publication . Preprint . 2017

Characterising dark matter haloes with computer vision

Merten, Julian; Llorens, Quim; Winther, Hans;
Open Access English
  • Published: 17 Apr 2017
Abstract
This work explores the ability of computer vision algorithms to characterise dark matter haloes formed in different models of structure formation. We produce surface mass density maps of the most massive haloes in a suite of eight numerical simulations, all based on the same initial conditions, but implementing different models of gravity. This suite includes a standard $\Lambda$CDM model, two variations of $f(R)$-gravity, two variations of Symmetron gravity and three Dvali, Gabadadze and Porrati (DGP) models. We use the publicly available WND-CHARM algorithm to extract 2919 image features from either the raw pixel intensities of the maps, or from a variety of i...
Subjects
free text keywords: Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology
Funded by
EC| WEBMAP
Project
WEBMAP
Mapping the Dark Web of the Cosmos
  • Funder: European Commission (EC)
  • Project Code: 627288
  • Funding stream: FP7 | SP3 | PEOPLE
,
EC| CosTesGrav
Project
CosTesGrav
Cosmological Tests of Gravity
  • Funder: European Commission (EC)
  • Project Code: 646702
  • Funding stream: H2020 | ERC | ERC-COG
Download from
85 references, page 1 of 6

Abraham R. G., van den Bergh S., Nair P., 2003, ApJ, 588, 218

Anderson L., et al., 2014, MNRAS, 441, 24

Barreira A., Llinares C., Bose S., Li B., 2016, J. Cosmology Astropart. Phys., 5, 001

Bartelmann M., 2010, Classical and Quantum Gravity, 27, 233001

Bartelmann M., Schneider P., 2001, Phys. Rep., 340, 291

Becker M. R., Kravtsov A. V., 2011, ApJ, 740, 25

Behroozi P. S., Wechsler R. H., Wu H.-Y., 2013, ApJ, 762, 109

Bengtsson E., Rodenacker K., 2003, Analytical Cellular Pathology, 24, 1

Betoule M., et al., 2014, A&A, 568, A22

Bishop C. M., 2006, Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA

Boylan-Kolchin M., Bullock J. S., Kaplinghat M., 2012, MNRAS, 422, 1203

Bozek B., Boylan-Kolchin M., Horiuchi S., Garrison-Kimmel S., Abazajian K., Bullock J. S., 2016, MNRAS, 459, 1489

Bradac M., et al., 2009, ApJ, 706, 1201

Caminha G. B., et al., 2016, A&A, 587, A80

Clifton T., Ferreira P. G., Padilla A., Skordis C., 2012, Phys. Rep., 513, 1

85 references, page 1 of 6
Abstract
This work explores the ability of computer vision algorithms to characterise dark matter haloes formed in different models of structure formation. We produce surface mass density maps of the most massive haloes in a suite of eight numerical simulations, all based on the same initial conditions, but implementing different models of gravity. This suite includes a standard $\Lambda$CDM model, two variations of $f(R)$-gravity, two variations of Symmetron gravity and three Dvali, Gabadadze and Porrati (DGP) models. We use the publicly available WND-CHARM algorithm to extract 2919 image features from either the raw pixel intensities of the maps, or from a variety of i...
Subjects
free text keywords: Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology
Funded by
EC| WEBMAP
Project
WEBMAP
Mapping the Dark Web of the Cosmos
  • Funder: European Commission (EC)
  • Project Code: 627288
  • Funding stream: FP7 | SP3 | PEOPLE
,
EC| CosTesGrav
Project
CosTesGrav
Cosmological Tests of Gravity
  • Funder: European Commission (EC)
  • Project Code: 646702
  • Funding stream: H2020 | ERC | ERC-COG
Download from
85 references, page 1 of 6

Abraham R. G., van den Bergh S., Nair P., 2003, ApJ, 588, 218

Anderson L., et al., 2014, MNRAS, 441, 24

Barreira A., Llinares C., Bose S., Li B., 2016, J. Cosmology Astropart. Phys., 5, 001

Bartelmann M., 2010, Classical and Quantum Gravity, 27, 233001

Bartelmann M., Schneider P., 2001, Phys. Rep., 340, 291

Becker M. R., Kravtsov A. V., 2011, ApJ, 740, 25

Behroozi P. S., Wechsler R. H., Wu H.-Y., 2013, ApJ, 762, 109

Bengtsson E., Rodenacker K., 2003, Analytical Cellular Pathology, 24, 1

Betoule M., et al., 2014, A&A, 568, A22

Bishop C. M., 2006, Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA

Boylan-Kolchin M., Bullock J. S., Kaplinghat M., 2012, MNRAS, 422, 1203

Bozek B., Boylan-Kolchin M., Horiuchi S., Garrison-Kimmel S., Abazajian K., Bullock J. S., 2016, MNRAS, 459, 1489

Bradac M., et al., 2009, ApJ, 706, 1201

Caminha G. B., et al., 2016, A&A, 587, A80

Clifton T., Ferreira P. G., Padilla A., Skordis C., 2012, Phys. Rep., 513, 1

85 references, page 1 of 6
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