English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We revisit the estimation of the combined mass of the Milky Way and Andromeda(M31), which dominate the mass of the Local Group. We make use of an ensembleof 30,190 halo pairs from the Small MultiDark simulation, assuming a$\Lambda$CDM (Cosmological Constant with Cold Dark Matter) cosmology, toinvestigate the relationship between the bound mass and parameterscharacterising the orbit of the binary and their local environment with the aidof machine learning methods (artificial neural networks, ANN). Results from theANN are most successful when information about the velocity shear is provided,which demonstrates the flexibility of machine learning to model physicalphenomena and readily incorporate new information as it becomes available. Theresulting estimate for the Local Group mass, when shear information isincluded, is $4.9 \times 10^{12} M_\odot$, with an error of $\pm0.8 \times10^{12} M_\odot$ from the 68% uncertainty in observables, and a 68% confidenceinterval of $^{+1.3}_{-1.4} \times 10^{12}M_\odot$ from the intrinsic scatterfrom the differences between the model and simulation masses. We also considera recently reported large transverse velocity of M31 relative to the Milky Way,and produce an alternative mass estimate of $3.6\pm0.3\pm1.4 \times10^{12}M_\odot$. Although different methods predict similar values for the mostlikely mass of the LG, application of ANN compared to the Timing Argumentreduces the scatter in the log mass by over half when tested on samples fromthe simulation.

Estimating the Mass of the Local Group using Machine Learning Applied to Numerical Simulations