PhD subjects

The James Webb Space Telescope has revealed the presence of spiral galaxies very early in the history of the universe (up to redshifts greater than z=5). The appearance of disks so early on is surprising, as they are fragile structures, and seems to reinforce the idea that angular momentum is contributed by the accretion of intergalactic matter. This phenomenon of accretion through cooled filaments could explain several unexpected results from the James Webb. It could also be at the origin of the universal star formation, known as secular star formation, observed in galaxies in the form of a correlation between star formation rate and stellar mass (main sequence of star formation, MS). They would provide the reservoirs for star formation and help regulate it. This represents a major paradigm shift in our understanding of the origin of galaxy shapes and their star formation history. During this thesis, we will address this question through the access to data from the James Webb, Euclid and numerical models to test this hypothesis. It should be noted that without this type of explanation for the high efficiency of galaxy formation observed by the James Webb, we would have to invoke much more drastic changes that could open up a new field. This thesis will help to determine this.

AI (artificial intelligence) is significantly changing the way we solve inverse problems in astrophysics.
In radio interferometry, the detection of radio sources and their classification require taking into account numerous effects such as non-Gaussian noise, incomplete sampling of Fourier space, and the need to construct a sufficient data set for the training. The difficulty increases when the source to be reconstructed evolves over time. Such examples of temporal variations are found in various inverse problems in astrophysics such as transient objects (supernovae, fast radio burst, etc.). ARGOS is a pilot study for a radio interferometer that will perform real-time continuous wide-field observations in centimetre wavelengths. The combination of a wide field of view with high sensitivity will allow ARGOS to detect transient sources that vary on timescales shorter than one second. ARGOS will be able to detect thousands of fast radio bursts per year. These events will need to be accurately differentiated from other transient sources detected by ARGOS, such as supernovae, gamma-ray bursts, white dwarfs, neutron stars, blazars, etc. Given the short time scales of some of these transient events and the need for quick follow up, ARGOS will require state-of-the-art classification solutions employing cutting-edge machine learning architectures. This thesis consists of developing innovative tools from machine learning to solve image reconstruction and source classification problems.

Present-day bulges of spiral galaxies and elliptical galaxies contain very old stars and are thought to be formed in the early Universe. How this actually happened in practice is not well understood, and the most relevant physical processes at play are still unclear. In the last decade, evidence has been growing of the existence of compact star bursting galaxies that might be signposts of bulges caught at the event of formation. More recently, also thanks to new findings from our group based on JWST, a number of further puzzling results has accumulated, currently difficult to explain: A) these star-bursting galaxies are always embedded in larger disk-like systems that are less active but contain most of the existing stellar mass, as if there was no ’naked’ bulge formation; B) in some cases, the outer disks have actually stopped forming stars, thus representing cases of quenching progressing from the outside-in, reversing the standard more familiar pattern (as observed in local spirals and the MilkyWay, where the center is quenched and the outskirts are forming stars); C) the disks are often strongly lopsided in their stellar mass distribution, a feature becoming more and more dominant when looking at earlier times. This phenomenology is currently unexplained. It could be related to merging activity, gas accretion or also feedback effects. If these are forming bulges, how they would evolve in present-day bulges and elliptical galaxies is unclear. Still, these new challenging observations promise breakthrough in the understanding of bulge formation if more progress can be made and further insight gathered. We propose a PhD project where the student will be using imaging and spectroscopy data from JWST to illuminate these issues. Imaging from deep and ultra deep public surveys that is accumulating will be used to increase the statistics and put on more solid grounds the early results gathered so far. The spectroscopy from JWST holds the key to detailed understanding of specific systems, providing information on kinematics of the compact star bursting cores as well as of the outer disks: if these subsystems are co-rotating without major disturbances would support non violent, gas accretion related evolution. On the contrary, counter-rotating subsystems or kinematics disturbances would betray merging events. This kind of test has not yet been carried out. We will use targeted spectroscopy in part already available from the Early-Release project CEERS of which we are members, from the large archive that is accumulating, and from dedicated proposals (pending, and to be submitted in future cycles).

In little more than one year of scientific operations, JWST has already revolutionized our understanding of exoplanets and their atmospheres. The ARIEL space mission, to be launched in 2029, will contribute in due course 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 both complex and computationally expensive. In a first step, our project will numerically investigate the extent of chemical disequilibrium in the atmospheres of JWST targets. We will use towards that end an in-house photochemical model. In a second step, our project will explore Machine Learning (ML) techniques to emulate the outputs of the full photochemical model at a reduced computational cost. The performance of the ML-based emulator will be analyzed with the ultimately goal of its integration into atmospheric retrieval models. The proposed project combines the sophisticated physics and chemistry of exoplanet atmospheres with developments in new numerical techniques.

Interstellar nanoparticles are a major physical component of galaxies, reprocessing starlight, controlling the heating and cooling of the gas, catalyzing chemical reactions and regulating star formation. The abundance, composition, structure and size distribution of these small solid particles, mixed with the interstellar gas, are however poorly-known. They indeed evolve within the interstellar medium and present systematic differences among galaxies. It is thus crucial to obtain detailed, carefully analyzed, empirical constraints of these properties, in a wide diversity of environments. Progress in this field are absolutely necessary to properly interpret observations of nearby star-forming regions and distant galaxies, as well as for precisely modeling interstellar physics.

Of particular interest are the long-wavelength optical properties of the nanoparticle mixture in the millimeter range. This spectral window is currently the least known. Yet, the millimeter opacity of the grain mixture has a central importance, since mass estimates based on spectral energy distribution fitting primarily rely on this quantity. A bias or systematic evolution of the millimeter opacity will directly translate in an inaccuracy in the nanoparticle mass, which is often used as a proxy to infer the gas mass of a region or galaxy.

Our guaranteed time program, IMEGIN (Interpreting the Millimeter Emission of Galaxies at IRAM with NIKA2; PI Madden; 200 hours), with the NIKA2 camera at the 30-m IRAM radiotelescope, has fully mapped 20 nearby galaxies at 1.2 mm and 2 mm. In addition, our open time program, SEINFELD (Submillimeter Excess In Nearby Fairly-Extended Low-metallicity Dwarfs; PI Galliano; 36 hours), is completing the survey down to low-metallicities (the metallicity is the relative abundance of elements heavier than Helium). These new and exceptional data are the first good quality maps of resolved galaxies at millimeter wavelengths, allowing us to study how the grain properties vary with the physical conditions.

The goal of the present PhD project is to combine these observations with other, already existing, multi-wavelength data (in particular, WISE, Spitzer and Herschel), in order to demonstrate how the millimeter opacity depends on the local physical conditions. The first step will consist in processing and homogenizing the data. The student will also have the opportunity to participate in our observing campaigns at Pico Veleta. In a second time, the student will model the spatially-resolved emission, using our in-house, state-of-the-art hierarchical Bayesian code, HerBIE. This will allow the student to produce maps of the nanoparticle properties and compare them to maps of the physical conditions. Finally, these results will be used to model the evolution timescales of the grain properties under the effects of radiation field and gas accretion. The laboratory measurements recently produced by the Toulouse group will be put to profit. This work will be performed within the IMEGIN international collaboration.

This project aims to enhance the synergy between astronomical observations, numerical cosmological simulations and galaxy modelling. Upcoming instruments like Euclid, DESI and Rubin LSST, among others, will make wide-field galaxy surveys with extremely precise measurements. The enhanced precision in the observations however, will requite robust theoretical predictions from galaxy formation models to achieve a profound understanding of the fundamental physics underlying the cosmological measurements.

To achieve this, exa-supercomputers will play a key role. Unlike modern supercomputers, which typically consist of thousands of CPUs for state-of-the-art simulation productions, exa-supercomputers will employ a hybrid configuration of CPUs hosts with GPUs accelerators. This configuration will empower the computations of up to 10^18 operations per second. Exa-supercomputers will revolutionise our ability to simulate cosmological volumes spanning 4 Gigaparsecs (Gpc) with 25 trillion particles! the minimum volume and resolution requirements necessary for making predictions of Euclid data.

However, the challenge to-date lies in the fact that cosmological simulation software designed for exa-supercomputers lacks the modelling for galaxy formation. Examples include the HACC-CRKSPH code (Habib et al. 2016, Emberson et al. 2019) and PKDGRAV3 (Potter, Stadel Teyssier 2017), that have produced the largest simulations to-date, FarPoint (Frontiere et al. 2022), encompassing 1.86 trillion particles within a 1 Gpc volume, and Euclid Flagship (Potter, Stadel Teyssier 2017), featuring 2 trillion particles in a 3 Gpc volume, respectively. While HACC-CRKSPH and PKDGRAV3 were developed to run on modern GPUs-accelerated supercomputers, they lack the complex physics of galaxy formation and can therefore only produce gravity-only cosmological boxes.

The SWIFT code (Schaller et al. 2023) is a parallel effort that has produced Flamingo (Schaye et al. 2023), the largest simulation that integrates gravity, hydrodynamics and galaxy formation physics, encompassing 0.3 trillion particles. However, the caveat of SWIFT is that it was primarily designed for CPU usage. The adaptation of SWIFT to run on modern GPUs will require the entire redevelopment of the code. Another example are the current big simulations of galaxy formation done at Irfu, such as Extreme Horizon (Chabanier et al. 2020), that have also reached their limit as they rely on CPU-based codes that hamper their scalability.

Understanding the intricacies of galaxy formation is paramount for interpreting astronomical observations. In this pursuit, CEA DRF/Irfu stands uniquely positioned to lead the advances in astrophysics in the emerging exascale era. Researchers at DAp and DPhP have already embarked on the analysis of high-quality data from the Euclid mission and DESI. Simultaneously, a team at DEDIP is developing DYABLO (Durocher Delorme, in preparation), a robust gravity + hydrodynamics code tailored explicitly for exa-supercomputing.

In recent years, significant investments have been channeled into the advancement of DYABLO. Numerous researchers at DAp and DEDIP have contributed on various aspects (from the hydrodynamics of solar physics to refining Input/Output processes) thanks to collaborative grants such as PTC-CEA grant and FETHPC European project IO-SEA. Additionally, DYABLO has benefited from interactions with CEA research unit, Maison de la simulation (CEA CNRS).

This ambitious project aims to extend DYABLOs capabilities by integrating galaxy formation modules in collaboration with Maxime Delorme. These modules will encompass radiative gas cooling and heating, star formation, chemical enrichment, stellar mass loss, energy feedback, black holes, and active galactic nuclei feedback. The ultimate objective is to enhance the analysis of Euclid and DESI data by generating simulation predictions of galaxy formation and evolution using DYABLO. The initial dataset will involve a comprehensive examination of clustering of matter and galaxy clustering, in partnership with researchers at DAp/LCEG and DAp/CosmoStat.

This thesis will create the first version of a galaxy formation code optimised for exa-scale supercomputing. Ongoing developments will not only expand its capabilities but also unlock new opportunities for in-depth research, enhancing synergy between astronomical observations, numerical cosmological simulations, and galaxy modelling.

References:
Habib, S., et al., 2016, New Astronomy, Volume 42, p. 49-65.
Emberson, J.D., et al., 2019, The Astrophysical Journal, Volume 877, Issue 2, article id. 85, 17 pp.
Potter, D., Stadel, J., Teyssier, R., 2017, Computational Astrophysics and Cosmology, Vol. 4, Issue 1, 13 pp.
Frontiere, N., et al., 2023, The Astrophysical Journal Supplement Series, Volume 264, Issue 2, 24 pp.
Schaller, M., et al., 2023, eprint arXiv:2305.13380
Schaye, J., et al., 2023, eprint arXiv:2306.04024
Chabanier, S., et al., 2020, Astronomy Astrophysics, Volume 643, id. L8, 12 pp.

The Suns activity is modulated according to an average 11-year magnetic cycle, with the next maximum expected in 2025. This increase in activity implies greater temporal variability for our star, both in terms of its magnetic field, with intense structures appearing and disappearing at a higher rate, and in terms of its atmosphere, which will produce a wind of charged particles that varies in speed and density. These variations have major consequences for the Earth, as it becomes more difficult to predict their impact on our technological society, such as radio blackouts or electrical surges. One of the greatest challenges facing space weather forecasting today is to provide reliable forecasts for the most variable events, which are often also the most extreme.
This thesis proposes to take advantage of the unprecedented conjunction of observations available for the next solar maximum with the Parker Solar Probe and Solar Orbiter space probes, in order to significantly improve the available solar wind models. The student will be able to calibrate the Wind Predict-AW 3D MHD model, one of the most advanced in Europe, to characterize its ability to reproduce conditions of maximum activity. This characterization will involve automated comparisons with different solar datasets, on highly parallel simulations (HPC) producing Big Data-scale results. He will also participate in the development of a new model capable of evolving magnetograms over time, based on the magneto-frictional approach and the evolution of the photospheric electric field - the most advanced techniques for the temporal evolution of magnetic structures - and will use them to quantify the information missing at maximum solar activity, and thus improve space weather forecasts.

The James Webb Space Telescope (JWST) is revolutionizing our view of the first billion years after the big bang, by enabling us to detect the primordial galaxies formed by the collapse of the Universes first overdensities. Initial studies of the properties of these galaxies, partly carried out by our team, have revealed that their formation is still largely misunderstood and potentially in tension with the Lambda Cold Dark Matter (LCDM) model. Indeed, these studies have uncovered a potential excess of massive primordial galaxies, implying accelerated growth of these galaxies at star formation efficiencies well beyond the predictions of theoretical models. Before invoking radically different cosmological and galaxy evolution models, however, it is necessary to confirm these tensions, which are currently based only on highly uncertain measurements of the stellar mass of a few galaxies.
The aim of this thesis is to confirm or refute these tensions by accurately constraining, for the first time, the stellar mass of a large statistical sample of primordial galaxies. To do this, we will combine data from four JWST extragalactic surveys with an original statistical approach of image stacking, enabling us to obtain the average stellar mass of primordial galaxies that are otherwise too faint to be detected individually by the JWST in the critical mid-infrared window. This information, together with that obtained on their star-forming activity, will be decisive in understanding the growth of the Universes first galaxies.

Clusters of galaxies are the most massive entities in the universe.
Applying artificial intelligence to the cosmological analysis of X-ray cluster surveys allows us to tackle this problem from a totally new perspective. Only directly observable parametres are used (redshift, X-ray colour and flux) in a deep learning approach based on hydrodynamical simulations; this allows us to establish an implicit link between the X-ray parameters and the underlying dark matter distribution. From this, we can infer the cosmological parameters, without explicitly computing cluster masses and bypassing the empirical formalism of scaling relations between the X-ray properties and cluster masses.
The goal of the thesis is to apply this method (developed at DAP) to the XMM-XXL survey, which is, 24 years after the XMM launch, the only programme having assembled a cosmological cluster sample with controlled selection effects (~ 400 objects). The expected results will constitute a first in the history of observational cosmology.

The formation and properties of exoplanetary systems is a fascinating question, which has been at the heart of our quest to define mankind and the conditions for life to develop in a broader context. Observations suggest there should be billions of planets in our Galaxy alone. What are the physical process that make planet formation so likely ? Which local conditions are required to transform the stardust of the interstellar medium into pebbles around young stars, and grow these further into planets ? Investigating the dust evolution along the star formation sequence is key to provide a complete picture of the planet formation scenario.
Moreover, the dust grains are crucial because they regulate some key physical processes: for example, the amount of small grains is a key parameter to set the coupling of magnetic fields, hence regulating the sizes and masses of the protostellar disks when they are assembled.

Recently, the star formation group at CEA has obtained some of the first observational clues that the dust particles contained in the pristine disk-forming reservoirs that are the embedded protostars may already have significantly evolved from the submicronic dust populating the interstellar medium.
The proposed PhD aims at exploring new dataset, observations of young protostars from the infrared to the millimeter wavelengths and investigate wether dust particles are indeed growing significantly already during the first 0.5 Myrs of the star formation process.
To improve our understanding of early dust evolution during the disk-building phase, this analysis of multi-scale observational data will be compared to the predictions of evolved dust models implemented in MHD numerical simulations of disk formation.

The student will analyze data obtained with the NIRSpec/NIRCam, then MIRI, instruments aboard the James webb Space Telescope, towards nearby embedded protostars. This data should probe the presence of micronic dust grains in the close vicinity of the young forming disks. The analysis of complementary dust emission maps from the ALMA and NOEMA interferometers, probing the colder dust, will complete the picture, allowing a multi-wavelength approach to constrain the models.

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.