Dark matter density extraction using Convolutional Neural Networks

Mapping Intimacies ◽

10.21203/rs.3.rs-612281/v1 ◽

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.

Download Full-text

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

Proceedings of the International Astronomical Union ◽

10.1017/s1743921317006044 ◽

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.

Download Full-text

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

Applied Sciences ◽

10.3390/app11041581 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1581

Author(s):

Jimy Oblitas ◽

Jezreel Mejia ◽

Miguel De-la-Torre ◽

Himer Avila-George ◽

Lucía Seguí Gil ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Cucurbita Pepo ◽

Performance Metrics ◽

Microstructural Analysis ◽

Relevant Information ◽

Machine Learning Techniques ◽

Radial Basis Neural Networks ◽

Vegetable Foods ◽

Vegetal Tissue

Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.

Download Full-text

Determining the local dark matter density with LAMOST data

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stw565 ◽

2016 ◽

Vol 458 (4) ◽

pp. 3839-3850 ◽

Cited By ~ 25

Author(s):

Qiran Xia ◽

Chao Liu ◽

Shude Mao ◽

Yingyi Song ◽

Lan Zhang ◽

...

Keyword(s):

Dark Matter ◽

Matter Density ◽

Dark Matter Density

Download Full-text

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

Astronomy and Astrophysics ◽

10.1051/0004-6361/200913240 ◽

2010 ◽

Vol 514 ◽

pp. A47 ◽

Cited By ~ 26

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

Download Full-text

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

Proceedings of the International Astronomical Union ◽

10.1017/s1743921314010904 ◽

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.

Download Full-text

A Survey on Techniques used in Medical Imaging Processing

Journal of Physics Conference Series ◽

10.1088/1742-6596/2089/1/012013 ◽

2021 ◽

Vol 2089 (1) ◽

pp. 012013

Author(s):

Priyadarshini Chatterjee ◽

Dutta Sushama Rani

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Analysis ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Network Architecture ◽

Medical Image Analysis ◽

Artificial Neural ◽

Automated Screening ◽

To Come

Abstract Automated diagnosis of diseases in the recent years have gain lots of advantages and potential. Specially automated screening of cancers has helped the clinicians over the time. Sometimes it is seen that the diagnosis of the clinicians is biased but automated detection can help them to come to a proper conclusion. Automated screening is implemented using either artificial inter connected system or convolutional inter connected system. As Artificial neural network is slow in computation, so Convolutional Neural Network has achieved lots of importance in the recent years. It is also seen that Convolutional Neural Network architecture requires a smaller number of datasets. This also provides them an edge over Artificial Neural Networks. Convolutional Neural Networks is used for both segmentation and classification. Image dissection is one of the important steps in the model used for any kind of image analysis. This paper surveys various such Convolutional Neural Networks that are used for medical image analysis.

Download Full-text

NIHAO – IV: core creation and destruction in dark matter density profiles across cosmic time

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stv2856 ◽

2016 ◽

Vol 456 (4) ◽

pp. 3542-3552 ◽

Cited By ~ 109

Author(s):

Edouard Tollet ◽

Andrea V. Macciò ◽

Aaron A. Dutton ◽

Greg S. Stinson ◽

Liang Wang ◽

...

Keyword(s):

Dark Matter ◽

Cosmic Time ◽

Matter Density ◽

Density Profiles ◽

Dark Matter Density

Download Full-text

Measuring the local dark matter density with LAMOST DR5 and Gaia DR2

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa1483 ◽

2020 ◽

Vol 495 (4) ◽

pp. 4828-4844 ◽

Cited By ~ 1

Author(s):

Rui Guo ◽

Chao Liu ◽

Shude Mao ◽

Xiang-Xiang Xue ◽

R J Long ◽

...

Keyword(s):

Dark Matter ◽

Azimuthal Angle ◽

Matter Density ◽

Dwarf Stars ◽

Dark Matter Density ◽

The North ◽

Vertical Data ◽

Data Release ◽

The Difference ◽

North And South

ABSTRACT We apply the vertical Jeans equation to the kinematics of Milky Way stars in the solar neighbourhood to measure the local dark matter density. More than 90 000 G- and K-type dwarf stars are selected from the cross-matched sample of LAMOST (Large Sky Area Multi-Object Fibre Spectroscopic Telescope) fifth data release and Gaia second data release for our analyses. The mass models applied consist of a single exponential stellar disc, a razor thin gas disc, and a constant dark matter density. We first consider the simplified vertical Jeans equation that ignores the tilt term and assumes a flat rotation curve. Under a Gaussian prior on the total stellar surface density, the local dark matter density inferred from Markov chain Monte Carlo simulations is $0.0133_{-0.0022}^{+0.0024}\ {\rm M}_{\odot }\, {\rm pc}^{-3}$. The local dark matter densities for subsamples in an azimuthal angle range of −10° < ϕ < 5° are consistent within their 1σ errors. However, the northern and southern subsamples show a large discrepancy due to plateaux in the northern and southern vertical velocity dispersion profiles. These plateaux may be the cause of the different estimates of the dark matter density between the north and south. Taking the tilt term into account has little effect on the parameter estimations and does not explain the north and south asymmetry. Taking half of the difference of σz profiles as unknown systematic errors, we then obtain consistent measurements for the northern and southern subsamples. We discuss the influence of the vertical data range, the scale height of the tracer population, the vertical distribution of stars, and the sample size on the uncertainty of the determination of the local dark matter density.

Download Full-text