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Register a new userProbability Friends-of-Friends (PFOF) Group Finder: Performance Study and Observational Data Applications on Photometric Surveys
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(Abridged) In tandem with observational datasets, we utilize realistic mock catalogs, based on a semi-analytic galaxy formation model, constructed specifically for Pan-STARRS1 Medium Deep Surveys in order to assess the performance of the Probability Friends-of-Friends (PFOF, Liu et al.) group finder, and aim to develop a grouping optimization method applicable to surveys like Pan-STARRS1. Producing mock PFOF group catalogs under a variety of photometric redshift accuracies ({sigma}{Delta}z/(1+zs)), we find that catalog purities and completenesses from ``good {sigma}{Delta}z/(1+zs)) ~ 0.01) to ``poor {sigma}{Delta}z/(1+zs)) ~ 0.07) photo-zs gradually degrade respectively from 77% and 70% to 52% and 47%. To avoid model dependency of the mock for use on observational data we apply a ``subset optimization approach, using spectroscopic-redshift group data from the target field to train the group finder for application to that field, as an alternative method for the grouping optimization. We demonstrate this approach using these spectroscopically identified groups as the training set, i.e. zCOSMOS groups for PFOF searches within PS1 Medium Deep Field04 (PS1MD04) and DEEP2 EGS groups for searches in PS1MD07. We ultimately apply PFOF to four datasets spanning the photo-z uncertainty range from 0.01 to 0.06 in order to quantify the dependence of group recovery performance on photo-z accuracy. We find purities and completenesses calculated from observational datasets broadly agree with their mock analogues. Further tests of the PFOF algorithm are performed via matches to X-ray clusters identified within the PS1MD04 and COSMOS footprints. Across over a decade in group mass, we find PFOF groups match ~85% of X-ray clusters in COSMOS and PS1MD04, but at a lower statistical significance in the latter.
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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 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.
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
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.
We have performed a detailed analysis of the ability of the friends-of-friends algorithm in identifying real galaxy systems in deep surveys such as the future Javalambre Physics of the Accelerating Universe Astrophysical Survey. Our approach is two-fold, i.e., assessing the reliability of the algorithm in both real and redshift space. In the latter, our intention is also to determine the degree of accuracy that could be achieved when using spectroscopic or photometric redshift determinations as a distance indicator. We have built a light-cone mock catalogue using synthetic galaxies constructed from the Millennium Run Simulation I plus a semi-analytical model of galaxy formation. We have explored different ways to define the proper linking length parameters of the algorithm in order to perform an identification of galaxy groups as suitable as possible in each case. We find that, when identifying systems in redshift space using spectroscopic information, the linking lengths should take into account the variation of the luminosity function with redshift as well as the linear redshift dependence of the radial fiducial velocity in the line of sight direction. When testing purity and completeness of the group samples, we find that the best resulting group sample reaches values of 40% and 70% of systems with high levels of purity and completeness, respectively, when using spectroscopic information. When identifying systems using photometric redshifts, we adopted a probabilistic approach to link galaxies in the line of sight direction. Our result suggests that it is possible to identify a sample of groups with less than 40% false identification at the same time as we recover around 60% of the true groups. This modified version of the algorithm can be applied to deep surveys provided that the linking lengths are selected appropriately for the science to be done with the data.
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