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High quality reconstructions of the three dimensional velocity and density fields of the local Universe are essential to study the local Large Scale Structure. In this paper, the Wiener Filter reconstruction technique is applied to galaxy radial peculiar velocity catalogs to understand how the Hubble constant (H0) value and the grouping scheme affect the reconstructions. While H0 is used to derive radial peculiar velocities from galaxy distance measurements and total velocities, the grouping scheme serves the purpose of removing non linear motions. Two different grouping schemes (based on the literature and a systematic algorithm) as well as five H0 values ranging from 72 to 76 km/s/Mpc are selected. The Wiener Filter is applied to the resulting catalogs. Whatever grouping scheme is used, the larger H0 is, the larger the infall onto the local Volume is. However, this conclusion has to be strongly mitigated: a bias minimization scheme applied to the catalogs after grouping suppresses this effect. At fixed H0, reconstructions obtained with catalogs grouped with the different schemes exhibit structures at the proper location in both cases but the latter are more contrasted in the less aggressive scheme case: having more constraints permits an infall from both sides onto the structures to reinforce their overdensity. Such findings highlight the importance of a balance between grouping to suppress non linear motions and preserving constraints to produce an infall onto structures expected to be large overdensities. Such an observation is promising to perform constrained simulations of the local Universe including its massive clusters.
We present a high performance solution to the Wiener filtering problem via a formulation that is dual to the recently developed messenger technique. This new dual messenger algorithm, like its predecessor, efficiently calculates the Wiener filter sol
Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps. However, the MAML objective is significantly more difficult to optimize comp
Comparison of the ISM properties of a wide range of metal-poor galaxies with normal metal-rich galaxies reveals striking differences. We find that the combination of the low dust abundance and the active star formation results in a very porous ISM fi
There is a longstanding discrepancy between the observed Galactic classical nova rate of $sim 10$ yr$^{-1}$ and the predicted rate from Galactic models of $sim 30$--50 yr$^{-1}$. One explanation for this discrepancy is that many novae are hidden by i
Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples from the source domain and unlabeled samples from the target domain. Classical UDA learning bounds show that target risk is upper bounded by three terms: sour