Analysis of luminosity distributions of strong lensing galaxies: subtraction of diffuse lensed signal

Strong gravitational lensing gives access to the total mass distribution of galaxies. It can unveil a great deal of information about the lenses dark matter content when combined with the study of the lenses light profile. However, gravitational lensing galaxies, by definition, appear surrounded by point-like and diffuse lensed signal that is irrelevant to the lens flux. Therefore, the observer is most often restricted to studying the innermost portions of the galaxy, where classical fitting methods show some instabilities. We aim at subtracting that lensed signal and at characterising some lenses light profile by computing their shape parameters. Our objective is to evaluate the total integrated flux in an aperture the size of the Einstein ring in order to obtain a robust estimate of the quantity of ordinary matter in each system. We are expanding the work we started in a previous paper that consisted in subtracting point-like lensed images and in independently measuring each shape parameter. We improve it by designing a subtraction of the diffuse lensed signal, based only on one simple hypothesis of symmetry. This extra step improves our study of the shape parameters and we refine it even more by upgrading our half-light radius measurement. We also calculate the impact of our specific image processing on the error bars. The diffuse lensed signal subtraction makes it possible to study a larger portion of relevant galactic flux, as the radius of the fitting region increases by on average 17\%. We retrieve new half-light radii values that are on average 11\% smaller than in our previous work, although the uncertainties overlap in most cases. This shows that not taking the diffuse lensed signal into account may lead to a significant overestimate of the half-light radius. We are also able to measure the flux within the Einstein radius and to compute secure error bars to all of our results

Projet « HRS4R – Research Data Management » : Faciliter la gestion ouverte et responsable des données de la recherche

Ce rapport concerne le projet “HRS4R - Research Data Management”, qui s'est déroulé de janvier 2021 à février 2022. Son objectif principal était de renforcer le soutien aux chercheurs en gestion responsable des données de la recherche. Soutenu par la Fédération Wallonie-Bruxelles, ce travail s’est déroulé suivant trois axes : la guidance, la sensibilisation et la formation des chercheurs. Les six universités de la FWB ont opéré en consortium pour formaliser les rôles de Research Data Officier et développer des outils et services favorisant l’application des principes FAIR et la traçabilité des données scientifiques. L’approche interuniversitaire a permis de créer un cadre structurant pour la gestion des données de recherche en FWB et d’apporter une véritable plus-value au projet, notamment en permettant à tous de profiter de l’avancement de chacun, en centralisant et en accélérant les initiatives.HRS4R - Research Data Managemen

Joigning forces: building a community of data ambassadors across universities in Brussels-wallonia federation (Belgium)

Over the last decades, research has become more digital, collaborative and open. Research openness has extended from articles to other research outputs, including datasets. Meanwhile, datasets are growing bigger and are requiring more financial and energy investments. In addition, the reproducibility crisis has highlighted the need for a cultural change in research data management, which most funding agencies are nudging by requiring compliance to the FAIR data principles. Institutions can help researchers address this new pressure through support from cross-disciplinary staff and librarians, and by implementing general data management tools. This basic and necessary initiative from universities most often translates into awareness campaigns and general training sessions that often end up overbooked, highlighting the growing interest of researchers in these issues. However, this central approach lacks the discipline-specific expertise needed to properly translate the general recommendations into actionable items. With most institutions being decentralised across many campuses, logic pushes towards relying on local relays. Inspired by their peers from Cambridge and TUDelft, the six universities in the Brussels-Wallonia Federation (FWB, Belgium) have launched, as a consortium, a community of Data Ambassadors (DAs). Sometimes called Data Champions, DAs are researchers acting as local experts who bring awareness in their immediate work environment (department, research unit…) towards data management best practices. DA networks have the advantage of automatically addressing another weakness of central support, that is, the lack of resources, by capitalizing on existing workforce with the relevant expertise. While lean in terms of spending, this approach does however point towards the issues of resources availability for such services. On top of these benefits, DA networks enable peer-to-peer support, which has been shown to be a much more efficient drive towards change than top-down initiatives. The FWB consortium hopes to strengthen links between research groups across universities and disciplines. The aim is to empower individual researchers and engage whole communities to be better data managers, instead of trying to command change from a hierarchic point of view. Launched in December 2021, the DA community has already received massive interest with over 60 members enrolled over the first couple of weeks. Although inspired by other institutions, this network presents the specificity of being an entirely bottom-up initiative, without any other existing catalysing interuniversity structure, thus facing the challenge of building its own tools from the ground up. The following paper introduces the launching process - based upon the TUDelft model, and it reports on the overall experience from pioneering DAs, including their achievements and difficulties. The vision for settling and expanding the network and its expected successes and challenges are presented