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MoRIA dwarf galaxy infalling play_circle_outline

Below you see a MoRIA galaxy (Verbeke et al., 2017) simulated in a Fornax-like cluster environment with the moving box technique (Nichols et al., 2015). The galaxy orbits around the cluster and it is simulated in a box which reproduces the time-varying conditions of its environment.

Color represents the column density of the gas. The galaxy is in an orbit with 800 kpc as apocenter, 200 kpc as pericenter.

A flow of gas with varying density and temperature is coming from left, and it increases and decreases as the galaxy goes through different regions of the cluster. Density profile is taken from Paolillo et al. 2002, and the temperature is derived assuming hydrostatic condition under an NFW gravitational potential. Tidal forces are also taken into account which generate tidal feature like those forming around t=10.8.

A first pericenter passage, corresponding to a high density wind flow around t=8.9, creates a burst of star formation and feedback which in turn displaces the gas from the center of the galaxy resulting in a enhanced ram pressure stripping of the galaxy.

An infalling dwarf galaxy play_circle_outline

Here an infalling dwarf galaxy is shown in the reference frame of the co-moving box.

A simulation of a MoRIA galaxy play_circle_outline

A simulation of a MoRIA galaxy (Models of Realistic dwarfs In Action, Verbeke et al. 2015, 2017) with tidal interactions with a cluster potential. Star Formation History and trajectory are shown.

Automatic object detection

An image from the IAC Stripe 82 deep dataset, showing many stars and galaxies and a large structure of galactic cirri. Coloured regions of the image mark distinct features, which have been automatically identified by our object detection algorithm. Credit: C. Haigh (ESR1, RUG), M. Wilkinson (RUG) and N. Chamba (ESR3, IAC)

A spiral galaxy with faint tidal features

A spiral galaxy from the IAC Stripe 82 deep dataset with faint tidal features. The bright regions of the galaxy are shown in RGB scale and the background and faint regions in grey. These features are signatures of past galactic interactions and studying them provide fascinating evidence about a galaxy's history and evolution. Watch this short video to learn more about this topic. Credit: Nushkia Chamba (ESR3, IAC).



Below are listed a selection of publications in which one or more Sundial ESRs were involved, their names are shown in bold.



  1. Are ultra-diffuse galaxies Milky Way-sized?Chamba, N., Trujillo, I. and Knapen, J. H.
    In A&A, 633: L3, 2020 doi 
  2. A physically motivated definition for the size of galaxies in an era of ultra-deep imagingTrujillo, I., Chamba, N. and Knapen, J. H.
    In mnras: 226, 2020


  1. Galaxy classification: A machine learning analysis of GAMA catalogue dataNolte, A., Wang, L., Bilicki, M., Holwerda, B. and Biehl, M.
    In Neurocomputing, 342: 172-190, 2019 doi 
  2. The Fornax Deep Survey with the VST. VII. Evolution and structure of late type galaxies inside the virial radius of the Fornax clusterRaj, M.A., Iodice, E., Napolitano, N.R., Spavone, M., Su, H. -S., Peletier, R.F., Davis, T.A., Zabel, N., Hilker, M., Mieske, S., Falcon Barroso, J., Cantiello, M., van de Ven, G., Watkins, A.E., Salo, H., Schipani, P., Capaccioli, M. and Venhola, A.
    In aap, 628: A4, 2019


  1. Prototype-based analysis of GAMA galaxy catalogue dataNolte, A., Wang, L. and Biehl, M.
  2. LeMMINGs - I. The eMERLIN legacy survey of nearby galaxies. 1.5-GHz parsec-scale radio structures and coresBaldi, R. D., Williams, D. R. A., McHardy, I. M., Beswick, R. J., Argo, M. K., Dullo, B. T., Knapen, J. H., Brinks, E., Muxlow, T. W. B., Aalto, S., Alberdi, A., Bendo, G. J., Corbel, S., Evans, R., Fenech, D. M., Green, D. A., Kl"ockner, H. -R., K"ording, E., Kharb, P., Maccarone, T. J., Mart'i-Vidal, I., Mundell, C. G., Panessa, F., Peck, A. B., P'erez-Torres, M. A., Saikia, D. J., Saikia, P., Shankar, F., Spencer, R. E., Stevens, I. R., Uttley, P. and Westcott, J.
    In mnras, 476 (3): 3478-3522, 2018 doi 
  3. Globular cluster detection in the Gaia surveyMohammadi, M., Peletier, R., Schleif, F-M., Petkov, N. and Bunte, K.
    In 26th European Symposium on Artificial Neural Networks, ESANN 2018, 2018
  4. Detection of Globular Clusters in the Halo of Milky WayMohammadi, M., Petkov, N., Peletier, R., Bibiloni Serrano, P. and Bunte, K.
  5. The first sample of spectroscopically confirmed ultra-compact massive galaxies in the Kilo Degree SurveyTortora, C., Napolitano, N.R., Spavone, M., La Barbera, F., D'Ago, G., Spiniello, C., Kuijken, K.H., Roy, N., Raj, M.A., Cavuoti, S., Brescia, M., Longo, G., Pota, V., Petrillo, C.E., Radovich, M., Getman, F., Koopmans, L.V.E., Trujillo, I., Verdoes Kleijn, G., Capaccioli, M., Grado, A., Covone, G., Scognamiglio, D., Blake, C., Glazebrook, K., Joudaki, S., Lidman, C. and Wolf, C.
    In mnras, 481 (4): 4728-4752, 2018
  6. Evolution of galaxy size-stellar mass relation from the Kilo-Degree SurveyRoy, N., Napolitano, N.R., La Barbera, F., Tortora, C., Getman, F., Radovich, M., Capaccioli, M., Brescia, M., Cavuoti, S., Longo, G., Raj, M.A., Puddu, E., Covone, G., Amaro, V., Vellucci, C., Grado, A., Kuijken, K., Verdoes Kleijn, G. and Valentijn, E.
    In mnras, 480 (1): 1057-1080, 2018
  7. The Fornax Deep Survey with VST: Surface Photometry of LTGs inside the virial radius of the Fornax clusterRaj, M. A., Iodice, E. and Napolitano, N. R. doi  www 

Data and software

Photo-spectroscopic catalog of ~ 63k galaxies in the Kilo Degree Survey

This catalog includes main aperture photometry of the galaxies, main 1-Sersic PSF convolved structural parameters (effective radius, sersic index, total luminosity, PA, ellipticity etc. obtained with 2DPHOT, La Barbera et al. 2009), all common emission line strengths in GAMA and/or BOSS and the central velocity dispersion from BOSS and GAMA (click here to view the README file or download it here), for a total of 143 columns. The latter has been produced by Giuseppe D'Ago (D'Ago et al.2018 in preparation).
The catalog is available here.

Software to use for MTObjects: Faint Object Detection Method.

MTObjects is a tool for detecting sources in astronomical images, and creating segmentation maps and parameter tables. The software code is available on GitHub.


Caroline Haigh Computer Science Rijksuniversiteit Groningen

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Supervisor: Dr. M. Wilkinson
Nguyen Xuan Thanh Computer Science Chambre de commerce et d'industrie de région Paris - Île-de-France (ESIEE)

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Supervisor: Prof. H. Talbot
Nushkia Chamba Astronomy Instituto de Astrofísica de Canarias (IAC)

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Supervisor: Prof. J. Knapen
Shivangee Rathi Astronomy Universiteit Gent (UGENT)

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Supervisor: Prof. S. de Rijcke
Maria Angela Raj Astronomy Istituto Nazionale di Astrofisica (INAF)

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Supervisor: Dr. N. Napolitano
Aleke Nolte Computer Science Rijksuniversiteit Groningen (RUG)

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Supervisor: Prof. M. Biehl
Oleksandra (Alex) Razim Astronomy Università degli Studi di Napoli Federico II (UNAPLES)

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Supervisor: Prof. G. Longo
Hung-Shuo (Alan) Su Astronomy Oulun yliopisto (UOULU)

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Supervisor: Dr. E. Laurikainen
Teymoor Saifollahi Astronomy Rijksuniversiteit Groningen (RUG)

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Supervisor: Prof. R.F. Peletier
Marco Canducci Astronomy University of Birmingham (BHAM)

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Supervisor: Prof. P. Tino
Michele Mastropietro Astronomy Universiteit Gent (UGENT)

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Supervisor: Prof. S. de Rijcke
Mohammad Mohammadi Computer Science Rijksuniversiteit Groningen (RUG)

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Supervisor: K. Bunte
Bahar Bidaran Astronomy Ruprecht-Karls-Universität Heidelberg (HD)

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Supervisor: Prof. E. Grebel
Abolfazl Taghribi Computer Science University of Birmingham (BHAM)

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Supervisor: Prof. P. Tino


As part of the training programme, the SUNDIAL EU ITN organised training schools on various topics.

Applications of Computer Science Techniques in Galaxy Science

21-26 January 2018, Groningen, the Netherlands

This training school consisted of lectures and tutorials on three topics: Machine Learning, Mathematical Morphology, and Galaxy Structure. For more information on this school, click here.

Professional Software Writing and Project Management

8 and 9 June, 2018, Naples, Italy.

As part of the Grant Agreement, INAF OACN Node has organised a training school, consisting of lectures, tutorials, and talks, on topics: Professional Software Writing and Project Management. For more information, click here.

Astronomical observing school

1-6 October 2018, La Palma, Canary Islands

This school introduced the students to the various aspects of astronomical observations. It consisted of some classes, together with real observing projects on the INT in La Palma. For more information on this school, click here.

Data mining astronomical archives and the Virtual Observatory

11-14 March 2019, Groningen, The Netherlands.

This school was organised by TARGET, together with the RUG. The training consisted of several courses, on machine learning, visualisation and databases, and various skills such as Scientific Writing, Outreach and Ethics. For more information, click here.

Knowledge Clips

30-31 May 2019, Ghent, Belgium.

The participants of this workshop learned how to create a knowledge clip, covering script writing, set creation, to actual filming, editing, and publishing. For more information, click here.