arXiv subjects

C. Avestruz

Publications and source records attributed to C. Avestruz.

6 recordsLinked to original sources

arXiv2026

Intracluster Medium Fluctuations on Scales up to 1 Mpc: A Combined eROSITA and SPT/Planck Analysis of Abell 3266

Galaxy clusters form through hierarchical assembly, where smaller substructures merge to build the largest gravitationally bound objects in the universe. These mergers, combined with feedback from AGN, filamentary accretion, and other energy injection processes, generate turbulence and perturbations within the intra-cluster medium (ICM). X-ray and Sunyaev-Zel'dovich (SZ) observations can be utilized to measure these ICM density and pressure inhomogeneities, in turn providing constraints on the effective Equation of State (EOS) of the perturbations and ICM velocities. In this work, we analyze deep SRG-eROSITA and Planck/SPT observations of Abell 3266 (A3266), a dynamically complex merging cluster with elongated morphology and significant substructure. We measure pressure and density fluctuations, and compute the power spectra and deprojected 3D amplitudes of these perturbations. We estimate the ratio of pressure-to-density fluctuation amplitudes as $1.00 \pm 0.55$ and non-thermal pressure support $0.068 \pm 0.050$. Density fluctuations are found to be stronger in the northern sector of the cluster compared to the south, consistent with ongoing accretion along a filamentary structure revealed by eROSITA. Further, we find the amplitude of density fluctuations increases with radius, qualitatively consistent with the trend found in cosmological simulations. Uncertainties in our results are dominated by the relatively low sensitivity of current Planck/SPT data, suggesting that improvements in SZ data quality could substantially improve our understanding of ICM energy injection, transport, and dissipation from this technique.

astro-ph.CO↗

arXiv2023

Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.

astro-ph.IM↗

arXiv2021

CLMM: a LSST-DESC Cluster weak Lensing Mass Modeling library for cosmology

We present the v1.0 release of CLMM, an open source Python library for the estimation of the weak lensing masses of clusters of galaxies. CLMM is designed as a standalone toolkit of building blocks to enable end-to-end analysis pipeline validation for upcoming cluster cosmology analyses such as the ones that will be performed by the LSST-DESC. Its purpose is to serve as a flexible, easy-to-install and easy-to-use interface for both weak lensing simulators and observers and can be applied to real and mock data to study the systematics affecting weak lensing mass reconstruction. At the core of CLMM are routines to model the weak lensing shear signal given the underlying mass distribution of galaxy clusters and a set of data operations to prepare the corresponding data vectors. The theoretical predictions rely on existing software, used as backends in the code, that have been thoroughly tested and cross-checked. Combined, theoretical predictions and data can be used to constrain the mass distribution of galaxy clusters as demonstrated in a suite of example Jupyter Notebooks shipped with the software and also available in the extensive online documentation.

astro-ph.CO↗

arXiv2019

Response to NITRD, NCO, NSF Request for Information on "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan"

We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspective of Fermilab, America's premier national laboratory for High Energy Physics (HEP). We believe the NAIRDSP should be extended in light of the rapid pace of development and innovation in the field of Artificial Intelligence (AI) since 2016, and present our recommendations below. AI has profoundly impacted many areas of human life, promising to dramatically reshape society --- e.g., economy, education, science --- in the coming years. We are still early in this process. It is critical to invest now in this technology to ensure it is safe and deployed ethically. Science and society both have a strong need for accuracy, efficiency, transparency, and accountability in algorithms, making investments in scientific AI particularly valuable. Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership. Moreover, investments in AI will be important for maintaining US leadership in the physical sciences.

astro-ph.IM↗

arXiv2019

Unveiling the Galaxy Cluster - Cosmic Web Connection with X-ray observations in the Next Decade

In recent years, the outskirts of galaxy clusters have emerged as one of the new frontiers and unique laboratories for studying the growth of large scale structure in the universe. Modern cosmological hydrodynamical simulations make firm and testable predictions of the thermodynamic and chemical evolution of the X-ray emitting intracluster medium. However, recent X-ray and Sunyaev-Zeldovich effect observations have revealed enigmatic disagreements with theoretical predictions, which have motivated deeper investigations of a plethora of astrophysical processes operating in the virialization region in the cluster outskirts. Much of the physics of cluster outskirts is fundamentally different from that of cluster cores, which has been the main focus of X-ray cluster science over the past several decades. A next-generation X-ray telescope, equipped with sub-arcsecond spatial resolution over a large field of view along with a low and stable instrumental background, is required in order to reveal the full story of the growth of galaxy clusters and the cosmic web and their applications for cosmology.

astro-ph.CO↗

arXiv2018

Star-galaxy classification in the Dark Energy Survey Y1 dataset

We perform a comparison of different approaches to star-galaxy classification using the broad-band photometric data from Year 1 of the Dark Energy Survey. This is done by performing a wide range of tests with and without external `truth' information, which can be ported to other similar datasets. We make a broad evaluation of the performance of the classifiers in two science cases with DES data that are most affected by this systematic effect: large-scale structure and Milky Way studies. In general, even though the default morphological classifiers used for DES Y1 cosmology studies are sufficient to maintain a low level of systematic contamination from stellar mis-classification, contamination can be reduced to the O(1%) level by using multi-epoch and infrared information from external datasets. For Milky Way studies the stellar sample can be augmented by ~20% for a given flux limit. Reference catalogs used in this work will be made available upon publication.

astro-ph.IM↗