What determines large scale galaxy clustering: halo mass or local density?

Using dark matter simulations we show how halo bias is determined by local density and not by halo mass. This is not totally surprising, as according to the peak-background split model, local density is the property that constraints bias at large scales. Massive haloes have a high clustering because they reside in high density regions. Small haloes can be found in a wide range of environments which determine their clustering amplitudes differently. This contradicts the assumption of standard Halo Occupation Distribution (HOD) models that the bias and occupation of haloes is determined solely by their mass. We show that the bias of central galaxies from semi-analytic models of galaxy formation as a function of luminosity and colour is not correctly predicted by the standard HOD model. Using local density instead of halo mass the HOD model correctly predicts galaxy bias. These results indicate the need to include information about local density and not only mass in order to correctly apply HOD analysis in these galaxy samples. This new model can be readily applied to observations and has the advantage that the galaxy density can be directly observed, in contrast with the dark matter halo mass.Comment: 11 pages, 9 figure

Measuring the growth of matter fluctuations with third-order galaxy correlations

Measurements of the linear growth factor $D$ at different redshifts $z$ are key to distinguish among cosmological models. One can estimate the derivative $dD(z)/d\ln(1+z)$ from redshift space measurements of the 3D anisotropic galaxy two-point correlation $\xi(z)$, but the degeneracy of its transverse (or projected) component with galaxy bias $b$, i.e. $\xi_{\perp}(z) \propto\ D^2(z) b^2(z)$, introduces large errors in the growth measurement. Here we present a comparison between two methods which break this degeneracy by combining second- and third-order statistics. One uses the shape of the reduced three-point correlation and the other a combination of third-order one- and two-point cumulants. These methods use the fact that, for Gaussian initial conditions and scales larger than $20$ $h^{-1}$Mpc, the reduced third-order matter correlations are independent of redshift (and therefore of the growth factor) while the third-order galaxy correlations depend on $b$. We use matter and halo catalogs from the MICE-GC simulation to test how well we can recover $b(z)$ and therefore $D(z)$ with these methods in 3D real space. We also present a new approach, which enables us to measure $D$ directly from the redshift evolution of second- and third-order galaxy correlations without the need of modelling matter correlations. For haloes with masses lower than $10^{14}$ $h^{-1}$M$_\odot$, we find $10$ deviations between the different estimates of $D$, which are comparable to current observational errors. At higher masses we find larger differences that can probably be attributed to the breakdown of the bias model and non-Poissonian shot noise.Comment: 24 pages, 20 figures, 2 tables, accepted for publication in MNRA

Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification

In this paper, we introduce a novel Synchronized Class Token Fusion (SCT Fusion) architecture in the framework of multi-modal multi-label classification (MLC) of remote sensing (RS) images. The proposed architecture leverages modality-specific attention-based transformer encoders to process varying input modalities, while exchanging information across modalities by synchronizing the special class tokens after each transformer encoder block. The synchronization involves fusing the class tokens with a trainable fusion transformation, resulting in a synchronized class token that contains information from all modalities. As the fusion transformation is trainable, it allows to reach an accurate representation of the shared features among different modalities. Experimental results show the effectiveness of the proposed architecture over single-modality architectures and an early fusion multi-modal architecture when evaluated on a multi-modal MLC dataset. The code of the proposed architecture is publicly available at https://git.tu-berlin.de/rsim/sct-fusion.Comment: Accepted at IEEE International Geoscience and Remote Sensing Symposium 202