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- MakaleYalnız künye2024Scopus SJRQ2Deep learning-assisted Hubble parameter analysis
Salti, Mehmet; Kangal, Evrim Ersin; Zengin, Bilgin
We turn our attention on evaluating the most recent Hubble parameter data measured via the differential evolution of cosmic-chronometers from a deep learning perspective. To achieve this goal, we start our investigation by introducing the selected theoretical setup and compiling the most recent statistical data obtained in cosmology experiments. Then we implement a tuned version of the long-short term memory (LSTM) architecture and run it to predict possible values of the Cosmic Hubble parameter for different red-shift states. Since we observe a good correlation between the observed and predicted datasets of the Hubble parameter, we conclude that the machine learning approaches can play important roles in the future cosmology investigations.
AtıfScopus2WoS2OpenAlex2
İncele - MakaleYalnız künye2022Scopus SJRQ2Data-driven predictive modeling of Hubble parameter
Salti, Mehmet; Ciger, Emel; Kangal, Evrim Ersin; Zengin, Bilgin
We redesign the generalized pressure dark energy (GPDE) model, which is covering three common types of pressure parameterizations, with the help of a caloric framework to construct a theoretical ground for the machine learning (ML) analysis of cosmic Hubble parameter. The theoretical setup was optimized to find out appropriate values of its arbitrary parameters with the help of genetic neural network (GNN) algorithm and the most recent observational measurements of Hubble parameter. Since there is a shortcoming that the GNN process does not provide a direct method to calculate errors on the optimized values of free model parameters, we therefore take the Fisher Information Matrix (FIM) algorithm into account to deal with this issue. We see that the best-fitting value of Hubble constant and dimensionless dark energy density are in very good agreement with the most recent observations. Also, we discussed the optimized model from a cosmological perspective by making use of the evolutionary behavior of some cosmological parameters to present additional cosmological aspects of our theoretical proposal. It is concluded that our model implies physically meaningful results. In summary, the constructed model can explain the current accelerated expansion phase of the cosmos via Hubble parameter successfully.
AtıfScopus7WoS5İncele - MakaleYalnız künye2019Scopus SJRQ2Polytropic Carnot heat engine
Askin, M.; Salti, M.; Aydogdu, O.
Recent astrophysical datasets have implied that the universe has entered a speedy expansion phase. The Polytropic gas model, which describes a unified formulation of dark contents (matter plus energy), is one of the most reasonable definitions of this mysterious phenomenon. This interesting formulation allows to simulate the dark contents in the cosmic form of the perfect fluid and gives an interesting point of view in the discussion of fundamental theories of physics. In the first step of our investigation, we discuss the thermal equation-of-state (EoS henceforth) and obtain the EoS and deceleration parameters as explicit functions of temperature. Subsequently, we obtain a relation for the thermal efficiency of the Carnot heat engine which depends on free parameters given in the cosmological Polytropic gas description and the limits of maximal and minimal temperatures imposed on the Carnot cycle.
AtıfScopus8WoS8OpenAlex9İncele - MakaleYalnız künye2017Scopus SJRQ2Cosmology via thermodynamics of polytropic gas
Askin, Muzaffer; Salti, Mustafa; Aydogdu, Oktay
We study cosmological scenarios of the polytropic gas (PG) dark matter-energy proposal in a Friedmann-Robertson-Walker (FRW) Universe. As a first step, we obtain an exact expression for the energy density of PG model by making use of the thermodynamics. Later, we investigate some cosmological quantities and perform neo-classical analyzes. Finally, we implement a connection between the PG and a homogenous minimally coupled scalar field by introducing its self-interacting potential.
AtıfScopus10WoS10OpenAlex13İncele