2019 ESAC Trainee Projects

Below are the projects being offered in 2019

1. Characterizing red stars in the Galactic Plane with Gaia, 2MASS, and GALANTE

ESAC supervisor(s): Jesús Maíz Apellániz , Pedro García Lario

The advent of large-scale photometric and astrometric surveys has opened the way to the study of the Galactic stellar population beyond 1 kpc, where photometry is dominated by the effect of extinction instead of the intrinsic spectral energy distribution of the stars, making all stars “red". At large distances the majority of the observed population in those surveys is composed of bright red giants but among them we also find obscured OB and AGB stars, among other interesting objects. Finding those needles in the haystack is the purpose of this traineeship. The trainee will combine the recent Gaia DR2 photometry and astrometry with ground-based photometry in the NIR (2MASS) and optical (GALANTE) to characterize the obscured population of the Galactic Plane, identify the different types of objects, and measure their extinction. This work is suitable to be considered as a research master project.

Duration: 6 months.

Desirable expertise:

  • knowledge of Python and/or IDL,
  • use of astronomical databases and associated tools (Aladin, Topcat),
  • previous experience with photometry is a plus.

2. Support for the CESAR Radio Telescope and Related Tools

ESAC supervisor(s): Michel G. Breitfellner, Manuel Castillo, Javier Ventura

CESAR (Cooperation through Education in Science and Astronomy Research) is a joint educational programme developed by the European Space Agency (ESA), the Spanish National Institute for Aerospace Technology (INTA) and INTA-owned company Isdefe. Its objective is to provide students from European secondary schools and universities with hands-on experience in Optical and Radio Astronomy. In addition, CESAR shall contribute with outreach activities to promote Space Science and to stimulate European students' interest in Science and Technology in general and Astronomy in particular.

The CESAR programme owns several telescopes and observatories which need improvements and upgrades. One of these observatories is the CESAR ESAC Radio Telescope (CERT). It is the result of the refurbishment as astronomical facility of the ESTRACK antennas located at ESAC, Spain. INTA contributes to CESAR sharing the use of a satellite tracking antenna (VIL-2) located in ESAC for education and science activities.

CERT consists of a Cassegrain antenna of 15m diameter, the added RF subsystems working in S-band and the operative control subsystems. The existing equipment together with a power meter and a Spectrum analyzer allow to perform radiometric measurement of signals emitted by natural radio sources. At present, after several research and development activities, it has been proven that several radio sources can be detected, measured and studied with CERT to show different radio emission mechanisms and its relation with different astronomical phenomena.

The project objective is to contribute to the CERT fine tuning for its scientific and educational operation in close collaboration with the CESAR/CERT team. His/her support will be required for the following activities:

  • Participation in CERT observation and calibration campaigns;
  • Carry out own and assigned Radio Astronomy observation plans;
  • Evaluate and improve the CERT data analysis and processing tools; • Evaluate the existing HW/SW identifying possible improvements or alternatives;
  • Write new and update existing CERT documentation;
  • Preparation of Radio Astronomy class room resources for both teachers and students.

The final goal of the project is to enable CERT for its systematic scientific and educational use.

Project duration: 6 months

Desirable expertise:

  • Good background in Astronomy and Astrophysics is required;
  • Understanding of Radio Frequency Fundamentals and related disciplines;
  • Practical software programming skills including any modern programming language including GUI programming skills;
  • Practical experience in observing with amateur telescopes would be an asset.

3. Software development for space science education initiative

ESAC supervisor(s): David Cabezas, Michel G. Breitfellner, Javier Ventura-Traveset

CesaR (Cooperation through Education in Science and Astronomy Research) is a joint educational programme developed by the European Space Agency (ESA), the Spanish National Institute for Aerospace Technology (INTA) and INTA-owned company Isdefe. Its objective is to provide students from European secondary schools and universities with hands-on experience in Optical and Radio Astronomy. In addition, CESAR shall contribute with outreach activities to promote Space Science and to stimulate European students' interest in Science and Technology in general and Astronomy in particular.

The CesaR initiative has its own observing facilities which allow to observe the universe actively on-line through a dedicated Control Centre hosted at ESA’s European Space Astronomy Centre (ESAC) in Madrid, Spain.

Traineeship opportunity:

ESA is offering a 6 months traineeship opportunity within the CesaR education initiative. The main tasks to be carried out by the trainee are the following:

  • Create a Web Archive Viewer for the images produced by the CesaR observatories.
  • Software integration and interconnection of subsystems.
  • Improve the interactive scientific cases tools for schools and universities.
  • Help creating new software for education using the latest technologies (mobile apps, vr glasses, 3D interfaces).

The final result of the project should be a set of software, apps and/or database applications with new and/or improved functionality.

Project duration: 6 months

Desirable expertise or programming language:

  • Programming experience in PHP, HTML, CSS, XML Javascript, HTML5
  • Experience in MySQL database Others:
  • Interest in Planetary Science, Space Science
  • Interest on 3D software design with engines like Unity
  • Linux knowledge as user
  • Hobbyist in DIY hardware and programming skills, like electronic, robotics, 3D print, VR, AR, Mobile app development

4. Social media and communication with emphasis on outreach and education

ESAC supervisor(s): Emmet Fletcher, Beatriz Arias, Arantxa Alonso, David Cabezas, Michel G. Breitfellner

ESAC Communication Office is responsible for official ESA communication activities in Spain and Portugal, organisation of launch and major events with national institutions (Museums, Planetariums, etc); all the Spanish and Portuguese media activities related to space; exhibitions; run and coordinate the Spanish and Portuguese ESA websites, social media activities, Intranet, as well as coordination of VIP visits to ESAC.

CesaR (Cooperation through Education in Science and Astronomy Research) is a joint educational programme developed by the European Space Agency (ESA), the Spanish National Institute for Aerospace Technology (INTA) and INTA-owned company Isdefe. Its objective is to provide students from European secondary schools and universities with hands-on experience in Optical and Radio Astronomy. In addition, CESAR shall contribute with outreach activities to promote Space Science and to stimulate European students' interest in Science and Technology in general and Astronomy in particular. It is based at ESA’s European Space Astronomy Centre (ESAC) in Villanueva de la Cañada, Madrid, Spain.

ESA is offering a 6 months traineeship opportunity at ESAC. The main tasks to be supported are:

  • Audio visual support (recording videos and/or taking pictures of events and subsequent editing)
  • Support in the organisation of events (press conferences, institutional events, VIP visits, social media events, CesaR workshops, videoconferencing, …)
  • Support with the office inventory and ESA Communications Office monthly report
  • Support with the design and implementation of strategy for social media for CesaR
  • Manage and edit CesaR news for the web and other media
  • Design and create multimedia contents for the CesaR web pages
  • Help with the daily interaction related to CesaR’s Space Science Experience (SSE) programme with teachers and schools
  • Carry out web and social network analysis and SEO monitoring

Project duration: 6 months

Desirable expertise or programming language:

  • Knowledge of graphic design
  • Good level of editing pictures and videos
  • Social Media knowledge
  • Organization skills (For events, visits)
  • Data base knowledge
  • Good communication skills
  • Good level of English, spoken and written, is required
  • Good level of Spanish, spoken and written, is required
  • Basic HTML knowledge Assets:
  • Interest in Planetary Science, Space Science and Communications
  • General multimedia knowledge

5. Cross Visualization ExoMars16 CASSiS and Mars Express HRSC Footprints

ESAC supervisor(s): Isa Barbarisi, Jaime Saiz, Tanya Lim, Emmanuel Grotheer, Bruno Merin

Within the Data and Engineering Division, the ESAC Science Data Centre (ESDC) is responsible for developing and operating the on-line science archives of all ESA space science missions (astronomy, planetary and solar heliophysics), in close cooperation with the Science Operations Centres at ESAC, the instrument teams and the scientific community. In particular, the Planetary Science Archive hosts all ESA planetary mission science data holdings and represent a unique platform for planetary science data exploitation.

The Trainee will be fully integrated into a small team of software engineers and planetary scientists at ESAC and will participate to the development of a prototype designed within the PSA (using the Agile/Scrum software development methodology), in particular in the context of Exomars 2016 CASSiS Footprints Visualization within a GIS Map Browser.

Duration: 6 months

Specific Tasks and Objectives: The student will work within the already existing infrastructure set in place in the ESAC Science Data Centre (Planetary department) in collaboration with Engineers and Scientists.

The goal of this work-package is to visualize both Mars Express footprints coming from HRSC instrument and ExoMars16 footprints from CASSiS instrument in a Map Browser Interface.

The student will be provided with the following inputs:

  • EM16 Cassis PDS4 Products containing geometry
  • MEX HRSC Datasets containing multiple products with well-established geometry
  • Scientific Use Case
  • User Interface (Map Browser) Prototype within Vaadin Framework

The student will deliver the following output:

  • PDS4 Geometry Parser library in Java
  • Definition and Implementation of a Data Model able to accommodate PDS4 geometry data and metadata
  • Geoserver Layer able to access geometry data and metadata
  • Set of Java classes able to display PDS4 Geometry in a GIS Map
  • (Time allowing) Set of recommendations to PDS4 geometry working group for optimal display of PDS4 geometry in the PSA Map Browser

Output and Performance Monitoring:

The student will get experience with PDS3 and PDS4 standard, GIS standard, PDS3 and PDS4 geometry definition, Vaadin and OpenLayer. He will implement all the workflow, from ingestion to visualization of CASSiS products according to the PSA GIS architecture.

The output shall be measured and assessed on the basis of:

  • The trainee will gain knowledge of various common data formats in planetary science and how/when to use each of them.
  • The trainees will gain knowledge of GIS tools and standards
  • The trainee will gain knowledge of PDS4 Standard and Geometry working group.
  • The trainee will learn how to research a problem in the domain of scientific computing, and implement their solution within an existing framework.
  • The trainee will gain experience of the challenges associated with long term archiving of scientific data.
  • The trainee will gain experience of day-to-day work in, and the culture of an International Organization.
  • The trainee will learn about science operations and the variety of activities undertaken at ESAC.

Interactions:

  • Daily Interaction with ESDC Engineers and Scientists within the framework of the Scrum methodology
  • Weekly progress meetings with mentors and other scientists and engineers at ESAC. More informal discussions as required.
  • Final assessment of the trainee’s work.

Specific Qualification Requirements:

  • Applicants should be in their final years of a University course at Masters Level (or equivalent) in computer science or planetary science.
  • Experience with Java programming languages is required. Knowledge of GIS, WebGL and PostgreSQL would be an asset.
  • Applicants should have good interpersonal and communication skills and should be able to work in a multi-cultural environment, both independently and as part of a team.
  • Applicants should be interested in planetary science, be open and curious minded. A good proficiency in English is required.

6. Allowing users to google for ESA Space Science Datasets

ESAC supervisor(s): Jesús Salgado, Beatriz Martínez, Isa Barbarisi and Bruno Merín

Google has recently released a “Dataset Search” interface in beta to allow world-wide users to search for and discover public datasets for scientific or other interests (see search interface at https://toolbox.google.com/datasetsearch and an article about the interface in https://www.nature.com/articles/d41586-018-06201-x). While the future scientific use of this interface is to be determined, it is undeniable that a potentially large fraction of the world population might be using this interface to search for scientific data in the next couple of years.

At the present time, the datasets from all of ESA’s Astronomy, Planetary and Heliophysics extremely rich datasets cannot be found through this interface because they have not yet been indexed with the schema.org community-supported indexing schema. The work of the trainee consists in re-parsing the metadata tables from the ~ 20 ESA’s space data science repositories (see the list in https://cosmos.esa.int/web/esdc) from their original data model schemas into schema.org, publish those schemas online and get google to index them such that they can be found in Google’s Dataset Search engine. As an extra, the trainee could work together with the ESDC staff to enable automatic updates of newly upcoming data to the archives to the same system to keep it always up to date.

The trainee will work in the ESAC Science Data Center, supported by a team of very experienced software engineers and scientists, all specialists in ESA's datasets and will apply agile/scrum development methodology.

Project Duration: 6 months

Desirable expertise or programming language :

  • The candidate should have proven experience in software development and management of data with XML and JSON formats.
  • Having knowledge on Semantic Web, standards, W3C, Social Web, technical research, RDF, schema.org, GitHub, FOAF, Google, Microdata or RDFa would be assets.

7. Optimizing two-point correlation statistics using Machine Learning techniques

ESAC supervisor(s): Ginevra Favole, Bruno Altieri, Roland Vavrek, Antonio D. Montero-Dorta (São Paulo University)

 

New-generation spectroscopic surveys as SDSS-IV/eBOSS, DESI, Euclid, 4MOST, WFIRST and Subaru PFS will target emission line galaxies (ELGs) out to redshift z~2 over enormous cosmological volumes. The clustering signal of these objects will be used as fundamental cosmological probe to trace the baryon acoustic oscillation feature, measure the growth rate of structure, study the distribution of matter and the star formation history of the Universe, and to unveil the nature of dark energy. In parallel, high-quality, multi-color imaging programmes as DECaLS, DES and Subaru HSC are providing estimates of galaxy shapes and photometric redshifts with unprecedented accuracy. These observations will critically improve our current understanding of the complex mechanisms that regulate the formation and evolution of galaxies. Therefore, understanding how to best measure and model the galaxy clustering properties of high-redshift emitters is a fundamental task we have to address now using the computing techniques currently available.

The main goal of this project is to build and deliver to the scientific community a software script optimized to compute two-point correlation functions (2PCFs), either from large observational data sets or large-volume highresolutioncosmological simulations. We will measure the 2PCFs of different SDSS-III/BOSS galaxy samples, selecting them by color, luminosity and morphology. In order to group galaxy populations with similar properties, we intend to use the scikit-learn clustering algorithm. We will interpret our results using the latest products of MultiDark-Galaxies, currently the cosmological simulation with best resolution/volume available run with different semi-analytic models of galaxy formation. The computational outcomes of this project will be of major interest for the new-generation of cosmological surveys, first of all the Euclid ESA mission.

Project duration: 6 months

Benefits for the Trainee: involvement in one of the most challenging cosmological mission ever built and experience in data analysis using the latest Big Data and Machine Learning techniques applied to astrophysics.

Desirable expertise or programming language:

  • Good communication
  • Python and scikit-learn
  • C++ and/or Fortran95

8. Building Information Management (BIM) tool

ESAC supervisor(s): Panos Machairas

The EFM Service at ESAC is developing a new method for the construction and follow up management of the ESA infrastructure. The method is based on the creation of three-dimensional object-based building models that contain all the information needed for the construction and the consequent Life Cycle Management of the asset.

The aim of the present Trainee Project, is to put in operation and test in practice the work performed in the course of a previous young graduate trainee project, during which the complete model of a building was created; it is moreover our aim to extend this work to additional buildings at ESAC.

The selected candidate has to be able to work autonomously in a multi-faceted development and application project, particularly in the coordination, application and integration phases.

Project duration: 6 months

Desirable expertise:

  • Advanced skills in drawing with Revit
  • Good knowledge of the Archibus FM software

Knowledge and experience in the following fields is valuable:

  • Experience with building management systems
  • Some experience with database administration.

9. Gaia, a micrometeoroid hit detector?

ESAC supervisor(s): Cian Crowley, Alex Bombrun, Juanma Fleitas, Nicolas Altobelli, Uwe Lammers

Description: ESAC is responsible for developing and running AGIS, the software that computes the astrometric parameters for the Gaia mission. As a side product of our data reduction, we identify features in our scanning rate data that originate from major micrometeoroid hits on the spacecraft. It turns out that Gaia seems to be a promising micrometeoroid hit detector. In order to better understand the limits on Gaia’s micrometeoroid hit detection we are looking for a candidate to improve our open source hit simulator (see https://github.com/bombrun/GaiaLab), as well as to continue the analysis of the real Gaia data. The results of this work will be then compared to the distribution of detected hits from simulations that are based on an up-to-date model of the solar system.

Project duration: 3 to 6 months

Desirable expertise or programming language:

  • Good communication skills
  • Python
  • Basic mathematics and statistics

Benefits for the trainee : a few months of immersion in one of the most challenging astronomical mission and an experience in model prototyping and data analysis in the Python environment

10. GaiaLab : simulation of a global astrometric solution

ESAC supervisor(s): Alex Bombrun, Jose Hernandez, Alfonso De Torres

ESAC is responsible for developing and running AGIS, the software that computes the astrometric parameters for the Gaia mission. The design and validation of Gaia global astrometric solution requires to be able to run simulations that include complex calibration issues. The current state of the art is AgisLab. This code is proprietary of DPAC, the scientific consortium processing the Gaia data and responsible for the publication of the final star catalogue. This code requires complex configuration and is very tied to Gaia data model.

Last year we have started the GaiaLab open source project, a simplified version of AgisLab in Python available on https://github.com/bombrun/GaiaLab. It is developed by students for students. The first part of the project has been carried on to simulate observations with a nominal scanning law and one has started to investigate some parts of the underlying least square problem. In order to simulate a global astrometric solution further developments are needed.

The ideal candidate should demonstrate basic knowledge of mathematics (linear algebra, polynomial, trigonometry) and more important the motivation to promote an open source project around global astrometry. The trainee will receive support regarding astrometry and Python.

Project duration: 3 to 6 months

Desirable expertise or programming language:

  • Good communication
  • Python
  • Basic mathematics (linear algebra, polynomial, trigonometry)

Benefits for the trainee: a few months of immersion in one of the most challenging astronomical mission and an experience in model prototyping and data analysis in the Python environment.

11. Fast Outflows in Protoplanetary Nebulae and young Planetary Nebula observed by Herschel/HIFI

ESAC supervisor(s): Pedro Garcia-Lario, David Teyssier, Valentin Bujarrabal

The HIFI instrument (Heterodyne Instrument for the Far-Infrared) was the high-resolution spectrometer on-board Herschel. After almost 4 years of activity in orbit, a unique scientific data-base has been collected, offering a mine of information for scientific investigation. Among the unique data obtained by this instrument were a set of observations in mid- to high-excitation transitions of 12CO in the envelopes of Protoplanetary Nebulae (PPNe) and young Planetary Nebula (PNe). A sub-set of those data was analysed and published by Bujarrabal et al. 2012, and was later augmented by companion project (Danilovich et al. 2015) allowing to collect data from various transitions ranging from J=1-0 to J=14-13, evidencing a large variety of velocity profile and of physicochemical conditions at play in the environments of those objets.

The goal of this project is to study the physical conditions in the fast outflows observed in some of those evolved stars by means of the comparison between the line intensity observed at different locations of the envelopes and winds, and in different transitions of the 12CO line. In addition, observations obtained in SiO in some of the targets will be used in order to probe different temperature and density conditions. The project involves analysis of high-resolution spectra obtained in about a dozen of sources, and comparison with simple model of line excitations in order to derive parameters such as density and temperatures. If time allows, modelling using more sophisticated tools (e.g. SHAPEMOL, Santander-Garcia et al. 2015) could be considered in selected targets.

References:

Bujarrabal et al. 2012, A&A 537, A8, https://arxiv.org/pdf/1109.6145.pdf

Danilovitch et al. 2015, A&A 581, A60, https://arxiv.org/pdf/1506.09065.pdf

Santander-Garcia et al. 2015, A&A 573, A56, https://arxiv.org/pdf/1410.6691.pdf

Project duration: 6 months

Desirable expertise or programming language:

  • Basic knowledge of of Unix/Linux OS.
  • The analysis can be made in the GILDAS/Class software, but other analysis environment such as Python are perfectly suited to the project.

12. ESA Human Resources – Procedure for Newcomers

ESAC supervisor(s): Javier Delgado

ESAC constantly receives new staff (Young Graduate Trainees, Research Fellows and permanent staff). Their arrival is known some months in advance and some procedures need to be implemented before their arrival.

The project would consist in giving administrative support to establish a formal procedure before the arrival of the newcomers. This work would be done together with the Facility Management team and the Division Assistants. The post-holder will have to understand ESA’s structure, attend to the internal meetings and prepare a document to be distributed to the people involved.

Duration: 6 months

Desirable expertise:

  • Fluency in Spanish & English, perfect writing & redacting skills
  • Good communication skills
  • Knowledge of MS Office application
  • Independent worker
  • High level of proactivity
  • The trainee shall be studying or have graduated from law, economics, business management

13. Multi-purpose Geometric Light curve model

ESAC supervisor(s): Jan-Uwe Ness

From large distances, small geometric structures such as binary stars cannot be resolved directly, but under certain circumstances such as viewing from the side, the evolution of the total brightness allows reconstruction of the geometric configuration from eclipses, shadowing, reflection etc. Light curves (brightness evolution with time) can be simulated assuming certain geometric configurations. Good agreement with an observed light curve can be interpreted as the assumed configuration to correspond to the unresolved system configuration.

Publicly available light curve models are limited to special situations without being flexible enough to be expanded to address new questions. The objective of this project is to develop a public multi-purpose light curve simulation tool. While such a tool will always be limited in scope, a modular design allows newly arising questions to be addressed by adding new modules.

The final tool should be documented for both, users and developers, such that it can be further expanded by others (e.g. future trainees).

Project Duration: 6 months

Desirable expertise or programming language:

  • Any high-level computer language,
  • Python preferred; possibly building on existing code in early development stage
  • Linux at user level
  • Experience with building code/documentation in git
  • Basic astrophysical background or interest desirable

14. Rosetta data tutorials using Jupyter Notebooks

ESAC supervisor(s): Mark S. Bentley, Dave Heather

The successful Rosetta mission generated a wealth of data from a variety of instruments onboard the orbiter and lander – from cameras to spectrometers, microscopes to magnetometers. Many of the instruments teams have recently been working on delivering enhanced data products to the Planetary Science Archive. As such there is now a treasure trove of data ready for analysis, but the barrier to getting started with this analysis is often high. This project proposes that a trainee pick one or more Rosetta instruments and digs into their data format to understand and, if possible, try to reproduce the results of a publishes paper. The result would ideally be a series of Jupyter Notebooks with both complete code and a description of the end-to-end process. To facilitate this, the EPN-TAP (EuroPlanet Table Access Protocol) interface to the PSA could be used. This allows data to be queried and retrieved directly into a Notebook, meaning that anyone could run the notebook to retrieve, process and display the data “live”. An example of such a notebook, written by one of the proposers for the Rosetta MIDAS instrument, is available on github and described in this blog post. The results of such Notebooks could be made available, for example, on BitBucket and linked from the Rosetta mission Cosmos pages to helpfuture users start to use the data.

Project duration: 6 months

The trainee would be required to:

  • Understand the basics of the Rosetta mission, and choose one or more instruments that they are interested in.
  • Ideally, pick a paper showing a published result that the trainee would like to reproduce (or pick a new topic, if they are confident and interested in the subject).
  • Read the appropriate archive documentation to understand how to open the data.
  • Attempt to (re)produce a scientific plot (or map, image, etc.)
  • Document (text and code) the results
  • Continue with another instrument, or use the technique to further investigate the data they now understand.

Desirable expertise:

  • Programming experience (python preferable, but other languages would be possible)
  • Background or interest in planetary science desirable

15. Earth Tides Measurement with GNSS

ESAC supervisor(s): Manuel Castillo, Fernando Martín-Porqueras, Javier Ventura-Traveset

The Galileo Navigation Science Office (GScO) is an inter-Directorate collaboration between NAV and SCI Directorates. Since GScO conception and in order to support its objectives, it has been considered as a main purpose the set-up and operation of the following facilities at ESAC:

  • GNSS Science Service Centre (GSSC) for the exploitation of GNSS data and products as a reference tool for scientific GNSS applications and research
  • GNSS Science Laboratory with advanced scientific receivers, GNSS data processors and training/education tools.

This project is proposed in the context of this office. Since long time, it was suspected that a relation exists between the tidal stress produced by the Moon and the Sun on the Earth crust and the occurrence of big earthquakes and volcanic events. Two years ago, it was shown that a significant statistical correlation exists between earthquake sizes and frequencies and the tidal Sun-Moon influence. However, instead of the use of direct measurements of the tidal effects, the relative Sun and Moon positions before and during earthquakes were analyzed. This indirect confirmation does not yet allow to understand how the tidal stress is transferred within the ground to cause a geological fault or magmatic chamber to move triggering an earthquake or a volcanic event.

Earth Tides are considered within the Precise Positioning Techniques used in GNSS for the analysis of tectonic plates, the definition of reference terrestrial coordinate systems, troposphere studies and other. The proposed project will consist in the direct quantification of the Earth Tides effects in GNSS Time Series. For this purpose, it is planned to use the worldwide data provided by the GNSS Science Service Centre (GSSC) at ESAC acting as a Global Data Center of the International GNSS Service (IGS). Analyzing the variability, amplitude and frequency of the tidal effects in the GNSS measurements it is intended to characterize the crust response in the GNSS receiver station locations. Later, in the cases where it can be feasible, the correlation of these effects with seismic and volcanic events occurring close to the used worldwide GNSS receiver stations network will be analyzed.

The trainee will participate and contribute in the development and operation of GNSS techniques for the quantification of Earth Tides in close collaboration with the GScO team. His/her support will be required for the following activities:

  • Development, implementation and validation of GNSS Data Processing tools for the measurement of Earth Tides;
  • Processing of GNSS data provided by the GSSC;
  • Processing of GNSS data captured at ESAC by GScO scientific-grade GNSS equipment;
  • Analysis and Modelling of Earth Tides and its relation with seismic and volcanic events.

Project duration: 6 months

Desirable expertise:

  • Good Knowledge of Geophysics, GNSS and related disciplines;
  • General knowledge of Astronomy and Geodesy;
  • Practical experience with GNSS equipment would be an asset; HW/SW programming skills would be also an asset.

16. Noiseless Image Enhancement in Astronomy

ESAC supervisor(s): Maggie Lieu, Lyndsay Old, Bruno Altieri, Ivan Valtchanov

In terms of observation time, astronomy is expensive. In such low light conditions, our data suffers from low photon counts and low signal-to-noise, in particular if our observation times are short. By using longer exposure times we can improve the noise levels, but at the cost of blurring. Often in astronomy we tend to stack multiple images to simultaneously reduce the noise and increase the signal. But more recently, Chen et al 2018 have developed a convolutional network that is able to enhance low light images to qualities better than any traditional signal processing technique, removing both noise and colour biases. Such a technique if successfully applied to astronomy could have big implications for astronomers, including significantly reducing observation times and cost. In this project we will train the convolutional network on our astronomical data and compare it to more traditional methods such as stacking, and testing the scientific viability of machine learning

Project duration: 6 months

Desirable expertise or programming languages: Python, Tensorflow (desirable)

17. CubeSat: cubesat communications subsystem

ESAC supervisor(s): Julio Gallegos, Xavier Dupac, Fernando Martín Porqueras

Our group has been working on the development of different subsystems for a CubeSat with the focus on an astronomical application. We would like to continue in the refinement of what we already have and advance the project to a full system in the near future.

If you decide to join us, you will work on the communication system to expand the present UHF/VHF to include a S-Band channel and to study the feasibility of an X-Band system. The ground station is fully operation in UHF/VHF and it will be part of the work to operate and decode the signals received. In addition, you will work on the link between the ground station and the AOCS demonstrator and, if time permits, a link to a simulated rover vehicle.

The roadmap for this project would include the design of the S-band addition to the present ground station, integration of the AOCS with UHF/VHF communication system and the power system. In parallel, the operation of the ground station. This project has a strong hardware component and you will need to work with electronics and antennas.

Project duration: 6 months

Desirable expertise or programming language:

Matlab/C, use of microprocessors (ARM, raspberry-π, etc.), communication and antenna theory and some hands-on experience. A previous course on Spacecraft System Engineering will be certainly useful.

18. Improving the quality of Science Ground Segment

ESAC supervisor(s): Maria Garcia-Reinaldos, Julio Gallegos, Jose Marcos, Silvia de Castro, Luis Martin

The prime objective of the Science Operations Product Assurance and Quality unit (SCI-OQ) is to ensure that science operations projects accomplish their defined mission objectives in a safe, available and reliable way. The management of Product Assurance is fully embedded in the management of the project. The early identification of aspects potentially detrimental for safety and mission success, and the cost-effective prevention of any adverse consequence of such aspects are the basic principles for the ECSS Product Assurance requirements. The disciplines covered by the unit are: product assurance programme implementation, processes assurance and product assurance.

Under the scope of the product and processes assurance, the SCI-OQ unit is interested in having objective evidences of the quality of the processes and the products in the different Science Ground Segment projects. PA/QA support is provided to those projects individually but up to now there are no quality measures of all projects as a whole. Having global quality metrics would allow SCI-OQ to have a global view of the SCI-O projects quality, to comparatively analyse the different projects and to draw conclusions about the quality of the Science Ground Segment projects as a whole. These conclusions will be later on used to define concrete quality objectives for the future.

SCI-OQ has recently started the preparation of a Product Assurance Environment that can be used to perform tests and analysis of the project processes and products using tools replicated from the projects and being able to simulate alternative solutions to improve quality.

The selected candidate will work in the definition, setup, configuration and maintenance of the Product Assurance Environment while she/he will be requested to define, collect, analyse and report on global metrics of applicability to all science projects. The selected candidate will be required to interact with the different projects, to extract results and to critically analyse them and extract conclusions that can be implemented to improve the quality of the processes and the products.

Project Duration: 6 months

Desirable expertise:

  • Software Quality Assurance practices and techniques
  • Software engineering practices
  • Basic knowledge of programming languages (e.g. java, python)
  • Knowledge of some software quality tools (e.g. SonarQube, findBugs, PMD)
  • Good communication skills
 

19. Advanced exploration of the Solar System through virtual reality

ESAC supervisor(s): Marc Costa, Bjoern Grieger, Vicente Navarro, Christophe Arviset

The ESA SPICE Service (ESS) provides ESA´s Solar System Exploration missions ancillary data and geometry information to the science community and to the science ground segments in the shape of SPICE data. Most of this data is three-dimensional, and its interpretation and visualisation is one of the challenges faced by the ground segments that operate the spacecrafts and the scientists that study its data.

Science Observations and contextual Data analysis of Planetary missions can be naturally accommodated into 3D visualizations. Virtual Reality (VR) brings an unprecedented level of interaction possible with these visualisations, and emerging VR platforms such as Oculus VR, Google Cardboard or Samsung Gear VR are making these technologies accessible to the wide public. VR might become in the near future a key part for spacecraft science operators and become a key element of the ground segments.

This project aims to contribute as a first step of this milestone by delviering advanced functionalities for VR tools created to access Solar System geometry, with particular emphasis on visualisation of the science observations carried out by the ESA Planetary fleet on the Solar System. The functionalities will also include advanced functionalities for navigation and data selection in a VR space through peripherals like Oculus Touch.

The trainee will work with the resources, infrastructure and support of the ESA SPICE Service. He or she will also be in contact with scientists, engineers and data experts of ESA’s planetary missions.

Project duration: 6 months.

Desirable expertise or programming language:

  • Software Engineering or general programming skills.
  • Interest on 3D games design (especially cross-platform engines like Unreal 4 or Unity).
  • Proven development experience of applications for mobile devices.
  • Interest in Planetary Science.

20. Can an artificial neural network help to understand X-ray spectra?

ESAC supervisor(s): Norbert Schartel, Maria Santos-Lleo, Richard Saxton

XMM-Newton is an ESA space observatory that collects X-rays from astronomical sources. It carries three high throughput X-ray telescopes with an unprecedented effective area.  Each telescope has an X-ray CCD camera, comprising the European Photon Imaging Camera (EPIC). In addition XMM-Newton is also equipped with two Reflection Grating Spectrometers and an Optical Monitor for simultaneous X-ray imaging, spectroscopy and UV/optical measurements.  The large collecting area and spectral capabilities combine with an ability to make long uninterrupted exposures making XMM-Newton an ideally suited observatory to provide highly sensitive, high spectral resolution, long and continuous X-ray observations.
 

At present, XMM-Newton has been observing the X-ray sky for more than 18 years and still continues. There exists a large data base of public X-ray spectra that populates the scientific XMM-Newton archive and which is growing.
 

In the astrophysical literature X-ray spectra are discussed in comparison to physical models that describe the radiation emitted under different scenarios. So far, none of the various attempts to classify X-ray spectra through machine learning techniques like the artificial neural networks was taken over by the astrophysical community. Therefore, comparison with physical models either remains a manual operation or can only be done in an automatic way for a few standard models. Large data-bases of X-ray spectra that try to provide the properties of the emitting material can only be populated with very limited results.

A group of Japanese scientists have recently used a neural network to analyse the observed X-ray spectrum of a well known astrophysical source. The interesting point of their approach is that the neural network provides the physical parameters of the X-ray emitting material and therefore bridges the gap to the discussions in the scientific literature. This was achieved by training the network with thousands of simulated (physical) emission spectra.

The successful applicant will be asked to explore the available public domain neutral network libraries and possible configurations for the analysis of X-ray data. The best suited combination will be installed, trained for XMM-Newton observations with a large number of simulated spectra and applied to several selected X-ray spectra of high and low resolution. The results will allow checking the performance of the constructed neural network and its re-usability in an automated way to perform specific tasks on real data and to infer the physical properties of the emitting material.

Outcome -Basic experience in:

  • Scientific  research
  • Analysis of data from space observatories, like XMM-Newton,
  • X-ray astrophysics, including the production of simulated X-ray spectra from adopted physical models with different parameters
  • Artificial neural networks, including comparison of differentlearning techniques, production of training data sets and configuration of the networks
  • Training, testing and analysing results of artificial neural networks
  • Potential for a Master Research Project
  • Potential to be published as an XMM-Newton technical note
  • Potential to be published as a refereed paper in the scientific literature, pending on the project duration and results

 

Project Duration: 3 -6 months

Desirable expertise or programming language:
Physics, Astrophysics, Mathematics, Data Science or Software Engineer career paths are appropriate to opt to this project.

 

One or more of the expertise listed below are desirable and will be considered in the selection, but they are not required:

  • Basic Astrophysical background, or Basic Data Science knowledge or Software Engineering background
  • Some experience with machine learned-based tools, like artificial neural networks Linux at user level
  • Some experience with programming languages like IDL or Python
  • Some experience with the XMM-Newton software (SAS) or with standard routines in X-ray astronomy like xspec
  • Some experience with astronomical databases (ADS, NED, Vizier ...)

21. Searching for UFOs around black holes

ESAC supervisor(s): Michael Parker, Gabriele Matzeu, Norbert Schartel, Maria Santos-Lleo

The supermassive black holes in the centers of galaxies feed on the surrounding gas and dust, radiating huge quantities of energy in all wavelengths, particularly in the UV and X-ray bands. Because the accreting material around the black hole gets hotter further in, the corresponding emission shifts to higher energies, so using X-ray telescopes allows us to study extreme physics close to the black hole. One of the most interesting phenomena observed are the ultrafast outflows (UFOs), which are powerful winds launched from the accretion disk. These winds travel at speeds of 30000-100000 km/s, and slam into the interstellar medium of the host galaxy, driving gas out of the galaxy altogether and shutting off star formation. Because of their extreme temperatures, these winds are largely transparent and can only be detected by a handful of absorption lines in the X-ray spectrum.

The aim of this project is to use a new detection method we developed and published in the astrophysics literature, based on the variable behaviour of these black holes, to take a fresh look at archival X-ray data and conduct a large search for new UFOs.

Outcome:

The successful applicant will gain experience of research in astrophysics and practical knowledge of programming and software. If the project is successful, we anticipate publishing the results in the astrophysics literature, and if time permits the student will take a leading role in this.

Duration: 3 - 6 months