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In this paper we employ the Renormalization Group (RG) method to study higher order corrections to the long-time asymptotics of a class of nonlinear integral equations with a generalized heat kernel and with time-dependent coefficients. This is a follow up of the papers \cite{bib:souz-brag-more-art,bib:souz-brag-more-marginal,bib:brag-more-silv}.

The burgeoning interest within the space community in digital beamforming is largely attributable to the superior flexibility that satellites with active antenna systems offer for a wide range of applications, notably in communication services. This paper delves into the analysis and practical implementation of a Digital Beamforming and Digital Down Conversion (DDC) chain, leveraging a high-speed Analog-to-Digital Converter (ADC) certified for space applications alongside a high-performance Field-Programmable Gate Array (FPGA). The proposed design strategy focuses on optimizing resource efficiency and minimizing power consumption by strategically sequencing the beamformer processor ahead of the complex down-conversion operation. This innovative approach entails the application of demodulation and low-pass filtering exclusively to the aggregated beam channel, culminating in a marked reduction in the requisite digital signal processing resources relative to traditional, more resource-intensive digital beamforming and DDC architectures. In the experimental validation, an evaluation board integrating a high-speed ADC and a FPGA was utilized. This setup facilitated the empirical validation of the design's efficacy by applying various RF input signals to the digital beamforming receiver system. The ADC employed is capable of high-resolution signal processing, while the FPGA provides the necessary computational flexibility and speed for real-time digital signal processing tasks. The findings underscore the potential of this design to significantly enhance the efficiency and performance of digital beamforming systems in space applications.

The ability to collect statistics about the execution of a program within a CPU is of the utmost importance across all fields of computing since it allows characterizing the timing performance of a program. This capability is even more relevant in safety-critical software systems, where it is mandatory to analyze software timing requirements to ensure the correct operation of the programs. Moreover, in order to properly evaluate and verify the extra-functional properties of these systems, besides timing performance, there are many other statistics available on a CPU, such as those associated with resource utilization. In this paper, we showcase a Performance Measurement Unit, also known as Hardware Performance Monitor, integrated into a RISC-V On-Board Computer designed for space applications by our research group. The monitoring technique features a novel approach whereby the events triggered are not counted immediately but instead are propagated through the pipeline so that their annotation is synchronized with the executed instruction. Additionally, we demonstrate the use of this PMU in a process to characterize the execution model of the processor. Finally, as an example of the statistics provided by the PMU, the results obtained running the CoreMark and Dhrystone benchmarks on the RISC-V OBC are shown.

Jet measurements in heavy ion collisions at low jet momentum can provide constraints on the properties of the quark gluon plasma but are overwhelmed by a significant, fluctuating background. We build upon our previous work which demonstrated the ability of the jet multiplicity method to extend jet measurements into the domain of low jet momentum [1, Mengel:2023]. We extend this method to a wide range of jet resolution parameters. We investigate the over-complexity of non-interpretable machine learning used to tackle the problem of jet background subtraction through network optimization. Finally, we show that the resulting shallow neural network is able to learn the underlying relationship between jet multiplicity and background fluctuations, with a lesser complexity, reinforcing the utility of interpretable methods., Comment: 14 pages including references. 10 figures. 1 appendix

The Universe's assumed homogeneity and isotropy is known as the cosmological principle. It is one of the assumptions that lead to the Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) metric and it is a cornerstone of modern cosmology, because the metric plays a crucial role into the determination of the cosmological observables. Thus, it is of paramount importance to question this principle and perform observational tests that may falsify this hypothesis. Here we explore the use of galaxy cluster counts as a probe of a large-scale inhomogeneity, which is a novel approach for the study of inhomogeneous models, and to determine the precision with which future galaxy cluster surveys will be able to test the cosmological principle. We present forecast constraints on the inhomogeneous Lema\^{\i}tre-Tolman-Bondi (LTB) model with a cosmological constant and cold dark matter, from a combination of simulated data according to a compilation of `Stage-IV' galaxy surveys following a methodology that involves the use of a mass function correction from numerical $N$-body simulations of an LTB cosmology. When considering the \lcdm fiducial model as a baseline for constructing our mock catalogs, we find that our combination of the forthcoming cluster surveys, will improve the constraints on the cosmological principle parameters as well on the FLRW parameters by about $50\%$ with respect to previous similar forecasts performed using geometrical and linear growth of structure probes, with $\pm20\%$ variations depending on the level of knowledge of systematic effects.These results indicate that galaxy cluster abundances are sensitive probes of inhomogeneity, and that next-generation galaxy cluster surveys, will thoroughly test homogeneity at cosmological scales, tightening the constraints on possible violations of the cosmological principle in the framework of $\Lambda$LTB scenarios. (Abridged), Comment: 9 pages, 5 figure, accepted as is and published in A&A journal after adding Fig. 1 for illustrative purposes

N-body simulations are the most powerful method to study the non-linear evolution of large-scale structure. However, they require large amounts of computational resources, making unfeasible their direct adoption in scenarios that require broad explorations of parameter spaces. In this work, we show that it is possible to perform fast dark matter density field emulations with competitive accuracy using simple machine-learning approaches. We build an emulator based on dimensionality reduction and machine learning regression combining simple Principal Component Analysis and supervised learning methods. For the estimations with a single free parameter, we train on the dark matter density parameter, $\Omega_m$, while for emulations with two free parameters, we train on a range of $\Omega_m$ and redshift. The method first adopts a projection of a grid of simulations on a given basis; then, a machine learning regression is trained on this projected grid. Finally, new density cubes for different cosmological parameters can be estimated without relying directly on new N-body simulations by predicting and de-projecting the basis coefficients. We show that the proposed emulator can generate density cubes at non-linear cosmological scales with density distributions within a few percent compared to the corresponding N-body simulations. The method enables gains of three orders of magnitude in CPU run times compared to performing a full N-body simulation while reproducing the power spectrum and bispectrum within $\sim 1\%$ and $\sim 3\%$, respectively, for the single free parameter emulation and $\sim 5\%$ and $\sim 15\%$ for two free parameters. This can significantly accelerate the generation of density cubes for a wide variety of cosmological models, opening the doors to previously unfeasible applications, such as parameter and model inferences at full survey scales as the ESA/NASA Euclid mission., Comment: 10 pages, 6 figures. To be submitted to A&A. Comments are welcome!

Jet measurements in heavy ion collisions can provide constraints on the properties of the quark gluon plasma, but the kinematic reach is limited by a large, fluctuating background. We present a novel application of symbolic regression to extract a functional representation of a deep neural network trained to subtract the background for measurements of jets in relativistic heavy ion collisions. We show that the deep neural network is approximately the same as a method using the particle multiplicity in a jet. This demonstrates that interpretable machine learning methods can provide insight into underlying physical processes.

The comparison of experimental data and theoretical predictions is important for our understanding of the mechanisms for interactions and particle production in hadron collisions, both at the Large Hadron Collider and at the Relativistic Heavy-Ion Collider experiments. Several tools were ideated to help with that. Rivet (Robust Independent Validation of Experiment and Theory) is a framework that facilitates the comparison between measurements from high-energy physics experiments and Monte Carlo event generators able to produce outputs using the HepMC package. Rivet contains a repository with analysis algorithms developed by experiments, providing analysis documentation and preservation. The recent development of features for the implementation of heavy-ion collision analyses, such as centrality determination, will be presented in this contribution, together with some of the open points still to be addressed., Comment: Proceedings of 37th Winter Workshop on Nuclear Dynamics. March 2022, Puerto Vallarta, Mexico

We study the large-time behavior of solutions to a generalized Burgers Equation, with initial zero mass data. Our main purpose is to present a modified version of the Renormalization Group map, which is able to provide the higher order asymptotic properties of the solution to the Cauchy problem of a class of nonlinear time-evolution problems., Comment: 25 pages

The Laser Interferometer Space Antenna (LISA) has the potential to reveal wonders about the fundamental theory of nature at play in the extreme gravity regime, where the gravitational interaction is both strong and dynamical. In this white paper, the Fundamental Physics Working Group of the LISA Consortium summarizes the current topics in fundamental physics where LISA observations of GWs can be expected to provide key input. We provide the briefest of reviews to then delineate avenues for future research directions and to discuss connections between this working group, other working groups and the consortium work package teams. These connections must be developed for LISA to live up to its science potential in these areas., Comment: Accepted in: Living Reviews in Relativity

The Laser Interferometer Space Antenna (LISA) has two scientific objectives of cosmological focus: to probe the expansion rate of the universe, and to understand stochastic gravitational-wave backgrounds and their implications for early universe and particle physics, from the MeV to the Planck scale. However, the range of potential cosmological applications of gravitational wave observations extends well beyond these two objectives. This publication presents a summary of the state of the art in LISA cosmology, theory and methods, and identifies new opportunities to use gravitational wave observations by LISA to probe the universe.

Sky survey telescopes and powerful targeted telescopes play complementary roles in astronomy. In order to investigate the nature and characteristics of the motions of very faint objects, a flexibly-pointed instrument capable of high astrometric accuracy is an ideal complement to current astrometric surveys and a unique tool for precision astrophysics. Such a space-based mission will push the frontier of precision astrometry from evidence of Earth-mass habitable worlds around the nearest stars, to distant Milky Way objects, and out to the Local Group of galaxies. As we enter the era of the James Webb Space Telescope and the new ground-based, adaptive-optics-enabled giant telescopes, by obtaining these high precision measurements on key objects that Gaia could not reach, a mission that focuses on high precision astrometry science can consolidate our theoretical understanding of the local Universe, enable extrapolation of physical processes to remote redshifts, and derive a much more consistent picture of cosmological evolution and the likely fate of our cosmos. Already several missions have been proposed to address the science case of faint objects in motion using high precision astrometry missions: NEAT proposed for the ESA M3 opportunity, micro-NEAT for the S1 opportunity, and Theia for the M4 and M5 opportunities. Additional new mission configurations adapted with technological innovations could be envisioned to pursue accurate measurements of these extremely small motions. The goal of this White Paper is to address the fundamental science questions that are at stake when we focus on the motions of faint sky objects and to briefly review instrumentation and mission profiles., Comment: arXiv admin note: substantial text overlap with arXiv:1910.08028, arXiv:1707.01348