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   https://doi.org/10.1051/0004-6361/201833302  

   Martínez-Delgado, David; Grebel, Eva K.; Javanmardi, Behnam; Boschin, Walter; Longeard, Nicolas; Carballo-Bello, Julio A.; Makarov, Dmitry; Beasley, Michael A.; Donatiello, Giuseppe; Haynes, Martha P.; et al (December 2018, Astronomy & Astrophysics)
2. A Census of Dwarf Galaxy Satellites around LMC-mass Galaxy NGC 2403*

   https://doi.org/10.3847/1538-4357/ad8dcd  

   Carlin, Jeffrey_L; Sand, David_J; Mutlu-Pakdil, Burçin; Crnojević, Denija; Doliva-Dolinsky, Amandine; Garling, Christopher_T; Peter, Annika_H_G; Brodie, Jean_P; Forbes, Duncan_A; Hargis, Jonathan_R; et al (December 2024, The Astrophysical Journal)

   Abstract We present the first comprehensive census of the satellite population around a Large Magellanic Cloud stellar-mass galaxy, as part of the Magellanic Analog Dwarf Companions and Stellar Halos (MADCASH) survey. We have surveyed NGC 2403 (D= 3.0 Mpc) with the Subaru/Hyper Suprime-Cam imager out to a projected radius of 90 kpc (with partial coverage extending out to ∼110 kpc, or ∼80% of the virial radius of NGC 2403), resolving stars in the uppermost ∼2.5 mag of its red giant branch. By looking for stellar overdensities in the red giant branch spatial density map, we identify 149 satellite candidates, of which only the previously discovered MADCASH J074238+65201-dw is a bona fide dwarf, together with the more massive and disrupting satellite DDO 44. We carefully assess the completeness of our search via injection of artificial dwarf galaxies into the images, finding that we are reliably sensitive to candidates down toM_V∼ −7.5 mag (and somewhat sensitive to even fainter satellites). A comparison of the satellite luminosity function of NGC 2403 down to this magnitude limit to theoretical expectations shows overall good agreement. This is the first of a full sample of 11 Magellanic Cloud–mass host galaxies we will analyze, creating a statistical sample that will provide the first quantitative constraints on hierarchical models of galaxy formation around low-mass hosts. 

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3. Accretion of galaxy groups into galaxy clusters

   https://doi.org/10.1093/mnras/staa2636  

   Benavides, José A; Sales, Laura V; Abadi, Mario G (September 2020, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT We study the role of group infall in the assembly and dynamics of galaxy clusters in ΛCDM. We select 10 clusters with virial mass M200 ∼ 1014 $$\rm M_\odot$$ from the cosmological hydrodynamical simulation Illustris and follow their galaxies with stellar mass M⋆ ≥ 1.5 × 108 $$\rm M_\odot$$. A median of $${\sim}38{{\ \rm per\ cent}}$$ of surviving galaxies at z = 0 is accreted as part of groups and did not infall directly from the field, albeit with significant cluster-to-cluster scatter. The evolution of these galaxy associations is quick, with observational signatures of their common origin eroding rapidly in 1–3 Gyr after infall. Substructure plays a dominant role in fostering the conditions for galaxy mergers to happen, even within the cluster environment. Integrated over time, we identify (per cluster) an average of 17 ± 9 mergers that occur in infalling galaxy associations, of which 7 ± 3 occur well within the virial radius of their cluster hosts. The number of mergers shows large dispersion from cluster to cluster, with our most massive system having 42 mergers above our mass cut-off. These mergers, which are typically gas rich for dwarfs and a combination of gas rich and gas poor for M⋆ ∼ 1011 $$\rm M_\odot$$, may contribute significantly within ΛCDM to the formation of specific morphologies, such as lenticulars (S0) and blue compact dwarfs in groups and clusters. 

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4. Galaxy Helm chart: a standardized method for deploying production Galaxy servers

   https://doi.org/10.1093/bioinformatics/btae486  

   Goonasekera, Nuwan; Mahmoud, Alexandru; Suderman, Keith; Afgan, Enis; Martelli, ed., Pier_Luigi (August 2024, Bioinformatics)

   Abstract MotivationThe Galaxy application is a popular open-source framework for data intensive sciences, counting thousands of monthly users across more than 100 public servers. To support a growing number of users and a greater variety of use cases, the complexity of a production-grade Galaxy installation has also grown, requiring more administration effort. There is a need for a rapid and reproducible Galaxy deployment method that can be maintained at high-availability with minimal maintenance. ResultsWe describe the Galaxy Helm chart that codifies all elements of a production-grade Galaxy installation into a single package. Deployable on Kubernetes clusters, the chart encapsulates supporting software services and implements the best-practices model for running Galaxy. It is also the most rapid method available for deploying a scalable, production-grade Galaxy instance on one’s own infrastructure. The chart is highly configurable, allowing systems administrators to swap dependent services if desired. Notable uses of the chart include on-demand, fully-automated deployments on AnVIL, providing training infrastructure for the Bioconductor project, and as the AWS-recommended solution for running Galaxy on the Amazon cloud. Availability and implementationThe source code for Galaxy Helm is available at https://github.com/galaxyproject/galaxy-helm, the corresponding Helm package at https://github.com/CloudVE/helm-charts, and the required Galaxy container image https://github.com/galaxyproject/galaxy-docker-k8s. 

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5. Exploiting non-linear scales in galaxy–galaxy lensing and galaxy clustering: A forecast for the dark energy survey

   https://doi.org/10.1093/mnras/stab3793  

   Salcedo, Andrés N.; Weinberg, David H.; Wu, Hao-Yi; Wibking, Benjamin D. (January 2022, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT The combination of galaxy–galaxy lensing (GGL) and galaxy clustering is a powerful probe of low-redshift matter clustering, especially if it is extended to the non-linear regime. To this end, we use an N-body and halo occupation distribution (HOD) emulator method to model the redMaGiC sample of colour-selected passive galaxies in the Dark Energy Survey (DES), adding parameters that describe central galaxy incompleteness, galaxy assembly bias, and a scale-independent multiplicative lensing bias Alens. We use this emulator to forecast cosmological constraints attainable from the GGL surface density profile ΔΣ(rp) and the projected galaxy correlation function wp, gg(rp) in the final (Year 6) DES data set over scales $$r_p=0.3\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$$. For a $$3{{\ \rm per\ cent}}$$ prior on Alens we forecast precisions of $$1.9{{\ \rm per\ cent}}$$, $$2.0{{\ \rm per\ cent}}$$, and $$1.9{{\ \rm per\ cent}}$$ on Ωm, σ8, and $$S_8 \equiv \sigma _8\Omega _m^{0.5}$$, marginalized over all halo occupation distribution (HOD) parameters as well as Alens. Adding scales $$r_p=0.3\!-\!3.0\, h^{-1} \, \mathrm{Mpc}$$ improves the S8 precision by a factor of ∼1.6 relative to a large scale ($$3.0\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$$) analysis, equivalent to increasing the survey area by a factor of ∼2.6. Sharpening the Alens prior to $$1{{\ \rm per\ cent}}$$ further improves the S8 precision to $$1.1{{\ \rm per\ cent}}$$, and it amplifies the gain from including non-linear scales. Our emulator achieves per cent-level accuracy similar to the projected DES statistical uncertainties, demonstrating the feasibility of a fully non-linear analysis. Obtaining precise parameter constraints from multiple galaxy types and from measurements that span linear and non-linear clustering offers many opportunities for internal cross-checks, which can diagnose systematics and demonstrate the robustness of cosmological results. 

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