GriSPy: A Python package for Fixed-Radius Nearest Neighbors Search

Chalela, Martín; Sillero, Emanuel; Pereyra, Luis; García, Mario Alejandro; Cabral, Juan B.; Lares, M.; Merchán, Manuel

doi:10.48550/arxiv.1912.09585

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…We also restrict our galaxy background sample by considering the galaxies with Z_BEST> 0.2 and up to 1.2 for all the shear catalogues, except for KiDS where a more restrictive cut is taken into account (Z_BEST< 0.9) according to the suggested by Hildebrandt 2 RCSLenS: https://www.cadc-ccda.hia-iha.nrccnrc.gc.ca/en/community/rcslens 3 KiDS-450: http://kids.strw.leidenuniv.nl/cosmicshear2018.php et al (2017). Background galaxies are assigned the to each group using the public regular grid search algorithm GRISPY 4 (Chalela et al 2019).…”

Section: Galaxy Background Selectionmentioning

confidence: 99%

On the weak lensing masses of a new sample of galaxy groups

Gonzalez,

Rodriguez,

Merchán

et al. 2021

Preprint

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Galaxy group masses are important to relate these systems with the dark matter halo hosts. However, deriving accurate mass estimates is particularly challenging for low-mass galaxy groups. Moreover, calibration of observational mass-proxies using weak-lensing estimates have been mainly focused on massive clusters. We present here a study of halo masses for a sample of galaxy groups identified according to a spectroscopic catalogue, spanning a wide mass range. The main motivation of our analysis is to assess mass estimates provided by the galaxy group catalogue derived through an abundance matching luminosity technique. We derive total halo mass estimates according to a stacking weak-lensing analysis. Our study allows to test the accuracy of mass estimates based on this technique as a proxy for the halo masses of large group samples. Lensing profiles are computed combining the groups in different bins of abundance matching mass, richness and redshift. Fitted lensing masses correlate with the masses obtained from abundance matching. However, when considering groups in the low-and intermediatemass ranges, masses computed according to the characteristic group luminosity tend to predict higher values than the determined by the weak-lensing analysis. The agreement improves for the low-mass range if the groups selected have a central early-type galaxy. Presented results validate the use of mass estimates based on abundance matching techniques which provide good proxies to the halo host mass in a wide mass range.

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Section: Galaxy Background Selectionmentioning

confidence: 99%

On the weak lensing masses of a new sample of galaxy groups

Gonzalez,

Rodriguez,

Merchán

et al. 2021

Preprint

Self Cite

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“…Therefore, the computational speed is accelerated if the algorithm is performed on a sub-sample of the feature vectors. To achieve better vector management, the fast searching algorithm proposed by Chalela et al [35] introduced a grid based searching paradigm. Another work [17] used the KD tree to manage the vectors and further accelerate the searching approach.…”

Section: Fast Mean-shift Algorithmsmentioning

confidence: 99%

Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking

Zhao,

Jha,

Liu

et al. 2020

Preprint

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Recently, single-stage embedding based deep learning algorithms gain increasing attention in cell segmentation and tracking. Compared with the traditional "segment-then-associate" two-stage approach, a single-stage algorithm not only simultaneously achieves consistent instance cell segmentation and tracking but also gains superior performance when distinguishing ambiguous pixels on boundaries and overlaps. However, the deployment of an embedding based algorithm is restricted by slow inference speed (e.g., ≈1-2 mins per frame). In this study, we propose a novel Faster Mean-shift algorithm, which tackles the computational bottleneck of embedding based cell segmentation and tracking. Different from previous GPU-accelerated fast mean-shift algorithms, a new online seed optimization policy (OSOP) is introduced to adaptively determine the minimal number of seeds, accelerate computation, and save GPU memory. With both embedding simulation and empirical validation via the four cohorts from the ISBI cell tracking challenge, the proposed Faster Mean-shift algorithm achieved 7-10 times speedup compared to the state-of-the-art embedding based cell instance segmentation and tracking algorithm. Our Faster Mean-shift algorithm also achieved the highest computational speed compared to other GPU benchmarks with optimized memory consumption. The Faster Meanshift is a plug-and-play model, which can be employed on other pixel embedding based clustering inference for medical image analysis.

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“…We neglect the effect of the inclusion of foreground and/or cluster galaxies in the background sample, known as 'boost factor', which cause a dilution effect in the lensing signal, since it is expected to be negligible considering the cuts implemented in the background sample selection (Leauthaud et al 2017;Shan et al 2018;Blake et al 2016). To assign background galaxies to each galaxy cluster we use the public regular grid search algorithm grispy 2 (Chalela et al 2019). An analysis of the lensing signal computed for the individual catalogues is presented in Apppendix A as a control test for their combination.…”

Section: Weak Lensing Surveysmentioning

confidence: 99%

Measuring the surface mass density ellipticity of redMaPPer galaxy clusters using weak-lensing

Gonzalez,

Makler,

Lambas

et al. 2020

Preprint

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In this work we study the shape of the projected surface mass density distribution of galaxy clusters using weak-lensing stacking techniques. In particular, we constrain the average aligned component of the projected ellipticity, ε, for a sample of redMaPPer clusters (0.1 ≤ z < 0.4). We consider six different proxies for the cluster orientation and measure ε for three ranges of projected distances from the cluster centres. The mass distribution in the inner region (up to 700 kpc) is better traced by the cluster galaxies with a higher membership probability, while the outer region (from 700 kpc up to 5 Mpc) is better traced by the inclusion of less probable galaxy cluster members. The fitted ellipticity in the inner region is ε = 0.21 ± 0.04, in agreement with previous estimates. We also study the relation between ε and the cluster mean redshift and richness. By splitting the sample in two redshift ranges according to the median redshift, we obtain larger ε values for clusters at higher redshifts, consistent with the expectation from simulations. In addition, we obtain higher ellipticity values in the outer region of clusters at low redshifts. We discuss several systematic effects that might affect the measured lensing ellipticities and their relation to the derived ellipticity of the mass distribution.

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GriSPy: A Python package for Fixed-Radius Nearest Neighbors Search

Cited by 4 publications

References 15 publications

On the weak lensing masses of a new sample of galaxy groups

On the weak lensing masses of a new sample of galaxy groups

Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking

Measuring the surface mass density ellipticity of redMaPPer galaxy clusters using weak-lensing

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