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Paterno, M.

Publications and source records attributed to Paterno, M..

Constraints on f ( R ) gravity from thermal-Sunyaev-Zel’dovich-effect-selected SPT galaxy clusters and weak lensing mass calibration from DES and HST

We present constraints on the f ( R ) gravity model using a sample of 1005 galaxy clusters in the redshift range 0.25–1.78 that have been selected through the thermal Sunyaev-Zel’dovich effect from South Pole Telescope data and subjected to optical and near-infrared confirmation with the multicomponent matched filter algorithm. We employ weak gravitational lensing mass calibration from the Dark Energy Survey Year 3 data for 688 clusters at z < 0.95 and from the Hubble Space Telescope for 39 clusters with 0.6 < z < 1.7 . Our cluster sample is a powerful probe of f ( R ) gravity, because this model predicts a scale-dependent enhancement in the growth of structure, which impacts the halo mass function (HMF) at cluster mass scales. To account for these modified gravity effects on the HMF, our analysis employs a semianalytical approach calibrated with numerical simulations. Combining calibrated cluster counts with primary cosmic microwave background temperature and polarization anisotropy measurements from the Planck 2018 release, we derive robust constraints on the f ( R ) parameter f R 0 . Our results, log 10 | f R 0 | < − 5.32 at the 95% credible level, are the tightest current constraints on f ( R ) gravity from cosmological scales. This upper limit rules out f ( R ) -like deviations from general relativity that result in more than a ∼ 20 % enhancement of the cluster population on mass scales M 200 c > 3 × 10 14 M ⊙ . Published by the American Physical Society 2025

79 ASTRONOMY AND ASTROPHYSICS↗

Dark Energy Survey Year 3: Blue Shear

Modeling the intrinsic alignment (IA) of galaxies poses a challenge to weak lensing analyses. The Dark Energy Survey is expected to be less impacted by IA when limited to blue, star-forming galaxies. The cosmological parameter constraints from this blue cosmic shear sample are stable to IA model choice, unlike passive galaxies in the full DES Y3 sample, the goodness-of-fit is improved and the $\Omega_{m}$ and $S_8$ better agree with the cosmic microwave background. Mitigating IA with sample selection, instead of flexible model choices, can reduce uncertainty in $S_8$ by a factor of 1.5.

79 ASTRONOMY AND ASTROPHYSICS↗

Building an Efficient Cluster Cosmology Software Package for Modeling Cluster Counts and Lensing

We introduce a software suite developed for galaxy cluster cosmological analysis with the Dark Energy Survey Data. Cosmological analyses based on galaxy cluster number counts and weak-lensing measurements need efficient software infrastructure to explore an increasingly large parameter space, and account for various cosmological and astrophysical effects. Our software package is designed to model the cluster observables in a wide-field optical survey, including galaxy cluster counts, their averaged weak-lensing masses, or the cluster's averaged weak-lensing radial signals. To ensure maximum efficiency, this software package is developed in C++ in the CosmoSIS software framework, making use of the CUBA integration library. We also implement a testing and validation scheme to ensure the quality of the package. We demonstrate the effectiveness of this development by applying the software to the Dark Energy Survey Year 1 galaxy cluster cosmological data sets, and acquired cosmological constraints that are consistent with the fiducial Dark Energy Survey analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

The catalog-to-cosmology framework for weak lensing and galaxy clustering for LSST

We present TXPipe, a modular, automated and reproducible pipeline for ingesting catalog data and performing all the calculations required to obtain quality-assured two-point measurements of lensing and clustering, and their covariances, with the metadata necessary for parameter estimation. The pipeline is developed within the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC), and designed for cosmology analyses using LSST data. In this paper, we present the pipeline for the so-called 3x2pt analysis -- a combination of three two-point functions that measure the auto- and cross-correlation between galaxy density and shapes. We perform the analysis both in real and harmonic space using TXPipe and other LSST-DESC tools. We validate the pipeline using Gaussian simulations and show that it accurately measures data vectors and recovers the input cosmology to the accuracy level required for the first year of LSST data under this simplified scenario. We also apply the pipeline to a realistic mock galaxy sample extracted from the CosmoDC2 simulation suite (Korytov et al. 2019). TXPipe establishes a baseline framework that can be built upon as the LSST survey proceeds. Furthermore, the pipeline is designed to be easily extended to science probes beyond the 3x2pt analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

Python and HPC for High Energy Physics Data Analyses

High level abstractions in Python that can utilize computing hardware well seem to be an attractive option for writing data reduction and analysis tasks. In this paper, we explore the features available in Python which are useful and efficient for end user analysis in High Energy Physics (HEP). A typical vertical slice of an HEP data analysis is somewhat fragmented: the state of the reduction/analysis process must be saved at certain stages to allow for selective reprocessing of only parts of a generally time-consuming workflow. Also, algorithms tend to to be modular because of the heterogeneous nature of most detectors and the need to analyze different parts of the detector separately before combining the information. This fragmentation causes difficulties for interactive data analysis, and as data sets increase in size and complexity (O10 TiB for a “small” neutrino experiment to the O10 PiB currently held by the CMS experiment at the LHC), data analysis methods traditional to the field must evolve to make optimum use of emerging HPC technologies and platforms. Mainstream big data tools, while suggesting a direction in terms of what can be done if an entire data set can be available across a system and analysed with high-level programming abstractions, are not designed with either scientific computing generally, or modern HPC platform features in particular, such as data caching levels, in mind. Our example HPC use case is a search for a new elementary particle which might explain the phenomenon known as “Dark Matter”. Here, using data from the CMS detector, we will use HDF5 as our input data format, and MPI with Python to implement our use case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

CosmoSIS: A system for MC parameter estimation

CosmoSIS is a modular system for cosmological parameter estimation, based on Markov Chain Monte Carlo and related techniques. It provides a series of samplers, which drive the exploration of the parameter space, and a series of modules, which calculate the likelihood of the observed data for a given physical model, determined by the location of a sample in the parameter space. While CosmoSIS ships with a set of modules that calculate quantities of interest to cosmologists, there is nothing about the framework itself, nor in the Markov Chain Monte Carlo technique, that is specific to cosmology. Thus CosmoSIS could be used for parameter estimation problems in other fields, including HEP. This paper describes the features of CosmoSIS and show an example of its use outside of cosmology. Furthermore, it also discusses how collaborative development strategies differ between two different communities: that of HEP physicists, accustomed to working in large collaborations, and that of cosmologists, who have traditionally not worked in large groups.

97 MATHEMATICS AND COMPUTING↗

CosmoSIS: Modular cosmological parameter estimation

Cosmological parameter estimation is entering a new era. Large collaborations need to coordinate high-stakes analyses using multiple methods; furthermore such analyses have grown in complexity due to sophisticated models of cosmology and systematic uncertainties. In this paper we argue that modularity is the key to addressing these challenges: calculations should be broken up into interchangeable modular units with inputs and outputs clearly defined. Here we present a new framework for cosmological parameter estimation, CosmoSIS, designed to connect together, share, and advance development of inference tools across the community. We describe the modules already available in CosmoSIS, including CAMB, Planck, cosmic shear calculations, and a suite of samplers. Lastly, we illustrate it using demonstration code that you can run out-of-the-box with the installer available at http://bitbucket.org/joezuntz/cosmosis

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

High Energy Electromagnetic Particle Transportation on the GPU

We present massively parallel high energy electromagnetic particle transportation through a finely segmented detector on a Graphics Processing Unit (GPU). Simulating events of energetic particle decay in a general-purpose high energy physics (HEP) detector requires intensive computing resources, due to the complexity of the geometry as well as physics processes applied to particles copiously produced by primary collisions and secondary interactions. The recent advent of hardware architectures of many-core or accelerated processors provides the variety of concurrent programming models applicable not only for the high performance parallel computing, but also for the conventional computing intensive application such as the HEP detector simulation. The components of our prototype are a transportation process under a non-uniform magnetic field, geometry navigation with a set of solid shapes and materials, electromagnetic physics processes for electrons and photons, and an interface to a framework that dispatches bundles of tracks in a highly vectorized manner optimizing for spatial locality and throughput. Core algorithms and methods are excerpted from the Geant4 toolkit, and are modified and optimized for the GPU application. Program kernels written in C/C++ are designed to be compatible with CUDA and OpenCL and with the aim to be generic enough for easy porting to future programming models and hardware architectures. To improve throughput by overlapping data transfers with kernel execution, multiple CUDA streams are used. Issues with floating point accuracy, random numbers generation, data structure, kernel divergences and register spills are also considered. Performance evaluation for the relative speedup compared to the corresponding sequential execution on CPU is presented as well.

Canal, P.↗

Improving robustness and computational efficiency using modern C++

For nearly two decades, the C++ programming language has been the dominant programming language for experimental HEP. The publication of ISO/IEC 14882:2011, the current version of the international standard for the C++ programming language, makes available a variety of language and library facilities for improving the robustness, expressiveness, and computational efficiency of C++ code. However, much of the C++ written by the experimental HEP community does not take advantage of the features of the language to obtain these benefits, either due to lack of familiarity with these features or concern that these features must somehow be computationally inefficient. In this paper, we address some of the features of modern C+-+, and show how they can be used to make programs that are both robust and computationally efficient. We compare and contrast simple yet realistic examples of some common implementation patterns in C, currently-typical C++, and modern C++, and show (when necessary, down to the level of generated assembly language code) the quality of the executable code produced by recent C++ compilers, with the aim of allowing the HEP community to make informed decisions on the costs and benefits of the use of modern C++.

Paterno, M.↗

Noto Station Status Report

The Noto VLBI station was fully operational in 2012, and the upgrade projects could be restarted, involving mainly the receiver area. Another important improvement was the activation of the 1 Gbps network.

Tuccari, G.↗