Abstract: Objective. To demonstrate the benefits of using an joint image reconstruction algorithm based on the List Mode Maximum Likelihood Expectation Maximization that combines events measured in different channels of information of a Compton camera. Approach. Both simulations and experimental data are employed to show the algorithm performance. Main results. The obtained joint images present improved image quality and yield better estimates of displacements of high-energy gamma-ray emitting sources. The algorithm also provides images that are more stable than any individual channel against the noisy convergence that characterizes Maximum Likelihood based algorithms. Significance. The joint reconstruction algorithm can improve the quality and robustness of Compton camera images. It also has high versatility, as it can be easily adapted to any Compton camera geometry. It is thus expected to represent an important step in the optimization of Compton camera imaging.

Abstract: PANDA (anti-proton annihiliation at Darmstadt) is planned to be one of the four main experiments at the future international accelerator complex FAIR (Facility for Antiproton and Ion Research) in Darmstadt, Germany. It is going to address fundamental questions of hadron physics and quantum chromodynamics using cooled antiproton beams with a high intensity and and momenta between 1.5 and 15 GeV/c. PANDA is designed to reach a maximum luminosity of 2 x 10(32) cm(-2) s. Most of the physics programs require an excellent particle identification (PID). The PID of hadronic states at the forward endcap of the target spectrometer will be done by a fast and compact Cherenkov detector that uses the detection of internally reflected Cherenkov light (DIRC) principle. It is designed to cover the polar angle range from 5 degrees to 22 degrees and to provide a separation power for the separation of charged pions and kaons up to 3 standard deviations (s.d.) for particle momenta up to 4 GeV/c in order to cover the important particle phase space. This document describes the technical design and the expected performance of the novel PANDA disc DIRC detector that has not been used in any other high energy physics experiment before. The performance has been studied with Monte-Carlo simulations and various beam tests at DESY and CERN. The final design meets all PANDA requirements and guarantees sufficient safety margins.

Abstract: Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures.

Abstract: Primordial black holes (PBHs) are compact objects proposed to have formed in the early Universe from the collapse of small-scale over-densities. Their existence may be detected from the observation of gravitational waves (GWs) emitted by PBH mergers, if the signals can be distinguished from those produced by the merging of astrophysical black holes. In this work, we forecast the capability of the Einstein Telescope, a proposed third-generation GW observatory, to identify and measure the abundance of a subdominant population of distant PBHs, using the difference in the redshift evolution of the merger rate of the two populations as our discriminant. We carefully model the merger rates and generate realistic mock catalogues of the luminosity distances and errors that would be obtained from GW signals observed by the Einstein Telescope. We use two independent statistical methods to analyse the mock data, finding that, with our more powerful, likelihood-based method, PBH abundances as small as fPBH approximate to 7 x 10(-6) ( fPBH approximate to 2 x 10(-6)) would be distinguishable from f(PBH) = 0 at the level of 3 sigma with a one year (ten year) observing run of the Einstein Telescope. Our mock data generation code, darksirens, is fast, easily extendable and publicly available on GitLab.

Abstract: Prompted by the recent Dresden-II reactor data, we examine its implications for the determination of the weak mixing angle, paying attention to the effect of the quenching function. We also determine the resulting constraints on the unitarity of the neutrino mixing matrix, as well as on the most general type of nonstandard neutral-current neutrino interactions.