| 12 sujets IRFU |
Dernière mise à jour :
The galaxy clusters in the XMM-Euclid FornaX deep field
SL-DRF-25-0502
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Starting date : 01-10-2025
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Laboratory link : https://www.cosmostat.org/
More : https://fornax.cosmostat.org/
The XMM Heritage project on the DEEP Euclid Fornax field aims to characterize distant galaxy clusters by comparing X-ray and optical/IR detections. The two methods call on very different cluster properties; ultimately, their combination will make it possible to set the free parameters of the Euclid cluster selection function over the entire WIDE survey, and thus constitute a fundamental ingredient for Euclid cosmological analysis.
The targeted redshift range ([1-2]) has never been systematically explored, despite being a critical area for the use of clusters in cosmology.
With FornaX, for the first time we'll have access to a large volume at these redshifts, enabling us to statistically quantify the evolution of clusters: role of AGNs in the properties of intracluster gas? Are there massive gas-deficient clusters? What are the respective biases of X-ray and optical detection?
The thesis work will involve (1) building and validating the X-ray cluster catalog; (2) correlating it with the optical/IR catalogs obtained by Euclid; and (3) studying the combined X-ray and optical evolution of the clusters.
All the algorithms for detecting and characterizing clusters in XMM images already exist, but we'll be pushing detection even further by using artificial intelligence techniques (combining spatial and spectral information on sources).
The complex problem of spatial correlation between XMM and Euclid cluster catalogs will also involve AI.
Project website: https://fornax.cosmostat.org/
The targeted redshift range ([1-2]) has never been systematically explored, despite being a critical area for the use of clusters in cosmology.
With FornaX, for the first time we'll have access to a large volume at these redshifts, enabling us to statistically quantify the evolution of clusters: role of AGNs in the properties of intracluster gas? Are there massive gas-deficient clusters? What are the respective biases of X-ray and optical detection?
The thesis work will involve (1) building and validating the X-ray cluster catalog; (2) correlating it with the optical/IR catalogs obtained by Euclid; and (3) studying the combined X-ray and optical evolution of the clusters.
All the algorithms for detecting and characterizing clusters in XMM images already exist, but we'll be pushing detection even further by using artificial intelligence techniques (combining spatial and spectral information on sources).
The complex problem of spatial correlation between XMM and Euclid cluster catalogs will also involve AI.
Project website: https://fornax.cosmostat.org/
Investigating the nature of Gamma-Ray Bursts with SVOM
SL-DRF-25-0437
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Starting date : 01-10-2025
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Gamma-Ray Bursts are short lived (0.1-100 s) gamma-ray transient sources that appear randomly on the entire sky. Even if they have been discovered at the end of the 1960s, their nature remained mysterious until the end of the 1990s. It is only thanks to the observations of the BeppoSAX satellite at the end of the last century and especially thanks to the observations of the Swift satellite starting from 2004, that the mysterious nature of GRBs started to be elucidated.
These emissions are related to the final stages of very massive stars (30-50 times the mass of the Sun) for the long GRBs (2 s) or to the merger of two compact objects (typically two neutron stars) for the short GRBs ( 2s). In either case there is the creation of a powerful relativistic jet, which is at the origin of the electromagnetic emission that is measure in gamma-rays and in other energy bands. If this jet points towards the Earth, GRBs can be detected up to very long distances (z~9.1) corresponding to a young age of the Universe (~500 Myr).
Svom is a sino-french space mission dedicated to GRBs, which has been successfully launched on June 22nd 2024, and in which CEA/Irfu/DAp is deeply involved. The PHD subject is aimed at exploiting the multi-wavelength data of SVOM and its partner telescopes in order to investigate the nature of GRBs, and in particular to make use of X-ray data from the MXT telescope in order to try to constrain the nature of the compact object which is at the origin of the relativistic jets.
These emissions are related to the final stages of very massive stars (30-50 times the mass of the Sun) for the long GRBs (2 s) or to the merger of two compact objects (typically two neutron stars) for the short GRBs ( 2s). In either case there is the creation of a powerful relativistic jet, which is at the origin of the electromagnetic emission that is measure in gamma-rays and in other energy bands. If this jet points towards the Earth, GRBs can be detected up to very long distances (z~9.1) corresponding to a young age of the Universe (~500 Myr).
Svom is a sino-french space mission dedicated to GRBs, which has been successfully launched on June 22nd 2024, and in which CEA/Irfu/DAp is deeply involved. The PHD subject is aimed at exploiting the multi-wavelength data of SVOM and its partner telescopes in order to investigate the nature of GRBs, and in particular to make use of X-ray data from the MXT telescope in order to try to constrain the nature of the compact object which is at the origin of the relativistic jets.
Detecting the first clusters of galaxies in the Universe in the maps of the cosmic microwave background
SL-DRF-25-0298
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Starting date : 01-09-2025
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Laboratory link : https://irfu.cea.fr
Galaxy clusters, located at the node of the cosmic web, are the largest gravitationally bound structures in the Universe. Their abundance and spatial distribution are very sensitive to cosmological parameters, such the matter density in the Universe. Galaxy clusters thus constitute a powerful cosmological probe. They have proven to be an efficient probe in the last years (Planck, South Pole Telescope, XXL, etc.) and they are expected to make great progress in the coming years (Euclid, Vera Rubin Observatory, Simons Observatory, CMB- S4, etc.).
The cosmological power of galaxy clusters increases with the size of the redshift (z) range covered by the catalogue. Planck detected the most massive clusters in the Universe in the redshift range 0
This thesis will first study and model the radio and infrared emission from galaxies in the clusters detected in the cosmic microwave background data (Planck, SPT and ACT) as a function of redshift.
Secondly, one will quantify the impact of these emissions on existing cluster detection tools, in the redshift range currently being probed (0
The cosmological power of galaxy clusters increases with the size of the redshift (z) range covered by the catalogue. Planck detected the most massive clusters in the Universe in the redshift range 0
This thesis will first study and model the radio and infrared emission from galaxies in the clusters detected in the cosmic microwave background data (Planck, SPT and ACT) as a function of redshift.
Secondly, one will quantify the impact of these emissions on existing cluster detection tools, in the redshift range currently being probed (0
Generative AI for Robust Uncertainty Quantification in Astrophysical Inverse Problems
SL-DRF-25-0514
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Starting date : 01-10-2025
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Personal web page : https://flanusse.net
Laboratory link : https://www.cosmostat.org
More : https://tobias-liaudat.github.io
Context
Inverse problems, i.e. estimating underlying signals from corrupted observations, are ubiquitous in astrophysics, and our ability to solve them accurately is critical to the scientific interpretation of the data. Examples of such problems include inferring the distribution of dark matter in the Universe from gravitational lensing effects [1], or component separation in radio interferometric imaging [2].
Thanks to recent deep learning advances, and in particular deep generative modeling techniques (e.g. diffusion models), it now becomes not only possible to get an estimate of the solution of these inverse problems, but to perform Uncertainty Quantification by estimating the full Bayesian posterior of the problem, i.e. having access to all possible solutions that would be allowed by the data, but also plausible under prior knowledge.
Our team has in particular been pioneering such Bayesian methods to combine our knowledge of the physics of the problem, in the form of an explicit likelihood term, with data-driven priors implemented as generative models. This physics-constrained approach ensures that solutions remain compatible with the data and prevents “hallucinations” that typically plague most generative AI applications.
However, despite remarkable progress over the last years, several challenges still remain in the aforementioned framework, and most notably:
[Imperfect or distributionally shifted prior data] Building data-driven priors typically requires having access to examples of non corrupted data, which in many cases do not exist (e.g. all astronomical images are observed with noise and some amount of blurring), or might exist but may have distribution shifts compared to the problems we would like to apply this prior to.
This mismatch can bias estimations and lead to incorrect scientific conclusions. Therefore, the adaptation, or calibration, of data-driven priors from incomplete and noisy observations becomes crucial for working with real data in astrophysical applications.
[Efficient sampling of high dimensional posteriors] Even if the likelihood and the data-driven prior are available, correctly sampling from non-convex multimodal probability distributions in such high-dimensions in an efficient way remains a challenging problem. The most effective methods to date rely on diffusion models, but rely on approximations and can be expensive at inference time to reach accurate estimates of the desired posteriors.
The stringent requirements of scientific applications are a powerful driver for improved methodologies, but beyond the astrophysical scientific context motivating this research, these tools also find broad applicability in many other domains, including medical images [3].
PhD project
The candidate will aim to address these limitations of current methodologies, with the overall aim to make uncertainty quantification for large scale inverse problems faster and more accurate.
As a first direction of research, we will extend recent methodology concurrently developed by our team and our Ciela collaborators [4,5], based on Expectation-Maximization, to iteratively learn (or adapt) diffusion-based priors to data observed under some amount of corruption. This strategy has been shown to be effective at correcting for distribution shifts in the prior (and therefore leading to well calibrated posteriors). However, this approach is still expensive as it requires iteratively solving inverse problems and retraining the diffusion models, and is critically dependent on the quality of the inverse problem solver. We will explore several strategies including variational inference and improved inverse problem sampling strategies to address these issues.
As a second (but connected) direction we will focus on the development of general methodologies for sampling complex posteriors (multimodal/complex geometries) of non-linear inverse problems. Specifically we will investigate strategies based on posterior annealing, inspired from diffusion model sampling, applicable in situations with explicit likelihoods and priors.
Finally, we will apply these methodologies to some challenging and high impact inverse problems in astrophysics, in particular in collaboration with our colleagues from the Ciela institute, we will aim to improve source and lens reconstruction of strong gravitational lensing systems.
Publications in top machine learning conferences are expected (NeurIPS, ICML), as well as publications of the applications of these methodologies in astrophysical journals.
References
[1] Benjamin Remy, Francois Lanusse, Niall Jeffrey, Jia Liu, Jean-Luc Starck, Ken Osato, Tim Schrabback, Probabilistic Mass Mapping with Neural Score Estimation, https://www.aanda.org/articles/aa/abs/2023/04/aa43054-22/aa43054-22.html
[2] Tobías I Liaudat, Matthijs Mars, Matthew A Price, Marcelo Pereyra, Marta M Betcke, Jason D McEwen, Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging, RAS Techniques and Instruments, Volume 3, Issue 1, January 2024, Pages 505–534, https://doi.org/10.1093/rasti/rzae030
[3] Zaccharie Ramzi, Benjamin Remy, Francois Lanusse, Jean-Luc Starck, Philippe Ciuciu, Denoising Score-Matching for Uncertainty Quantification in Inverse Problems, https://arxiv.org/abs/2011.08698
[4] François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe, Learning Diffusion Priors from Observations by Expectation Maximization, NeurIPS 2024, https://arxiv.org/abs/2405.13712
[5] Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur, Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems, https://arxiv.org/abs/2407.17667
Inverse problems, i.e. estimating underlying signals from corrupted observations, are ubiquitous in astrophysics, and our ability to solve them accurately is critical to the scientific interpretation of the data. Examples of such problems include inferring the distribution of dark matter in the Universe from gravitational lensing effects [1], or component separation in radio interferometric imaging [2].
Thanks to recent deep learning advances, and in particular deep generative modeling techniques (e.g. diffusion models), it now becomes not only possible to get an estimate of the solution of these inverse problems, but to perform Uncertainty Quantification by estimating the full Bayesian posterior of the problem, i.e. having access to all possible solutions that would be allowed by the data, but also plausible under prior knowledge.
Our team has in particular been pioneering such Bayesian methods to combine our knowledge of the physics of the problem, in the form of an explicit likelihood term, with data-driven priors implemented as generative models. This physics-constrained approach ensures that solutions remain compatible with the data and prevents “hallucinations” that typically plague most generative AI applications.
However, despite remarkable progress over the last years, several challenges still remain in the aforementioned framework, and most notably:
[Imperfect or distributionally shifted prior data] Building data-driven priors typically requires having access to examples of non corrupted data, which in many cases do not exist (e.g. all astronomical images are observed with noise and some amount of blurring), or might exist but may have distribution shifts compared to the problems we would like to apply this prior to.
This mismatch can bias estimations and lead to incorrect scientific conclusions. Therefore, the adaptation, or calibration, of data-driven priors from incomplete and noisy observations becomes crucial for working with real data in astrophysical applications.
[Efficient sampling of high dimensional posteriors] Even if the likelihood and the data-driven prior are available, correctly sampling from non-convex multimodal probability distributions in such high-dimensions in an efficient way remains a challenging problem. The most effective methods to date rely on diffusion models, but rely on approximations and can be expensive at inference time to reach accurate estimates of the desired posteriors.
The stringent requirements of scientific applications are a powerful driver for improved methodologies, but beyond the astrophysical scientific context motivating this research, these tools also find broad applicability in many other domains, including medical images [3].
PhD project
The candidate will aim to address these limitations of current methodologies, with the overall aim to make uncertainty quantification for large scale inverse problems faster and more accurate.
As a first direction of research, we will extend recent methodology concurrently developed by our team and our Ciela collaborators [4,5], based on Expectation-Maximization, to iteratively learn (or adapt) diffusion-based priors to data observed under some amount of corruption. This strategy has been shown to be effective at correcting for distribution shifts in the prior (and therefore leading to well calibrated posteriors). However, this approach is still expensive as it requires iteratively solving inverse problems and retraining the diffusion models, and is critically dependent on the quality of the inverse problem solver. We will explore several strategies including variational inference and improved inverse problem sampling strategies to address these issues.
As a second (but connected) direction we will focus on the development of general methodologies for sampling complex posteriors (multimodal/complex geometries) of non-linear inverse problems. Specifically we will investigate strategies based on posterior annealing, inspired from diffusion model sampling, applicable in situations with explicit likelihoods and priors.
Finally, we will apply these methodologies to some challenging and high impact inverse problems in astrophysics, in particular in collaboration with our colleagues from the Ciela institute, we will aim to improve source and lens reconstruction of strong gravitational lensing systems.
Publications in top machine learning conferences are expected (NeurIPS, ICML), as well as publications of the applications of these methodologies in astrophysical journals.
References
[1] Benjamin Remy, Francois Lanusse, Niall Jeffrey, Jia Liu, Jean-Luc Starck, Ken Osato, Tim Schrabback, Probabilistic Mass Mapping with Neural Score Estimation, https://www.aanda.org/articles/aa/abs/2023/04/aa43054-22/aa43054-22.html
[2] Tobías I Liaudat, Matthijs Mars, Matthew A Price, Marcelo Pereyra, Marta M Betcke, Jason D McEwen, Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging, RAS Techniques and Instruments, Volume 3, Issue 1, January 2024, Pages 505–534, https://doi.org/10.1093/rasti/rzae030
[3] Zaccharie Ramzi, Benjamin Remy, Francois Lanusse, Jean-Luc Starck, Philippe Ciuciu, Denoising Score-Matching for Uncertainty Quantification in Inverse Problems, https://arxiv.org/abs/2011.08698
[4] François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe, Learning Diffusion Priors from Observations by Expectation Maximization, NeurIPS 2024, https://arxiv.org/abs/2405.13712
[5] Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur, Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems, https://arxiv.org/abs/2407.17667
Source clustering impact on Euclid weak lensing high-order statistics
SL-DRF-25-0341
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Starting date : 01-10-2025
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Personal web page : http://jstarck.cosmostat.org
Laboratory link : http://www.cosmostat.org
More : https://www.physics.ox.ac.uk/our-people/porqueres
In the coming years, the Euclid mission will provide measurements of the shapes and positions of billions of galaxies with unprecedented precision. As the light from the background galaxies travels through the Universe, it is deflected by the gravity of cosmic structures, distorting the apparent shapes of galaxies. This effect, known as weak lensing, is the most powerful cosmological probe of the next decade, and it can answer some of the biggest questions in cosmology: What are dark matter and dark energy, and how do cosmic structures form?
The standard approach to weak lensing analysis is to fit the two-point statistics of the data, such as the correlation function of the observed galaxy shapes. However, this data compression is sub- optimal and discards large amounts of information. This has led to the development of several approaches based on high-order statistics, such as third moments, wavelet phase harmonics and field-level analyses. These techniques provide more precise constraints on the parameters of the cosmological model (Ajani et al. 2023). However, with their increasing precision, these methods become sensitive to systematic effects that were negligible in the standard two-point statistics analyses.
One of these systematics is source clustering, which refers to the non-uniform distribution of the galaxies observed in weak lensing surveys. Rather than being uniformly distributed, the observed galaxies trace the underlying matter density. This clustering causes a correlation between the lensing signal and the galaxy number density, leading to two effects: (1) it modulates the effective redshift distribution of the galaxies, and (2) it correlates the galaxy shape noise with the lensing signal. Although this effect is negligible for two-point statistics (Krause et al. 2021, Linke et al. 2024), it significantly impacts the results of high-order statistics (Gatti et al. 2023). Therefore, accurate modelling of source clustering is critical to applying these new techniques to Euclid’s weak lensing data.
In this project, we will develop an inference framework to model source clustering and asses its impact on cosmological constraints from high-order statistics. The objectives of the project are:
1. Develop an inference framework that populates dark matter fields with galaxies, accurately modelling the non-uniform distribution of background galaxies in weak lensing surveys.
2. Quantify the source clustering impact on the cosmological parameters from wavelet transforms and field-level analyses.
3. Incorporate source clustering in emulators of the matter distribution to enable accurate data modelling in the high-order statistics analyses.
With these developments, this project will improve the accuracy of cosmological analyses and the realism of the data modelling, making high-order statistics analyses possible for Euclid data.
The standard approach to weak lensing analysis is to fit the two-point statistics of the data, such as the correlation function of the observed galaxy shapes. However, this data compression is sub- optimal and discards large amounts of information. This has led to the development of several approaches based on high-order statistics, such as third moments, wavelet phase harmonics and field-level analyses. These techniques provide more precise constraints on the parameters of the cosmological model (Ajani et al. 2023). However, with their increasing precision, these methods become sensitive to systematic effects that were negligible in the standard two-point statistics analyses.
One of these systematics is source clustering, which refers to the non-uniform distribution of the galaxies observed in weak lensing surveys. Rather than being uniformly distributed, the observed galaxies trace the underlying matter density. This clustering causes a correlation between the lensing signal and the galaxy number density, leading to two effects: (1) it modulates the effective redshift distribution of the galaxies, and (2) it correlates the galaxy shape noise with the lensing signal. Although this effect is negligible for two-point statistics (Krause et al. 2021, Linke et al. 2024), it significantly impacts the results of high-order statistics (Gatti et al. 2023). Therefore, accurate modelling of source clustering is critical to applying these new techniques to Euclid’s weak lensing data.
In this project, we will develop an inference framework to model source clustering and asses its impact on cosmological constraints from high-order statistics. The objectives of the project are:
1. Develop an inference framework that populates dark matter fields with galaxies, accurately modelling the non-uniform distribution of background galaxies in weak lensing surveys.
2. Quantify the source clustering impact on the cosmological parameters from wavelet transforms and field-level analyses.
3. Incorporate source clustering in emulators of the matter distribution to enable accurate data modelling in the high-order statistics analyses.
With these developments, this project will improve the accuracy of cosmological analyses and the realism of the data modelling, making high-order statistics analyses possible for Euclid data.
The biased Cosmic web, from theoretical modelling to observations
SL-DRF-25-0270
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The study of the filamentary Cosmic Web is a paramount aspect of modern research in cosmology. With the advent of extremely large and precise cosmological datasets which are now (or within months) coming notably from the Euclid space mission, it becomes feasible to study in detail the formation of cosmic structures through gravitational instability. In particular, fine non-linear aspects of this dynamics can be studied from a theoretical point of view with the hope of detecting signatures in real observations. One of the major difficulty in this regard is probably to make the link between the observed distribution of galaxies along filaments and the underlying matter distribution for which first-principles models are known. Building on recent and state of the art theoretical developments in gravitational perturbation theory and constrained random field theory, the successful candidate will develop first-principles predictions for statistical observables (extrema counts, topological estimators, extrema correlation functions, e.g. Pogosyan et al. 2009, MNRAS 396 or Ayçoberry, Barthelemy, Codis 2024, A&A 686) of the cosmic web, applied to the actual discrete field of galaxies which only traces the total matter in a biased manner. This model will then be applied to the analysis of Euclid data.
Bayesian Inference with Differentiable Simulators for the Joint Analysis of Galaxy Clustering and CMB Lensing
SL-DRF-25-0351
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The goal of this PhD project is to develop a novel joint analysis for the DESI galaxy clustering
and Planck PR4/ACT CMB lensing data, based on numerical simulations of the surveys and
state-of-the-art machine learning and statistical inference techniques. The aim is to overcome
many of the limitations of the traditional approaches and improve the recovery of cosmological
parameters. The joint galaxy clustering - CMB lensing inference will significantly improve
constraints on the growth of structure upon DESI-only analyses and refine even more the test of general relativity.
and Planck PR4/ACT CMB lensing data, based on numerical simulations of the surveys and
state-of-the-art machine learning and statistical inference techniques. The aim is to overcome
many of the limitations of the traditional approaches and improve the recovery of cosmological
parameters. The joint galaxy clustering - CMB lensing inference will significantly improve
constraints on the growth of structure upon DESI-only analyses and refine even more the test of general relativity.
Multi-messenger analysis of core-collapse supernovae
SL-DRF-25-0316
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Starting date : 01-10-2024
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Personal web page : https://www.youtube.com/watch?v=-IjAwszbiO8
Core-collapse supernovae play a crucial role in the stellar evolution of massive stars, the birth of neutron stars and black holes, and the chemical enrichment of galaxies. How do they explode? The explosion mechanism can be revealed by the analysis of multi-messenger signals: the production of neutrinos and gravitational waves is modulated by hydrodynamic instabilities during the second following the formation of a proto-neutron star.
This thesis proposes to use the complementarity of multi-messenger signals, using numerical simulations of the stellar core- collapse and perturbative analysis, in order to extract physical information on the explosion mechanism.
The project will particularly focus on the multi-messenger properties of the stationary shock instability (SASI) and the corotational instability (low T/W) for a rotating progenitor. For each of these instabilities, the signal from different species of neutrinos and the gravitational waves with different polarization will be exploited, as well as the correlation between them.
This thesis proposes to use the complementarity of multi-messenger signals, using numerical simulations of the stellar core- collapse and perturbative analysis, in order to extract physical information on the explosion mechanism.
The project will particularly focus on the multi-messenger properties of the stationary shock instability (SASI) and the corotational instability (low T/W) for a rotating progenitor. For each of these instabilities, the signal from different species of neutrinos and the gravitational waves with different polarization will be exploited, as well as the correlation between them.
Machine-learning methods for the cosmological analysis of weak- gravitational lensing images from the Euclid satellite
SL-DRF-25-0367
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Starting date : 01-10-2025
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Personal web page : http://www.cosmostat.org/people/kilbinger
Laboratory link : http://www.cosmostat.org
Weak gravitational lensing, the distortion of the images of high-redshift galaxies due to foreground matter structures on large scales, is one
of the most promising tools of cosmology to probe the dark sector of the Universe. The statistical analysis of lensing distortions can reveal
the dark-matter distribution on large scales, The European space satellite Euclid will measure cosmological parameters to unprecedented accuracy. To achieve this ambitious goal, a number of sources of systematic errors have to be quanti?ed and understood. One of the main origins of bias is related to the detection of galaxies. There is a strong dependence on local number density and whether the galaxy's light emission overlaps with nearby
objects. If not handled correctly, such ``blended`` galaxies will strongly bias any subsequent measurement of weak-lensing image
distortions.
The goal of this PhD is to quantify and correct weak-lensing detection biases, in particular due to blending. To that end, modern machine-
and deep-learning algorithms, including auto-di?erentiation techniques, will be used. Those techniques allow for a very e?cient estimation
of the sensitivity of biases to galaxy and survey properties without the need to create a vast number of simulations. The student will carry out cosmological parameter inference of Euclid weak-lensing data. Bias corrections developed during this thesis will be included a prior in galaxy shape measurements, or a posterior as nuisance parameters. This will lead to measurements of cosmological parameters with an reliability and robustness required for precision cosmology.
of the most promising tools of cosmology to probe the dark sector of the Universe. The statistical analysis of lensing distortions can reveal
the dark-matter distribution on large scales, The European space satellite Euclid will measure cosmological parameters to unprecedented accuracy. To achieve this ambitious goal, a number of sources of systematic errors have to be quanti?ed and understood. One of the main origins of bias is related to the detection of galaxies. There is a strong dependence on local number density and whether the galaxy's light emission overlaps with nearby
objects. If not handled correctly, such ``blended`` galaxies will strongly bias any subsequent measurement of weak-lensing image
distortions.
The goal of this PhD is to quantify and correct weak-lensing detection biases, in particular due to blending. To that end, modern machine-
and deep-learning algorithms, including auto-di?erentiation techniques, will be used. Those techniques allow for a very e?cient estimation
of the sensitivity of biases to galaxy and survey properties without the need to create a vast number of simulations. The student will carry out cosmological parameter inference of Euclid weak-lensing data. Bias corrections developed during this thesis will be included a prior in galaxy shape measurements, or a posterior as nuisance parameters. This will lead to measurements of cosmological parameters with an reliability and robustness required for precision cosmology.
The dawn of planet formation
SL-DRF-25-0399
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Starting date : 01-10-2025
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Personal web page : https://ulebreui.github.io/
Laboratory link : https://irfu.cea.fr/Phocea/Vie_des_labos/Ast/ast_groupe.php?id_groupe=1250
Planet formation is a key topic of modern astrophysics with implications on existential questions such as the origin of life in the Universe. Quite surprisingly, we do not precisely know when and where planets are formed in protoplanetary disks. Recent observations however indicate that this might happen sooner than we previously believed. But the physical conditions in the young disks remain poorly constrained. During this thesis we propose to test the hypothesis that planets could form early. We will perform 3D simulations of protoplanetary disk formation with gas, dust and including the mechanisms of planetesimal formation. In addition from determining whether planets form early we will be able to predict the architectures of exoplanet systems and to compare them to real ones. This work, beyond the current state-of-the-art, is timely as many efforts are currently being done by our community to better understand exoplanets as well as our origins.
Fast parameter inference of gravitational waves for the LISA space mission
SL-DRF-25-0422
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Starting date : 01-10-2025
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Personal web page : https://tobias-liaudat.github.io
Context
In 2016, the announcement of the first direct detection of gravitational waves ushered in an era in which the universe will be probed in an unprecedented way. At the same time, the complete success of the LISA Pathfinder mission validated certain technologies selected for the LISA (Laser Interferometer Space Antenna) project. The year 2024 started with the adoption of the LISA mission by the European Space Agency (ESA) and NASA. This unprecedented gravitational wave space observatory will consist of three satellites 2.5 million kilometres apart and will enable the direct detection of gravitational waves at undetectable frequencies by terrestrial interferometers. ESA plans a launch in 2035.
In parallel with the technical aspects, the LISA mission introduces several data analysis challenges that need to be addressed for the mission’s success. The mission needs to prove that with simulations, the scientific community will be able to identify and characterise the detected gravitational wave signals. Data analysis involves various stages, one of which is the rapid analysis pipeline, whose role is to detect new events and characterise the detected events. The last point concerns the rapid estimation of the position in the sky of the source of gravitational wave emission and their characteristic time, such as the coalescence time for a black hole merger.
These analysis tools form the low-latency analysis pipeline. As well as being of interest to LISA, this pipeline also plays a vital role in enabling multi-messenger astronomy, consisting of rapidly monitoring events detected by electromagnetic observations (ground-based or space-based observatories, from radio waves to Gamma rays).
PhD project
The PhD project focuses on the development of event detection and identification tools for the low-latency alert pipeline (LLAP) of LISA. This pipeline will be an essential part of the LISA analysis workflow, providing a rapid detection of massive black hole binaries, as well as a fast and accurate estimation of the sources’ sky localizations as well as coalescence time. These are key information for multi-messager follow-ups as well as for the global analysis of the LISA data.
While rapid analysis methods have been developed for ground-based interferometers, the case of space-based interferometers such as LISA remains a field to be explored. Adapted data processing will have to consider how data is transmitted in packets, making it necessary to detect events from incomplete data. Using data marred by artefacts such as glitches or missing data packages, these methods should enable the detection, discrimination and analysis of various sources: black hole mergers, EMRIs (spiral binaries with extreme mass ratios), bursts and binaries from compact objects. A final and crucial element of complexity is the speed of analysis, which constitutes a strong constraint on the methods to be developed.
To this end, the problems we will be tackling during this thesis will be:
1. The fast parameter inference of the gravitational waves, noticeably, the sky position, and the coalescence time. Two of the main difficulties reside in the multimodality of the posterior probability distribution of the target parameters and the stringent computing time requirements. To that end, we will consider different advanced inference strategies including:
(a) Using gradient-based sampling algorithms like Langevin diffusions or Hamiltonian Monte Carlo methods adapted to LISA’s gravitational wave problem,
(b) Using machine learning-assisted methods to accelerate the sampling (e.g. normalising flows),
(c) Using variational inference techniques.
2. The early detection of black hole mergers.
3. The increasing complexity of LISA data, including, among others, realistic noise, realistic instrument response, glitches, data gaps, and overlapping sources.
4. The online handling of the incoming 5-minute data packages with the developed fast inference framework.
This thesis will be based on applying Bayesian and statistical methods for data analysis and machine learning. However, an effort on the physics part is necessary, both to understand the simulations and the different waveforms considered (with their underlying hypotheses) and to interpret the results regarding the detectability of black hole merger signals in the context of the rapid analysis of LISA data.
In 2016, the announcement of the first direct detection of gravitational waves ushered in an era in which the universe will be probed in an unprecedented way. At the same time, the complete success of the LISA Pathfinder mission validated certain technologies selected for the LISA (Laser Interferometer Space Antenna) project. The year 2024 started with the adoption of the LISA mission by the European Space Agency (ESA) and NASA. This unprecedented gravitational wave space observatory will consist of three satellites 2.5 million kilometres apart and will enable the direct detection of gravitational waves at undetectable frequencies by terrestrial interferometers. ESA plans a launch in 2035.
In parallel with the technical aspects, the LISA mission introduces several data analysis challenges that need to be addressed for the mission’s success. The mission needs to prove that with simulations, the scientific community will be able to identify and characterise the detected gravitational wave signals. Data analysis involves various stages, one of which is the rapid analysis pipeline, whose role is to detect new events and characterise the detected events. The last point concerns the rapid estimation of the position in the sky of the source of gravitational wave emission and their characteristic time, such as the coalescence time for a black hole merger.
These analysis tools form the low-latency analysis pipeline. As well as being of interest to LISA, this pipeline also plays a vital role in enabling multi-messenger astronomy, consisting of rapidly monitoring events detected by electromagnetic observations (ground-based or space-based observatories, from radio waves to Gamma rays).
PhD project
The PhD project focuses on the development of event detection and identification tools for the low-latency alert pipeline (LLAP) of LISA. This pipeline will be an essential part of the LISA analysis workflow, providing a rapid detection of massive black hole binaries, as well as a fast and accurate estimation of the sources’ sky localizations as well as coalescence time. These are key information for multi-messager follow-ups as well as for the global analysis of the LISA data.
While rapid analysis methods have been developed for ground-based interferometers, the case of space-based interferometers such as LISA remains a field to be explored. Adapted data processing will have to consider how data is transmitted in packets, making it necessary to detect events from incomplete data. Using data marred by artefacts such as glitches or missing data packages, these methods should enable the detection, discrimination and analysis of various sources: black hole mergers, EMRIs (spiral binaries with extreme mass ratios), bursts and binaries from compact objects. A final and crucial element of complexity is the speed of analysis, which constitutes a strong constraint on the methods to be developed.
To this end, the problems we will be tackling during this thesis will be:
1. The fast parameter inference of the gravitational waves, noticeably, the sky position, and the coalescence time. Two of the main difficulties reside in the multimodality of the posterior probability distribution of the target parameters and the stringent computing time requirements. To that end, we will consider different advanced inference strategies including:
(a) Using gradient-based sampling algorithms like Langevin diffusions or Hamiltonian Monte Carlo methods adapted to LISA’s gravitational wave problem,
(b) Using machine learning-assisted methods to accelerate the sampling (e.g. normalising flows),
(c) Using variational inference techniques.
2. The early detection of black hole mergers.
3. The increasing complexity of LISA data, including, among others, realistic noise, realistic instrument response, glitches, data gaps, and overlapping sources.
4. The online handling of the incoming 5-minute data packages with the developed fast inference framework.
This thesis will be based on applying Bayesian and statistical methods for data analysis and machine learning. However, an effort on the physics part is necessary, both to understand the simulations and the different waveforms considered (with their underlying hypotheses) and to interpret the results regarding the detectability of black hole merger signals in the context of the rapid analysis of LISA data.
Disequilibrium chemistry of exoplanets’ high-metallicity atmospheres in JWST times
SL-DRF-25-0451
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In little more than two years of scientific operations, JWST has revolutionized our understanding of exoplanets and their atmospheres. The ARIEL space mission, to be launched in 2029, will soon contribute to this revolution. A main finding that has been enabled by the exquisite quality of the JWST data is that exoplanet atmospheres are in chemical disequilibrium. A full treatment of disequilibrium is complex, especially when the atmospheres are metal-rich, i.e. when they contain in significant abundances elements other than hydrogen and helium. In a first step, our project will numerically investigate the extent of chemical disequilibrium in the atmospheres of JWST targets suspected to have metal-rich atmospheres. We will use towards that end an in-house photochemical model. In a second step, our project will explore the effect of super-thermal chemistry as a driver of chemical disequilibrium. This will offer previously-unexplored insight into the chemistry of metal-rich atmospheres, with the potential to shed new light into the chemical and evolutionary paths of low-mass exoplanets.
