Materials Map

Discover the materials research landscape. Find experts, partners, networks.

  • About
  • Privacy Policy
  • Legal Notice
  • Contact

The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

×

Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

To Graph

1.080 Topics available

To Map

977 Locations available

693.932 PEOPLE
693.932 People People

693.932 People

Show results for 693.932 people that are selected by your search filters.

←

Page 1 of 27758

→
←

Page 1 of 0

→
PeopleLocationsStatistics
Naji, M.
  • 2
  • 13
  • 3
  • 2025
Motta, Antonella
  • 8
  • 52
  • 159
  • 2025
Aletan, Dirar
  • 1
  • 1
  • 0
  • 2025
Mohamed, Tarek
  • 1
  • 7
  • 2
  • 2025
Ertürk, Emre
  • 2
  • 3
  • 0
  • 2025
Taccardi, Nicola
  • 9
  • 81
  • 75
  • 2025
Kononenko, Denys
  • 1
  • 8
  • 2
  • 2025
Petrov, R. H.Madrid
  • 46
  • 125
  • 1k
  • 2025
Alshaaer, MazenBrussels
  • 17
  • 31
  • 172
  • 2025
Bih, L.
  • 15
  • 44
  • 145
  • 2025
Casati, R.
  • 31
  • 86
  • 661
  • 2025
Muller, Hermance
  • 1
  • 11
  • 0
  • 2025
Kočí, JanPrague
  • 28
  • 34
  • 209
  • 2025
Šuljagić, Marija
  • 10
  • 33
  • 43
  • 2025
Kalteremidou, Kalliopi-ArtemiBrussels
  • 14
  • 22
  • 158
  • 2025
Azam, Siraj
  • 1
  • 3
  • 2
  • 2025
Ospanova, Alyiya
  • 1
  • 6
  • 0
  • 2025
Blanpain, Bart
  • 568
  • 653
  • 13k
  • 2025
Ali, M. A.
  • 7
  • 75
  • 187
  • 2025
Popa, V.
  • 5
  • 12
  • 45
  • 2025
Rančić, M.
  • 2
  • 13
  • 0
  • 2025
Ollier, Nadège
  • 28
  • 75
  • 239
  • 2025
Azevedo, Nuno Monteiro
  • 4
  • 8
  • 25
  • 2025
Landes, Michael
  • 1
  • 9
  • 2
  • 2025
Rignanese, Gian-Marco
  • 15
  • 98
  • 805
  • 2025

Meneghetti, Massimo

  • Google
  • 2
  • 11
  • 119

in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2024Dianoga SIDM: galaxy cluster self-interacting dark matter simulations13citations
  • 2009Combining weak and strong cluster lensing: applications to simulations and MS 2137106citations

Places of action

Chart of shared publication
Calura, Francesco
1 / 1 shared
Giocoli, Carlo
1 / 1 shared
Dolag, Klaus
1 / 5 shared
Despali, Giulia
1 / 1 shared
Moscardini, Lauro
1 / 1 shared
Ragagnin, Antonio
1 / 2 shared
Fischer, Moritz S.
1 / 2 shared
Bartelmann, M.
1 / 1 shared
Mignone, C.
1 / 1 shared
Cacciato, M.
1 / 1 shared
Merten, J.
1 / 1 shared
Chart of publication period
2024
2009

Co-Authors (by relevance)

  • Calura, Francesco
  • Giocoli, Carlo
  • Dolag, Klaus
  • Despali, Giulia
  • Moscardini, Lauro
  • Ragagnin, Antonio
  • Fischer, Moritz S.
  • Bartelmann, M.
  • Mignone, C.
  • Cacciato, M.
  • Merten, J.
OrganizationsLocationPeople

article

Combining weak and strong cluster lensing: applications to simulations and MS 2137

  • Bartelmann, M.
  • Mignone, C.
  • Cacciato, M.
  • Merten, J.
  • Meneghetti, Massimo
Abstract

Aims: While weak lensing cannot resolve cluster cores and strong lensing is almost insensitive to density profiles outside the scale radius, combinations of both effects promise to constrain density profiles of galaxy clusters well, and thus to allow testing of the CDM expectation on dark-matter halo density profiles.<BR />Methods: We develop an algorithm further that we had recently proposed for this purpose. It recovers a lensing potential optimally reproducing observations of both strong and weak-lensing effects by combining high resolution in cluster cores with the larger-scale information from weak lensing. The main extensions concern the accommodation of mild non-linearity in inner iterations, the progressive increase in resolution in outer iterations, and the introduction of a suitable regularisation term. The linearity of the method is essentially preserved.<BR />Results: We demonstrate the success of the algorithm with both idealised and realistic simulated data, showing that the simulated lensing mass distribution and its density profile are well reproduced. We then apply it to weak and strong lensing data of the cluster MS 2137 and obtain a parameter-free solution which is in good qualitative agreement with earlier parametric studies.

Topics
  • density
  • impedance spectroscopy
  • cluster
  • simulation
  • mass spectrometry