Institute of research into the fundamental laws of the Universe

Multi-wavelength observation of astrophysical sources is the key to a global understanding of the physical processes involved. Due to instrumental constraints, the spectral band from 0.1 to 1 MeV is the one that suffers most from insufficient detection sensitivity in existing observatories. This band allows us to observe the deepest and most distant active galactic nuclei, to better understand the formation and evolution of galaxies on cosmological scales. It reveals the processes of nucleosynthesis of the heavy elements in our Universe and the origin of the cosmic rays that are omnipresent in the Universe. The intrinsic difficulty of detection in this spectral range lies in the absorption of these very energetic photons after multiple interactions in the material. This requires good detection efficiency, but also good localisation of all the interactions in order to deduce the direction and energy of the incident photon. These detection challenges are the same for other applications with a strong societal and environmental impact, such as the dismantling of nuclear facilities, air quality monitoring and radiotherapy dosimetry.

The aim of this instrumentation thesis is to develop a versatile 3D detector that can be used in the fields of astrophysics and nuclear physics, with improved detection efficiency in the 100 keV to 1 MeV range and Compton events, as well as the possibility of locating interactions in the detector at better than pixel size.

Several groups around the world, including our own, have developed hard X-ray imaging spectrometers based on high-density pixelated semiconductors for astrophysics (CZT for NuSTAR, CdTe for Solar Orbiter and Hitomi), for synchrotron (Hexitec UK, RAL) or for industrial applications (Timepix, ADVACAM). However, their energy range remains limited to around 200 keV (except for Timepix) due to the thinness of the crystals and their intrinsic operating limitations. To extend the energy range beyond MeV, thicker crystals with good charge carrier transport properties are needed. This is currently possible with CZT, but several challenges need to be overcome.

The first challenge was the ability of manufacturers to produce thick homogeneous CZT crystals. Advances in this field over the last 20 years mean that we can now foresee detectors up to at least 10 mm thick (Redlen, Kromek).

The main remaining technical challenge is the precise estimation of the charge generated by the interaction of a photon in the semiconductor. In a pixelated detector where only the X and Y coordinates of the interaction are recorded, increasing the thickness of the crystal degrades spectral performance. Obtaining Z interaction depth information in a monolithic crystal theoretically makes it possible overcome the associated challenge. This requires the deployment of experimental methods, physical simulations, the design of readout microelectronics circuits and original data analysis methods. In addition, the ability to localise interactions in the detector to better than the size of a pixel will help to solve this challenge.

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.

This PhD thesis focuses on developing an advanced analysis framework for inspecting nuclear waste containers using muon tomography, particularly the scattering method. Muon tomography, which leverages naturally occurring muons from cosmic rays to scan dense structures, has proven valuable in areas where traditional imaging methods fail. CEA/Irfu, with expertise in muon detectors, seeks to harness AI and Machine Learning (ML) to optimize muon data analysis, particularly to reduce long exposure times and improve image reliability.

The project will involve familiarizing with muography (muon tomography image) principles, simulating muon interactions with waste containers, and developing ML-based data augmentation and image processing techniques. The outcome should yield efficient tools to interpret muon images, enhance analysis speed, and classify container contents reliably. The thesis aims to improve nuclear waste inspection’s safety and reliability by producing cleaner, faster, and more interpretable muon tomography data through innovative analysis methods.

Future experiments on linear colliders (e+e-) with low hadronic background require improvements in the spatial resolution of pixel vertex detectors to the micron range, in order to determine precisely the primary and secondary vertices for particles with a high transverse momentum. This kind of detector is set closest to the interaction point. This will provide the opportunity to make precision lifetime measurements of short-lived charged particles. We need to develop pixels arrays with a pixel dimension below the micron squared. The proposed technologies (DOTPIX: Quantum Dot Pixels) should give a significant advance in particle tracking and vertexing. Although the principle of these new devices has been already been studied in IRFU (see reference), this doctoral work should focus on the study of real devices which should then be fabricated using nanotechnologies in collaboration with other Institutes. This should require the use of simulation codes and the fabrication of test structures. Applications outside basics physics are X ray imaging and optimum resolution sensors for visible light holographic cameras.