Machine Learning techniques have revolutionized artificial intelligence. Their application to astrophysics and cosmology permits us to analyze the large quantity of data obtained with current surveys and expected from future surveys with the aim of improving our understanding of the cosmological model.
The internship is in the context of the Vera Rubin Observatory (https://www.lsst.org/about) LLST (Legacy Survey of Space and Time), in particular in the context of the Dark Energy (DESC) and Galaxies Rubin Science Collaborations (https://rubinobservatory.org/for-scientists/science-collaborations), and of the Euclid space mission (https://sci.esa.int/web/euclid).
We will oapply two kinds of deep machine learning networks to optimally extract distances, as photometric redshifts, of galaxies detected in Rubin observations.
The Vera Rubin Observatory’s mission is to build a well-understood system that will produce an unprecedented astronomical data set for studies of the deep and dynamic universe, make the data widely accessible to a diverse community of scientists with the goal to address some of the most pressing questions about the structure and evolution of the universe and the objects in it. The Rubin Observatory will conduct a deep survey over a very large of over ten years (LSST) to achieve astronomical catalogs thousands of times larger than have ever previously been compiled. Only the development of efficient Machine learning techniques will permit us to analyze this large amount of data.
At present, photometric redshift estimates are based on the knowledge of galaxy stellar populations, which determine the galaxy SED (e.g. Newman & Gruen 2022). The major limitations are precision and redshift distribution. The first problem is that we still do not in a precise way the galaxy stellar populations, and we can calibrate them with limited spectroscopical samples, and this affect photometric redshift precision. The second problem is due the difficulty to recover the redshift distributions of a large sample of galaxies in the presence of uncertainty on individual redshifts.
The APC Rubin/LSST and Euclid team has validated convolutional and transformer deep machine learning models on galaxy simulations already available in the collaboration and compared their performance. In the internship we will optimize and apply these models to a recent LSST dataset that was released in the summer 2026.
This work is in collaboration with Stanford University and the Rubin team at the SLAC National Accelerator Laboratory, in Stanford, USA. We have obtained a grant for the intern salary from 3 to 4 months with lodging and meals for 2-3 weeks in Stanford in the winter 2026-2027. The intern will work with the Rubin and Euclid teams at APC, which include 2 Ph.D. students, in the larger context of the APC (https://apc.u-paris.fr/) Cosmology team (https://apc.u-paris.fr/fr/cosmologie), and with Ayoub Karine at the LIPADE laboratory.
The internship is open to M2 students in astrophysics or computer science and engineer students for 3 to 4 months (also ideal for engineer césures) starting at the latest in November 2026 and ending in February 2027 (contact us if your dates are different). If you are interested in this internship, please contact Simona Mei (mei@apc.in2p3.fr) as soon as possible. The internship could lead to a PhD thesis, and if you are an international student, we will need to apply for international grants as soon as possible.