Determining the Local Dark Matter Density with SDSS G-dwarf data

2017 ◽  
Vol 12 (S330) ◽  
pp. 255-258
Author(s):  
Hamish Silverwood ◽  
Sofia Sivertsson ◽  
Justin Read ◽  
Gianfranco Bertone ◽  
Pascal Steger
Keyword(s):  
Dark Matter ◽  
Vertical Velocity ◽  
Velocity Dispersion ◽  
Matter Density ◽  
Young Population ◽  
Reliable Determination ◽  
Dark Matter Density ◽  
To Come ◽  
Gaia Dr2

AbstractWe present a determination of the local dark matter density derived using the integrated Jeans equation method presented in Silverwoodet al.(2016) applied to SDSS-SEGUE G-dwarf data processed by Büdenbender et al. (2015). For our analysis we construct models for the tracer density, dark matter and baryon distribution, and tilt term (linking radial and vertical motions), and then calculate the vertical velocity dispersion using the integrated Jeans equation. These models are then fit to the data usingMultiNest, and a posterior distribution for the local dark matter density is derived. We find the most reliable determination to come from the α-young population presented in Büdenbenderet al.(2015), yielding a result of ρDM= 0.46+0.07−0.09GeV cm−3= 0.012+0.001−0.002M⊙pc−3. Our results also illuminate the path ahead for future analyses using Gaia DR2 data, highlighting which quantities will need to be determined and which assumptions could be relaxed.

scholarly journals A new determination of the local dark matter density from the kinematics of K dwarfs

2012 ◽  
Vol 425 (2) ◽  
pp. 1445-1458 ◽  
Author(s):  
Silvia Garbari ◽  
Chao Liu ◽  
Justin I. Read ◽  
George Lake
Keyword(s):  
Dark Matter ◽  
Matter Density ◽  
Dark Matter Density

scholarly journals Systematic uncertainties in the determination of the local dark matter density

2010 ◽  
Vol 82 (2) ◽  
Author(s):  
Miguel Pato ◽  
Oscar Agertz ◽  
Gianfranco Bertone ◽  
Ben Moore ◽  
Romain Teyssier
Keyword(s):  
Dark Matter ◽  
Matter Density ◽  
Dark Matter Density ◽  
Systematic Uncertainties

scholarly journals Determination of the local dark matter density using K-dwarfs from Gaia DR2

2020 ◽  
Vol 240 ◽  
pp. 04002
Author(s):  
M. Dafa Wardana ◽  
Hesti Wulandari ◽  
Sulistiyowati ◽  
Akbar H. Khatami
Keyword(s):  
Dark Matter ◽  
Galactic Disk ◽  
Matter Density ◽  
Solar Neighborhood ◽  
Stellar Disk ◽  
Vertical Position ◽  
Dark Halo ◽  
Detection Rates ◽  
Dark Matter Density ◽  
Gaia Dr2

Local dark matter density, ρdm, is one of the crucial astrophysical inputs for the estimation of detection rates in dark matter direct search experi- ments. Knowing the value also helps us to investigate the shape of the Galactic dark halo, which is of importance for indirect dark matter searches, as well as for various studies in astrophysics and cosmology. In this work, we performed kinematics study of stars in the solar neighborhood to determine the local dark matter density. As tracers we used 95,543 K-dwarfs from Gaia DR2 inside a heliocentric cylinder with a radius of 150 pc and height 200 pc above and below the Galactic mid plane. Their positions and motions were analyzed, assum- ing that the Galaxy is axisymmetric and the tracers are in dynamical equilib- rium. We applied Jeans and Poisson equations to relate the observed quantities, i.e. vertical position and velocity, with the local dark matter density. The tilt term in the Jeans equation is considered to be small and is therefore neglected. Galactic disk is modelled to consist of a single exponential stellar disk, a thin gas layer, and dark matter whose density is constant within the volume consid- ered. Marginalization for the free parameters was performed with Bayesian theorem using Markov Chain Monte Carlo (MCMC) method. We find that ρdm = 0.0116 ± 0.0012 MO/pc or ρdm = 0.439 ± 0.046 GeV/cm3, in agreement within the range of uncertainty with results of several previous studies.

scholarly journals Using Gaia DR2 to constrain local dark matter density and thin dark disk

2019 ◽  
Vol 2019 (04) ◽  
pp. 026-026 ◽  
Author(s):  
Jatan Buch ◽  
Shing Chau (John) Leung ◽  
JiJi Fan
Keyword(s):  
Dark Matter ◽  
Matter Density ◽  
Dark Matter Density ◽  
Gaia Dr2

scholarly journals Dark matter density extraction using Convolutional Neural Networks

2021 ◽  
Author(s):  
Pratik Dongre
Keyword(s):  
Neural Networks ◽  
Dark Matter ◽  
Convolutional Neural Networks ◽  
Matter Density ◽  
Machine Learning Techniques ◽  
Microwave Background ◽  
Vast Amount ◽  
Dark Matter Density ◽  
The Neural Networks ◽  
To Come

Abstract Ever since its discovery back in 1964 Cosmic Microwave Background (CMB) has been of great interest to cosmologists and played a crucial role in understanding and studying the early universe .One of the most interesting topic of current interest is dark matter and its existence is by now well established. By analyzing the CMB data we can estimate the dark matter density of the universe.With vast amount of astronomical data already present and a more vast amount which is to come in future, Machine Learning techniques can provide a variety of benefits in astrophysical and cosomological research. Here I explore the use of deep learning to estimate dark matter density. I have used convolutional neural networks in this paper. I have used simulated CMB temprature maps as a dataset to train the neural networks and correlate the dark matter density from the power spectrum of the corrseponding simlutaed CMB temprature map.

scholarly journals Determining the local dark matter density with LAMOST data

2016 ◽  
Vol 458 (4) ◽  
pp. 3839-3850 ◽  
Author(s):  
Qiran Xia ◽  
Chao Liu ◽  
Shude Mao ◽  
Yingyi Song ◽  
Lan Zhang ◽  
...  
Keyword(s):  
Dark Matter ◽  
Matter Density ◽  
Dark Matter Density

scholarly journals Isolated dwarf galaxies: from cuspy to flat dark matter density profiles and metalicity gradients

2010 ◽  
Vol 514 ◽  
pp. A47 ◽  
Author(s):  
S. Pasetto ◽  
E. K. Grebel ◽  
P. Berczik ◽  
R. Spurzem ◽  
W. Dehnen
Keyword(s):  
Dark Matter ◽  
Dwarf Galaxies ◽  
Matter Density ◽  
Density Profiles ◽  
Dark Matter Density

scholarly journals Non-Gaussian inference from non-linear and non-Poisson biased distributed data

2014 ◽  
Vol 10 (S306) ◽  
pp. 258-261
Author(s):  
Metin Ata ◽  
Francisco-Shu Kitaura ◽  
Volker Müller
Keyword(s):  
Dark Matter ◽  
Statistical Inference ◽  
Computer Code ◽  
Matter Density ◽  
Density Field ◽  
Distributed Data ◽  
Dark Matter Density ◽  
Posterior Sampling ◽  
Non Linear ◽  
Non Gaussian

AbstractWe study the statistical inference of the cosmological dark matter density field from non-Gaussian, non-linear and non-Poisson biased distributed tracers. We have implemented a Bayesian posterior sampling computer-code solving this problem and tested it with mock data based onN-body simulations.

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