Abstract: The recently released Type Ia supernovae (SNe Ia) sample, Pantheon+, is an updated version of Pantheon and has very important cosmological implications. To explore the origin of the enhanced constraining power and internal correlations of datasets in different redshifts, we perform a comprehensively tomographic analysis of the Pantheon+ sample without and with the Cepheid host distance calibration, respectively. Specifically, we take two binning methods to analyze the Pantheon+ sample, i.e., equal redshift interval and equal supernovae number for each bin. For the case of equal redshift interval, after dividing the sample to 10 bins, the first bin in the redshift range z is an element of [0.00122, 0.227235] dominates the constraining power of the whole sample. For the case of equal supernovae number, the first three low redshift bins prefer a large matter fraction Omega(m) and only the sixth bin gives a relatively low cosmic expansion rate H-0. For both binning methods, we find no obvious evidence of evolution of H-0 and Omega(m) at the 2 sigma confidence level. The inclusion of the SHOES calibration can significantly compress the parameter space of background dynamics of the universe in each bin. When not considering the calibration, combining the Pantheon+ sample with cosmic microwave background, baryon acoustic oscillations, cosmic chronometers, galaxy clustering and weak lensing data, we give the strongest 1 sigma constraint H-0 = 67.88 +/- 0.42kms(-1) Mpc(-1). However, the addition of the calibration leads to a global shift of the parameter space from the combined constraint and H-0 = 68.66 +/- 0.42 km s(-1) Mpc(-1), which is inconsistent with the Planck-2018 result at about 2 sigma confidence level.

Abstract: A novel high-gradient accelerating structure with low phase velocity, v/c = 0.38, has been designed, manufactured and high-power tested. The structure was designed and built using the methodology and technology developed for CLIC 100 MV/m high-gradient accelerating structures, which have speed of light phase velocity, but adapts them to a structure for nonrelativistic particles. The parameters of the structure were optimized for the compact proton therapy linac project, and specifically to 76 MeV energy protons, but the type of structure opens more generally the possibility of compact low phase velocity linacs. The structure operates in S-band, is backward traveling wave (BTW) with a phase advance of 150 degrees and has an active length of 19 cm. The main objective for designing and testing this structure was to demonstrate that low velocity particles, in particular protons, can be accelerated with high gradients. In addition, the performance of this structure compared to other type of structures provides insights into the factors that limit high gradient operation. The structure was conditioned successfully to high gradient using the same protocol as for CLIC X-band structures. However, after the high power test, data analysis realized that the structure had been installed backwards, that is, the input power had been fed into what is nominally the output end of the structure. This resulted in higher peak fields at the power feed end and a steeply decreasing field profile along the structure, rather than the intended near constant field and gradient profile. A local accelerating gradient of 81 MV/m near the input end was achieved at a pulse length of 1.2 μs and with a breakdown rate (BDR) of 7.2 x 10(-7) 1 /pulse/m. The reverse configuration was accidental but the operating with this field condition gave very important insights into high-gradient behaviour and a comprehensive analysis has been carried out. A particular attention was paid to the characterization of the distribution of BD positions along the structure and within a cell.

Abstract: A complete methodology for detector calibration and energy-loss correction in charged-particle spectroscopy is presented. This has been applied to a compact set-up of four silicon detectors used for beta-delayed particle spectroscopy. The characterisation of the set-up was carried out using GEANT4 Monte Carlo simulations and standard alpha-calibration sources. The response function of the system was in this way accurately determined to be used for spectral unfolding.

Abstract: Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a similar to 0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on GitHub (https://github.com/PabloVD/HaloGraphNet).

Abstract: Primordial black holes (PBHs) represent a natural candidate for one of the components of the dark matter (DM) in the Universe. In this review, we shall discuss the basics of their formation, abundance and signatures. Some of their characteristic signals are examined, such as the emission of particles due to Hawking evaporation and the accretion of the surrounding matter, effects which could leave an impact in the evolution of the Universe and the formation of structures. The most relevant probes capable of constraining their masses and population are discussed.