Browsing by Autor "Xiao-Dong Li"

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ASTRA-DESI DR1 Release v0.6
(European Organization for Nuclear Research, 2025) Alejandro Palomino; Felipe Leonardo Gómez-Cortés; Xiao-Dong Li; Jaime E. Forero-Romero; Jaime E. Forero-Romero
ASTRA-DESI Data Release 1 (DR1) This record bundles the ASTRA-DESI DR1 products for the two DESI survey regions (NGC1 and NGC2). Every catalogue is delivered as a compressed FITS binary table (.fits.gz) whose first extension (HDU 1) contains the data. File headers store the keywords RELEASE=DR1 and the corresponding ZONE label for quick checks. The regions follow the sky boxes defined by Hou et al. (2025, arXiv:2508.09070): NGC1 covers 110 ° ≤ RA ≤ 260 ° and -10 ° ≤ DEC ≤ 8 °; NGC2 covers 180 ° ≤ RA ≤ 260 ° and 30 ° ≤ DEC ≤ 40 °. Tracer Number of objects BGS 240,986 LRG 102,199 ELG 67,456 QSO 87,307 Directory layout The archive preserves the NERSC production tree. Top-level folders contain: raw/ – merged real and random catalogues per region. classification/ – neighbour count summaries per tracer and random iteration. probabilities/ – web-type membership probabilities derived from the classification counts. pairs/ – sparse list of target pairs used when counting neighbours. Naming conventions raw/zone_REGION.fits.gz – Region NGC1 or NGC2. classification/zone_REGION_classified.fits.gz – combined tracer summary for the region. probabilities/zone_REGION_probability.fits.gz – per-object web-type probabilities. pairs/zone_REGION_pairs.fits.gz – neighbour pairs for the region. Column reference Raw catalogues (raw/*.fits.gz) Column Type Description TARGETID int64 DESI target identifier. RA float64 Right ascension in degrees. DEC float64 Declination in degrees. Z float64 Spectroscopic redshift. XCART, YCART, ZCART float64 Comoving Cartesian coordinates (Mpc) computed with the Planck18 cosmology. TRACERTYPE string Tracer label with suffix _DATA or _RAND (BGS_BRIGHT, ELG, LRG, QSO). RANDITER int32 -1 for real objects, otherwise the random iteration index (0–99). Random catalogues mirror the data counts for every tracer and random iteration. Classification catalogues (classification/*.fits.gz) Column Type Description TARGETID int64 Identifier of the central object. RANDITER int32 -1 for data rows, otherwise the random catalogue index. ISDATA bool True for data rows; False for random iterations. NDATA int32 Number of neighbour pairs built from data tracers for this target. NRAND int32 Number of neighbour pairs contributed by random tracers. TRACERTYPE string Tracer family (BGS_BRIGHT, ELG, LRG, QSO). Probability catalogues (probabilities/*.fits.gz) Column Type Description TARGETID int64 Identifier of the object. TRACERTYPE string Tracer family. PVOID float32 Probability of the object being a void class. PSHEET float32 Probability of the sheet class. PFILAMENT float32 Probability of the filament class. PKNOT float32 Probability of the knot class. Pair catalogues (pairs/*.fits.gz) Column Type Description TARGETID1 int64 Central object in the neighbour pair. TARGETID2 int64 Neighbour contributing to the count. RANDITER int32 -1 for data pairs, otherwise random catalogue index. Using the catalogues All FITS tables are compressed with gzip. Random catalogues use RANDITER values 0–99; filtering on RANDITER >= 0 isolates the random iterations. The Cartesian coordinates assume the Planck18 cosmology provided by astropy.cosmology.Planck18.
Cosmic web classification through stochastic topological ranking
(Oxford University Press, 2025) J. E. Forero-Romero; Alejandro Palomino; Felipe Leonardo Gómez-Cortés; Xiao-Dong Li
ABSTRACT This paper introduces ASTRA (Algorithm for Stochastic Topological RAnking), a new method for classifying galaxies into cosmic web structures – voids, sheets, filaments, and knots – specifically designed for large spectroscopic surveys. ASTRA operates on observed galaxy positions and a corresponding random catalogue, generating probabilistic cosmic web classifications for both data sets. The method’s key innovation lies in using random points to trace underdense regions, enabling robust identification of cosmic voids that are poorly sampled by galaxies. We evaluate ASTRA using N-body simulations (dark matter-only and hydrodynamical) and SDSS observational data, performing both visual inspections and quantitative analyses of mass and volume distributions. The algorithm successfully produces void catalogues with size functions following theoretical expectations and demonstrates consistent environmental statistics across diverse data sets. Comparative analysis against established cosmic web classifiers confirms ASTRA’s effectiveness, particularly for filament identification. By incorporating both observed and random points in its classification scheme, ASTRA provides a full cosmic web characterization without requiring density field interpolation or fixed geometric assumptions. The method’s ability to quantify spatial correlations among different cosmic web components offers promising avenues for enhancing cosmological parameter constraints through non-standard clustering statistics.
Improving SDSS cosmological constraints through <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>β</mml:mi></mml:mrow></mml:math>-skeleton weighted correlation functions
(American Physical Society, 2024) Fenfen Yin; Jiacheng Ding; Limin Lai; Wei Zhang; Liang Xiao; Zihan Wang; J. E. Forero-Romero; Le Zhang; Xiao-Dong Li
The $\ensuremath{\beta}$-skeleton approach can be conveniently utilized to construct the cosmic web based on the spatial geometry distribution of galaxies, particularly in sparse samples. This method plays a key role in establishing the three-dimensional structure of the Universe and serves as a tool for quantitatively characterizing the nature of the cosmic web. This study is the first application of $\ensuremath{\beta}$-skeleton information as weights in mark weighted correlation functions (MCFs), presenting a novel statistical measure. We have applied the $\ensuremath{\beta}$-skeleton approach to the CMASS NGC galaxy samples from SDSS BOSS DR12 in the redshift interval $0.45\ensuremath{\le}z\ensuremath{\le}0.55$. Additionally, we applied this approach to three COLA cosmological simulations with different settings (${\mathrm{\ensuremath{\Omega}}}_{m}=0.25$, ${\mathrm{\ensuremath{\Omega}}}_{m}=0.31$, ${\mathrm{\ensuremath{\Omega}}}_{m}=0.4$) for comparison. We measured three MCFs, each weighted by (i) the number of neighboring galaxies around each galaxy, (ii) the average distance of each galaxy from its surrounding neighbors, and (iii) the reciprocal of the average distance of each galaxy from its surrounding neighbors. By comparing measurements and calculating corresponding ${\ensuremath{\chi}}^{2}$ statistics, we observe high sensitivity to the cosmological parameter ${\mathrm{\ensuremath{\Omega}}}_{m}$ through a joint analysis of the two-point correlation and three MCFs.