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Register a new userThe 2MASS redshift survey galaxy group catalogue derived from a graph-theory based friends-of-friends algorithm
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We present the galaxy group catalogue for the recently-completed 2MASS Redshift Survey (2MRS, Macri2019) which consists of 44572 redshifts, including 1041 new measurements for galaxies mostly located within the Zone of Avoidance. The galaxy group catalogue is generated by using a novel, graph-theory based, modified version of the Friends-of-Friends algorithm. Several graph-theory examples are presented throughout this paper, including a new method for identifying substructures within groups. The results and graph-theory methods have been thoroughly interrogated against previous 2MRS group catalogues and a Theoretical Astrophysical Observatory (TAO) mock by making use of cutting-edge visualization techniques including immersive facilities, a digital planetarium, and virtual reality. This has resulted in a stable and robust catalogue with on-sky positions and line-of-sight distances within 0.5 Mpc and 2 Mpc, respectively, and has recovered all major groups and clusters. The final catalogue consists of 3022 groups, resulting in the most complete whole-sky galaxy group catalogue to date. We determine the 3D positions of these groups, as well as their luminosity and comoving distances, observed and corrected number of members, richness metric, velocity dispersion, and estimates of $R_{200}$ and $M_{200}$. We present three additional data products, i.e. the 2MRS galaxies found in groups, a catalogue of subgroups, and a catalogue of 687 new group candidates with no counterparts in previous 2MRS-based analyses.
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We present a catalogue of galaxy groups and clusters selected using a friends-of-friends algorithm with a dynamic linking length from the 2dF-SDSS and QSO (2SLAQ) luminous red galaxy survey. The linking parameters for the code are chosen through an analysis of simulated 2SLAQ haloes. The resulting catalogue includes 313 clusters containing 1,152 galaxies. The galaxy groups and clusters have an average velocity dispersion of sigma_v = 467.97 km/s and an average size of R_clt = 0.78 Mpc/h. Galaxies from regions of one square degree and centred on the galaxy clusters were downloaded from the Sloan Digital Sky Survey Data Release 6 (SDSS DR6). Investigating the photometric redshifts and cluster red-sequence of these galaxies shows that the galaxy clusters detected with the FoF algorithm are reliable out to z~0.6. We estimate masses for the clusters using their velocity dispersions. These mass estimates are shown to be consistent with 2SLAQ mock halo masses. Further analysis of the simulation haloes shows that clipping out low richness groups with large radii improves the purity of catalogue from 52% to 88%, while retaining a completeness of 94%. Finally, we test the two-point correlation function of our cluster catalogue. We find a best-fitting power law model with parameters r0 = 24pm4 Mpc/h and gamma = -2.1pm 0.2, which are in agreement with other low redshift cluster samples and consistent with a {Lambda}CDM universe.
Context. Groups form the most abundant class of galaxy systems. They act as the principal drivers of galaxy evolution and can be used as tracers of the large-scale structure and the underlying cosmology. However, the detection of galaxy groups from galaxy redshift survey data is hampered by several observational limitations. Aims. We improve the widely used friends-of-friends (FoF) group finding algorithm with membership refinement procedures and apply the method to a combined dataset of galaxies in the local Universe. A major aim of the refinement is to detect subgroups within the FoF groups, enabling a more reliable suppression of the fingers-of-God effect. Methods. The FoF algorithm is often suspected of leaving subsystems of groups and clusters undetected. We used a galaxy sample built of the 2MRS, CF2, and 2M++ survey data comprising nearly 80000 galaxies within the local volume of 430 Mpc radius to detect FoF groups. We conducted a multimodality check on the detected groups in search for subgroups. We furthermore refined group membership using the group virial radius and escape velocity to expose unbound galaxies. We used the virial theorem to estimate group masses. Results. The analysis results in a catalogue of 6282 galaxy groups in the 2MRS sample with two or more members, together with their mass estimates. About half of the initial FoF groups with ten or more members were split into smaller systems with the multimodality check. An interesting comparison to our detected groups is provided by another group catalogue that is based on similar data but a completely different methodology. Two thirds of the groups are identical or very similar. Differences mostly concern the smallest and largest of these other groups, the former sometimes missing and the latter being divided into subsystems in our catalogue.
We adapt the friends of friends algorithm to the analysis of light curves, and show that it can be successfully applied to searches for transient phenomena in large photometric databases. As a test case we search OGLE-III light curves for known dwarf novae. A single combination of control parameters allows to narrow the search to 1% of the data while reaching a $sim$90% detection efficiency. A search involving $sim$2% of the data and three combinations of control parameters can be significantly more effective - in our case a 100% efficiency is reached. The method can also quite efficiently detect semi-regular or strictly periodic variability. We report 28 new variables found in the field of the globular cluster M22, which was examined earlier with the help of periodicity-searching algorithms
The Friends of Friends algorithm identifies groups of objects with similar spatial and kinematic properties, and has recently been used extensively to quantify the distributions of gas and stars in young star-forming regions. We apply the Friends of Friends algorithm to $N$-body simulations of the dynamical evolution of subvirial (collapsing) and supervirial (expanding) star-forming regions. We find that the algorithm picks out a wide range of groups (1 -- 25) for statistically identical initial conditions, and cannot distinguish between subvirial and supervirial regions in that we obtain similar mode and median values for the number of groups it identifies. We find no correlation between the number of groups identified initially and either the initial or subsequent spatial and kinematic tracers of the regions evolution, such as the amount of spatial substructure, dynamical mass segregation, or velocity dispersion. We therefore urge caution in using the Friends of Friends algorithm to quantify the initial conditions of star formation.
Barter exchange studies the setting where each agent owns a good, and they can exchange with each other if that gives them more preferred goods. This exchange will give better outcomes if there are more participants. The challenge here is how to get more participants and our goal is to incentivize the existing participants to invite new participants. However, new participants might be competitors for the existing participants. Therefore, we design an exchange mechanism based on the classical Top Trading Cycle (TTC) algorithm to solve their conflicts. Our mechanism is truthful in terms of revealing their preferences and also guarantees that inviting all their neighbors is a dominant strategy for all participants. The mechanism can be applied in settings where more participants are preferred but no extra budget to reach new participants.
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