KCl) and nickel-nickel (Ni-Ni) collisions over a wide energy range have been studied in this work. The experimental data measured by various collaborations have been analyzed. The blast-wave fit with Tsallis statistics is used to extract the kinetic freeze-out temperature and transverse flow velocity from the experimental data of transverse momentum (mass) spectra. The extracted parameters increase with the increase of collision energy and appear with the trend of saturation at the Beam Energy Scan (BES) energies at the Relativistic Heavy Ion Collider (RHIC). This saturation implies that the onset energy of phase transition of partial deconfinement is 7.7 GeV and that of whole deconfinement is 39 GeV. Furthermore, the energy scan/dependence of kinetic freeze-out scenarios are observed for the multi-strange and other identified particles, though the multiple freeze-out scenarios are also observed for various particles.

hep-ph↗

arXiv2020

Deep Learning Techniques for Future Intelligent Cross-Media Retrieval

With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention. It plays a significant role in big data applications and consists in searching and finding data from different types of media. In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation. These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based methods. Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. Moreover, we also present an extensive review of the state-of-the-art problems and its corresponding solutions for encouraging deep learning in cross-media retrieval. The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems and the potential solutions of deep learning assisted cross-media retrieval. To the best of our knowledge, this is the first comprehensive survey to address cross-media retrieval under deep learning methods.

cs.IR↗

arXiv2020

Dependence of temperatures and kinetic freeze-out volume on centrality in Au-Au and Pb-Pb collisions at high energy

Centrality-dependent double-differential transverse momentum spectra of negatively charged particles ($π^-$, $K^-$ and $\bar p$) at mid-(pseudo)rapidity interval in nuclear collisions are analyzed by the standard distribution in terms of multi-component. The experimental data measured in gold-gold (Au-Au) collisions by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC) and in lead-lead (Pb-Pb) collisions by the ALICE Collaboration at the Large Hadron Collider (LHC) are studied. The effective temperature, initial temperature, kinetic freeze-out temperature, transverse flow velocity and kinetic freeze-out volume are extracted from the fitting to transverse momentum spectra. We observed, that the mentioned five quantities increase with the increase of event centrality due to the fact that the average transverse momentum increases with the increase of event centrality. This renders that larger momentum (energy) transfer and further multiple-scattering had happened in central centrality.

hep-ph↗

arXiv2020

Analysis and Prediction of COVID-19 Pandemic in Pakistan using Time-dependent SIR Model

The current outbreak is known as Coronavirus Disease or COVID-19 caused by the virus SAR-COV-2 which continues to wreak havoc across the globe. The World Health Organization (WHO) has declared the outbreak a Public Health Emergency of International Concern. In Pakistan, the spread of the virus is on the rise with the number of infected people and causalities rapidly increasing. In the absence of proper vaccination and treatment, to reduce the number of infections and casualties, the only option so far is to educate people regarding preventive measures and to enforce countrywide lock-down. Any strategy about the preventive measures needs to be based upon detailed analysis of the COVID-19 outbreak and accurate scientific predictions. In this paper, we conduct mathematical and numerical analysis to come up with reliable and accurate predictions of the outbreak in Pakistan. The time-dependent Susceptible-Infected-Recovered (SIR) model is used to fit the data and provide future predictions. The turning point of the peak of the pandemic is defined as the day when the transmission rate becomes less than the recovering rate. We have predicted that the outbreak will reach its maximum peak occurring from late May to 9 June with unrecovered number of Infectives in the range 20000-47000 and the cumulative number of infected cases in the range of 57500-153100. The number of Infectives will remain at the lower end in the lock-down scenario but can rapidly double or triple if the spread of the epidemic is not curtailed and localized. The uncertainty on single day projection in our analysis after April 15 is found to be within 5\%.

q-bio.PE↗

arXiv2020

Excitation functions of related parameters from transverse momentum (mass) spectra in high energy collisions

Transverse momentum (mass) spectra of positively and negatively charged pions, positively and negatively charged kaons, protons and antiprotons produced at mid-(pseudo)rapidity in various collisions at high energies are analyzed in this work. The experimental data measured in central gold-gold, central lead-lead, and inelastic proton-proton collisions by several international collaborations are studied. The (two-component) standard distribution is used to fit the data and extract the excitation function of effective temperature. Then, the excitation functions of kinetic freeze-out temperature, transverse flow velocity, and initial temperature are obtained. In the considered collisions, the four parameters increase with the increase of collision energy in general, and the kinetic freeze-out temperature appears the trend of saturation at the top Relativistic Heavy Ion Collider and the Large Hadron Collider.

hep-ph↗

arXiv2019

Initial, effective, and kinetic freeze-out temperatures from transverse momentum spectra in high energy proton(deuteron)-nucleus and nucleus-nucleus collisions

The transverse momentum spectra of charged particles produced in proton(deuteron)-nucleus and nucleus-nucleus collisions at high energies are analyzed by the Hagedorn thermal model and the standard distribution in terms of multi-component. The experimental data measured in central and peripheral gold-gold (Au-Au) and deuteron-gold ($d$-Au) collisions by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC), as well as in central and peripheral lead-lead (Pb-Pb) and proton-lead ($p$-Pb) collisions by the ALICE Collaboration at the Large Hadron Collider (LHC) are fitted by the two models. The initial, effective, and kinetic freeze-out temperatures are then extracted from the fitting to the transverse momentum spectra. It is shown that the initial temperature is larger than the effective temperature, and the effective temperature is larger than the kinetic freeze-out temperature. The three types of temperatures in central collisions are comparable with those in peripheral collisions, and those at the LHC are comparable with those at the RHIC.

hep-ph↗

arXiv2019

Kinetic freeze-out temperature and transverse flow velocity in Au-Au collisions at RHIC-BES energies

Based on the data-driven analysis, the mid-rapidity transverse momentum spectra of charged hadrons produced in central and peripheral gold-gold (Au-Au) collisions from the Beam Energy Scan (BES) program at the relativistic Heavy Ion Collider (RHIC) are fitted by the blast-wave model with Boltzmann-Gibbs statistics. The model result are in agreement with the experimental data measured by the STAR Collaboration at the RHIC-BES energies. We observe that the kinetic freeze-out temperature, transverse flow velocity, mean transverse momentum, and initial temperature increase with the collision energy and with the event centrality.

hep-ph↗

arXiv2019

Water Preservation in Soan River Basin using Deep Learning Techniques

Water supplies are crucial for the development of living beings. However, change in the hydrological process i.e. climate and land usage are the key issues. Sustaining water level and accurate estimating for dynamic conditions is a critical job for hydrologists, but predicting hydrological extremes is an open issue. In this paper, we proposed two deep learning techniques and three machine learning algorithms to predict stream flow, given the present climate conditions. The results showed that the Recurrent Neural Network (RNN) or Long Short-term Memory (LSTM), an artificial neural network based method, outperform other conventional and machine-learning algorithms for predicting stream flow. Furthermore, we analyzed that stream flow is directly affected by precipitation, land usage, and temperature. These indexes are critical, which can be used by hydrologists to identify the potential for stream flow. We make the dataset publicly available (https://github.com/sadaqat007/Dataset) so that others should be able to replicate and build upon the results published.

cs.NE↗

arXiv2018

Possible scenarios for single, double, or multiple kinetic freeze-out in high energy collisions

Transverse momentum spectra of different types of particles produced in mid-rapidity interval in central and peripheral gold-gold (Au-Au) collisions, central and peripheral deuteron-gold ($d$-Au) collisions, and inelastic (INEL) or non-single-diffractive (NSD) proton-proton ($pp$) collisions at the Relativistic Heavy Ion Collider (RHIC), as well as in central and peripheral lead-lead (Pb-Pb) collisions, central and peripheral proton-lead ($p$-Pb) collisions, and INEL or NSD $pp$ collisions at the Large Hadron Collider (LHC) are analyzed by the blast-wave model with Boltzmann-Gibbs statistics. The model results are largely consist with the experimental data in special transverse momentum ranges measured by the PHENIX, STAR, ALICE, and CMS Collaborations. It is showed that the kinetic freeze-out temperature of emission source is dependent on particle mass, which reveals the scenario for multiple kinetic freeze-out in collisions at the RHIC and LHC. The scenario for single or double kinetic freeze-out is not observed in this study.

nucl-th↗