2020 ESAC TRAINEES

Patricio Yael Reller
Technische Hochschule Ulm (THU)  

Automatic identification of astronomical objects in HST images with deep learning
Tutors: Bruno Merín, Héctor Cánovas, Javier Durán, Sandor Kruk

Space Science is indeed a paradigm case for Big Data science. The continuing development of ground and space-based observatories, including large sky surveys, is bringing Astronomy to the Big Data era. Gaia or Euclid are examples in space but new ground-based projects, like LSST or SKA, will need the new tools even more. Means and methodologies to do research with these facilities will, no doubt, be needed. In contrast to the techniques that have been applied so far in space science, one of the applications of deep learning for the analysis of astronomical data will be to exploit all the information present in the pixel data of the heterogeneous multi-wavelength set of images available in the ESA archives.

So far, all searches of the ESA science data archives and all other astronomical archives are based on metadata from the observations (target, sky coordinates, date of observation, instrument, filter, proposer, etc.), while there is an immense amount of information content in the images that has not yet been fully exploited systematically in a data-intensive manner. As part of the ESAC Machine Learning Group, we present an inference-first approach with neural networks for processing, identifying and classifying astronomical objects in complete datasets of FITS images (paying special attention to modularity and scalability in preparation for potential high-performance computing clusters), with the objective of simplifying the task of analyzing large datasets with different classification models.

This work will enable users to do data-driven searches (e.g. show me all the optically resolved galaxies with an X-ray point source counterpart of hardness ratio X and with a Far-infrared Herschel observation available or show me all the co-moving Gaia sources in the star-forming region Y with clean near-IR background and no PSF blending issues) in the future via the ESASky (https://sky.esa.int) portal, and could potentially be integrated into ESA Datalabs (https://datalabs.esa.int).

As an initial Proof of Concept, a current state-of-the-art neural network model was applied to a region of the 2MASS and Skymapper optical image datasets, with the intention of generating a pure Machine/Deep Learning catalog. This trial will later be expanded into classifying the full datasets, using ESAC’s GPU grids, Docker and Kubernetes.

Ditlev Frickmann
University 

Detecting galaxy clusters in simulation data in preparation for the Euclid mission
Tutors: Lyndsay Old, Bruno Altieri, Ivan Valtchanov, Nuria Alvarez

 

Julien Demange-Chryst
University:ISAE-SUPAERO

Astrometric Residuals Analysis
Tutors: Jose Hernandez, Alex Bombrun

The main goal of the Gaia mission is to make the largest, most precise three-dimensional map of our Galaxy by surveying an unprecedented one per cent of the galaxy's population of 100 billion stars.The Gaia measurement principle is that differences in the transit time between stars observed by each telescope can be translated into angular measurements. All these measurements are affected if the basic angle, the angle between both telescopes, is variable. Ideally, the basic angle has to be stable but it is not the case indeed because of temperature variations for example. So it is crucial to control its variations. 

The theoretical value of the basic angle is Γ = 106.5° and the “Basic angle monitor” (BAM) is continuously measuring its the variations. The project deals with the data from this measuring tool. Before the project, bad data gaps were tuned manually using astrometric residuals, BAM data and AGIS calibration. It was thus necessary to look over the whole data to place those gaps. This process was not very convenient and it took a significant time. Then, the main goal of the project was to develop machine learning algorithms to automate break points and bad data gaps placement in order to make the process faster. 
In order to train the models, I used BAM data from the beginning of the mission, and during the project, new data have been released so I was able to use them to test the algorithms. 

It was a wonderful experience to have the opportunity to perform an traineeship at ESAC and I really enjoyed it.

Charles Cloutier
University of 

CubeSat: Attitude Determination and Control System
Tutors: Xavier Dupac, Julio Gallegos, Fernando Martín-Porqueras, Marcos López-Caniego

 

 

Hector Rueda
University of 

Super-resolution and de-noising of XMM-Newton images using machine learning techniques
Tutors: Eva Verdugo, Ivan Valtchanov, Pedro Rodriguez, Maggie Lieu, Antonia Vojtekova

 

Lucia Haerer
Friedrich-Alexander University of Erlangen-Nürnberg

Understanding the X-ray variability of Black Holes
Tutors: Michael Parker, Gabriele Matzeu, Nuria Alvarez, Felix Fuerst, Andrew Lobban, Willam Alston

X-ray emission from active galactic nuclei (AGN) is known to vary on various timescales. Studying this variability gives unique insights into the behaviour of AGN and accretion physics in general. Excess variance spectra are a promising tool to quantify the variability in an objects light curve, by calculating its variance above the expected noise level. 

An example of the diagnostic power of excess variance spectra is the study of ultra-fast outflows, fast (~0.1-0.3c) accretion disc winds which are characterized by a wide opening angle and large column densities, thereby possessing huge mechanical power. These outflows produce characteristic spikes in the excess variance spectrum, which can be used to probe their physical properties and structure, providing insights that cannot be gained from static count spectra. A major limitation to such studies has been the lack of generally applicable models to fit excess variance spectra, analogous to those available for count spectra. 

In my Trainee project, I extended an excess variance model for ultra-fast outflows, by accounting for the broadening of their characteristic spikes observed in the luminous, low-redshift quasar PDS 456. I was able to confirm previous results with the new model and found that two effect contribute to the broadening of the spikes: Doppler broadening due to the intrinsic velocity of the gas and a trend between outflow velocity and X-ray luminosity. 

Luana Michela Modafferi
Università di Pisa

The historic X-ray light curve of the blazars Mrk421 and PKS2155-304 on the 20th anniversary of XMM-Newton

Tutors: Nuria Álvarez Crespo, Ignacio de La Calle

This project focused on the temporal and spectral analysis of the blazar Mrk421, used as calibration source by the X-ray space observatory XMM-Newton, since it is one of the brightest sources at X-ray energies. With the light curves and the spectra one can analyse variability and spectral properties in different states. The code was developed so that the analysis can be repeated for other XMM-Newton sources as well and therefore allows the comparison between results of different objects. 

The project was carried out remotely, due to the outbreak of Covid-19. However, my supervisors held weekly meetings and guided me throughout the entire process. I also really enjoyed communicating with the other members of the XMM-Newton team, who were always there to support me and advise me on my academic career.

Mireia Leon
Delft University of Technology (TU Delft)

Integrated mapping of geological features on comet 67P/Churyumov-Gerasimenko as observed by the Rosetta mission
Tutors: Michael Kueppers, Sebastien Besse

From July to December 2020 I have worked as an intern at ESAC on the geological mapping of comet 67P/Churyumov-Gerasimenko.

The data from Rosetta have encouraged numerous studies of the comet leading to reconstruct its shape and identify the main features forming the surface. The peculiar form of the comet has made it challenging to project these geological features on an unambiguous frame. As a result, the geological maps published to date are created on top of comet images, making them dependent on the viewing angle and image resolution. During this period, I have worked on the integration of the geological landmarks published in previous studies in a single framework and the identification of new features using the images from the OSIRIS instrument onboard Rosetta. For this process, the Small Body Mapping Tool, a three-dimensional mapping software has been used, which supposes a great improvement with respect to two-dimensional mapping.

In collaboration with Björn Grieger, developer of a new projection to map the comet, it was possible to project the features with unambiguous coordinates. This technique, together with the extensive mapping performed lead to the writing of a paper on the topic, which is currently under review.

My experience at ESAC has been marked by the COVID-19 pandemic, which forced to conduct the first half of the internship remotely. On the bright side, this allowed to collaborate with colleagues located across Europe and participate in online conferences. With great help of my supervisors, I was able to go to ESAC for the second half of the internship and work with them in person. I am very grateful for this opportunity, not only because it allowed to work in a more efficient way, but also because it let me discover life at ESAC which was an eye-opening experience.  Although under special circumstances, I felt part of the community and I had the chance to meet fellow researchers and participate in very interesting scientific discussions. I am very grateful to ESA for the possibility offered to contribute to the scientific research of such an amazing mission.

Karol Fulat
University of 

Characterising Suprathermal Electrons at Interplanetary Shocks
Tutors: Georgina Graham, Andrew Walsh, Yannis Zouganelis

 

Cristina Madurga
Complutense University of Madrid

Python modules for SunPy and HelioPy to access Solar Orbiter Archive data
Tutors: Pedro Osuna, Andrew Walsh

Understanding of the Solar Orbiter mission and some of its instruments has been acquired, by reading the relevant information and working with the Solar Orbiter archive and the data corresponding to the MAG and SWA instruments.

Study and analysis of Magnetic field of the Sun. Creation of a Python script that allows downloading data directly from the Solar Orbiter archive (via its machine access interface and Heliopy Solar Orbiter module) and comparing the theoretical angle predicted by the Parker spiral with the real angle measured by the MAG instrument onboard Solar Orbiter, together with measurements of the Solar Wind velocity obtained by the SWA instrument.

Practical knowledge of the Python programming language has been obtained. Familiarity has been gained with the CDF format, the main format used in most missions dealing with Heliospheric data.

It has been an extraordinary opportunity to develop and grow. The professional and high scientific level has allowed me to learn tremendously, and it has challenged me to improve. This experience has made me reassure my passion about Space Physics and has given me the courage and desire to continue educating myself in this amazing world.

The personal relationship with my supervisors has been very friendly and satisfactory, and they have always been willing to help and teach me, even in this hard situation due to the pandemic, when all the traineeship has been done online.

Aurelie Bracco
University of 

A Science Logbook for the BepiColombo Quick-Look Analysis
Tutors: Santa Martinez, Mark Bentley, Thomas Cornet, Alan Macfarlane

 

Davide Viero
University of 

Cross-match operations on large astronomical catalogues on Spark and AXS
Tutors: Bruno Altieri, Sara Nieto, Pilar de Teodoro

 

Javier Ballester
University of 

Centralized acces system of PA metrics
Tutors: Maria Garcia Reinaldos, Luis Martin, Jose Marcos, Julio Gallegos

 

Paul Vénard
University of 

Conference Mobile Application Development
Tutors: Rocio Guerra, Ruben Alvarez, Miguel Doctor

 

Salvatore Vicinanza
Delft University of Technology (TU Delft)

Exploiting GNSS data from space users for upper atmospheric (Aeronomy) analysis
Tutors: Vicente Navarro

From May 2020 to October 2020, I worked as an engineer intern at the European Space Astronomy Centre (ESAC) on a project called “Exploiting GNSS data from space users for upper atmospheric (Aeronomy) analysis”. 

Upper atmospheric studies are of key importance to enhance scientific knowledge about the physics of the Earth, to learn how the Earth’s atmosphere interacts with the Sun and space weather, but also to characterise the effects of the ionosphere on satellite signal propagation. Research in this field offers tremendous scientific benefit and significant societal impact. To this end, I am honoured to have contributed to the on-going and pioneering Office's research on the use of GNSS measurements for the characterization of the ionosphere, paving the way for future activities in this area. 

During my internship project at ESAC, I was specifically responsible for the development and verification of an innovative methodology to exploit GNSS observations collected by LEO-based receivers to model the topside ionosphere, and I studied how to complement these space-borne products with traditional ground-based ionospheric maps. To do so, I analysed microwave signals collected by GOCE and SWARM satellites to characterise the upper polar atmosphere and I examined, together with my supervisors, the feasibility of including datasets from other ESA EO missions (e.g. Sentinels) in the optics of creating continuous and world-wide ionospheric estimations from space. 
Due to the COVID-19 pandemic situation at the time, the set-up of my entire internship experience has been remote-working. However, despite the impossibility to reach the ESAC’s site, I can sincerely and confidently state to have had a unique and eye-opening experience; this also thanks to the invaluable work of the Galileo Science Office and ESAC, which promptly engineered solutions to continue working and innovating in the unexpected situation. 

Working closely with experts and sharing ideas with them during my internship allowed me to build knowledge about the practical working life and gain meaningful experience in space research, which I would immensely benefit from in my future career. Through my experience at ESA, I had the privilege to collaborate with specialists in space science and engineering and operated within an international community with highly motivated people always striving to push the boundaries of what is possible. This allowed me to grow both from a professional and personal point of view, and taught me how to successfully contribute and innovate in this sector. For these reasons, I am extremely grateful to ESA for the possibility given and honoured to have participated in this ground-breaking project. 
 

 

GALILEO NAVIGATION SCIENCE OFFICE

1. CesaR Science Education

2. Exploiting GNSS data from space users for upper atmospheric (Aeronomy) analysis

3. CesaR ESAC Solar Observatory Master Control Programme (NEW)

4. CesaR ESAC Optical Observatory setup and configuration (NEW)

5. CesaR Educational Software Development (NEW)