We investigate the dependence of stellar population properties of galaxies on group dynamical stage for a subsample of Yang catalog. We classify groups according to their galaxy velocity distribution into Gaussian (G) and Non-Gaussian (NG). Using two totally independent approaches we have shown that our measurement of Gaussianity is robust and reliable. Our sample covers Yang's groups in the redshift range 0.03 ≤ z ≤ 0.1 having mass ≥ 10 14 M . The new method, Hellinger Distance (HD), to determine whether a group has a velocity distribution Gaussian or Non-Gaussian is very effective in distinguishing between the two families. NG groups present halo masses higher than the G ones, confirming previous findings. Examining the Skewness and Kurtosis of the velocity distribution of G and NG groups, we find that faint galaxies in NG groups are mainly infalling for the first time into the groups. We show that considering only faint galaxies in the outskirts, those in NG groups are older and more metal rich than the ones in G groups. Also, examining the Projected Phase Space of cluster galaxies we see that bright and faint galactic systems in G groups are in dynamical equilibrium which does not seem to be the case in NG groups. These findings suggest that NG systems have a higher infall rate, assembling more galaxies which experienced preprocessing before entering the group.

Gradient pattern analysis (GPA) is a well-established technique for measuring gradient bilateral asymmetries of a square numerical lattice. This paper introduces an improved version of GPA designed for galaxy morphometry. We show the performance of the new method on a selected sample of 54,896 objects from the SDSS-DR7 in common with Galaxy Zoo 1 catalog. The results suggest that the second gradient moment, G 2 , has the potential to dramatically improve over more conventional morphometric parameters. It separates early from late type galaxies better (∼ 90%) than the CAS system (C ∼ 79%, A ∼ 50%, S ∼ 43%) and a benchmark test shows that it is applicable to hundreds of thousands of galaxies using typical processing systems.

While the density profiles (DPs) of ΛCDM haloes obey the NFW law out to roughly one virial radius, r vir , the structure of their outer parts is still poorly understood, because the 1-halo term describing the halo itself is dominated by the 2-halo term representing the other halos picked up. Using a semi-analytical model, we measure the real-space 1-halo number DP of groups out to 20 r vir by assigning each galaxy to its nearest group above mass M a , in units of the group r vir . If M a is small (large), the outer DP of groups falls rapidly (slowly). We find that there is an optimal M a for which the stacked DP resembles the NFW model to 0.1 dex accuracy out to 13 virial radii. We find similar long-range NFW surface DPs (out to 10 r vir ) in the SDSS observations using a galaxy assignment scheme that combines the non-linear virialized regions of groups with their linear outer parts. The optimal M a scales as the minimum mass of the groups that are stacked to a power 0.25-0.3. The NFW model thus does not solely originate from violent relaxation. Moreover, populating haloes with galaxies using HOD models must proceed out to much larger radii than usually done.

No abstract

The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) is taking data at the Angra 2 nuclear reactor with the aim of detecting the coherent elastic scattering of reactor antineutrinos with silicon nuclei using charge-coupled devices (CCDs). In 2019 the experiment operated with a hardware binning applied to the readout stage, leading to lower levels of readout noise and improving the detection threshold down to 50 eV. The results of the analysis of 2019 data are reported here, corresponding to the detector array of 8 CCDs with a fiducial mass of 36.2 g and a total exposure of 2.2 kg-days. The difference between the reactor-on and reactor-off spectra shows no excess at low energies and yields upper limits at 95% confidence level for the neutrino interaction rates. In the lowest-energy range, 50 − 180 eV, the expected limit stands at 34 (39) times the standard model prediction, while the observed limit is 66 (75) times the standard model prediction with Sarkis (Chavarria) quenching factors.

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on 10^3 pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype.

No abstract

Dengue fever is an endemic disease, present in tropical and subtropical regions, transmitted by the Aedes Aegypti mosquito vector. It has recently appeared in non-tropical regions with dry weather. This represents a setback for advanced temperature-based reference models, since mosquitos reproductive cycle does not necessarily match with the outbreaks. This situation indicates that other variables are also involved in epidemic outbreaks. In this work we propose to include a component that capture this process, whether entomological, environmental or related to population mobility, and include it to the reference model by adding a Gaussian function to the formulation of humans (β h ) and vectors (βv) transmission rate. The parameters to be adjusted for this function were evaluated by a probabilistic model selection experiment. The parameters for this function are u, σ and k. The results indicate that, our model outperforms the reference model, and that additional information about outbreaks can be obtained from the new parameters. .