arXiv subjects

John F. Suárez-Pérez

Publications and source records attributed to John F. Suárez-Pérez.

4 recordsLinked to original sources

arXiv2026

Cosmic-web quenching with DESI DR1: T-Web environments and mass-dependent red/blue classification

We study DESI DR1 galaxies to quantify colour dependence on cosmic web environment for three tracers spanning complementary regimes: BGS ($0.15\le z<0.55$), LRG ($0.6\le z<0.9$), and ELG ($0.6\le z<1.6$). Web environments are reconstructed with the tidal-tensor (T-Web) formalism on a $256^3$ grid in an $800\,Mpc$ cube and classified into voids, sheets, filaments, and knots. Sheets and filaments dominate volume ($\sim 45$--$48\%$ and $\sim 37$--$40\%$), voids $\sim 6$--$16\%$ knots $\sim 4$--$6\%$. A mass-dependent Otsu method separates red and blue populations. The BGS red fraction evolves non-monotonically: at $z\approx0.20$, voids ($13.89\pm5.76\%$), sheets ($6.13\pm1.27\%$), filaments ($9.24\pm1.66\%$), knots ($6.12\pm3.42\%$); at $z\approx0.30$, values range from $0.63\pm0.44\%$ to $2.01\pm0.99\%$; at $z\approx0.50$, from $17.93\pm0.44\%$ to $19.63\pm1.08\%$; environmental differences are small. LRGs show environment-dependent quenching: at $z\approx0.66$, knots ($65.90\pm0.45\%$), voids ($62.40\pm1.81\%$), filaments ($60.21\pm0.48\%$), sheets ($58.37\pm3.15\%$); by $z\approx0.88$, these converge to $\sim 68$--$70\%$. ELGs exhibit strong redshift evolution: filaments drop from $55.18\pm0.31\%$ at $z\approx0.65$ to $33.22\pm0.21\%$ at $z\approx0.95$; voids and sheets show similar declines, with weak and non-monotonic. High-mass selection increases red fractions but preserves trends. Relative red and blue fractions (RRF/RBF) show filaments and sheets host the largest shares of both red and blue galaxies; knots contribute less despite elevated red fractions. The $(g-r)$ colour distributions reveal an enhanced red component in knots and bluer colours in voids, with the clearest bimodality at low redshift. Overall, stellar mass drives the primary quenching trend, while environment provides a systematic secondary modulation, strongest in dense knots and at lower stellar masses.

astro-ph.GA↗

arXiv2025

Deep-TAO: The Deep Learning Transient Astronomical Object data set for Astronomical Transient Event Classification

We present the Deep-learning Transient Astronomical Object (Deep-TAO), a dataset of 1,249,079 annotated images from the Catalina Real-time Transient Survey, including 3,807 transient and 12,500 non-transient sequences. Deep-TAO has been curated to provide a clean, open-access, and user-friendly resource for benchmarking deep learning models. Deep-TAO covers transient classes such as blazars, active galactic nuclei, cataclysmic variables, supernovae, and events of indeterminate nature. The dataset is publicly available in FITS format, with Python routines and Jupyter notebooks for easy data manipulation. Using Deep-TAO, a baseline Convolutional Neural Network outperformed traditional random forest classifiers trained on light curves, demonstrating its potential for advancing transient classification.

astro-ph.IM↗

arXiv2021

The four cosmic tidal web elements from the $β$-skeleton

Precise cosmic web classification of observed galaxies in massive spectroscopic surveys can be either highly uncertain or computationally expensive. As an alternative, we explore a fast Machine Learning-based approach to infer the underlying dark matter tidal cosmic web environment of a galaxy distribution from its $β$-skeleton graph. We develop and test our methodology using the cosmological magnetohydrodynamic simulation Illustris-TNG at $z=0$. We explore three different tree-based machine-learning algorithms to find that a random forest classifier can best use graph-based features to classify a galaxy as belonging to a peak, filament or sheet as defined by the T-Web classification algorithm. The best match between the galaxies and the dark matter T-Web corresponds to a density field smoothed over scales of $2$ Mpc, a threshold over the eigenvalues of the dimensionless tidal tensor of $λ_{\rm{th}}=0.0$ and galaxy number densities around $8\times 10^{-3}$ Mpc$^{-3}$. This methodology results on a weighted F1 score of 0.728 and a global accuracy of 74\%. More extensive tests that take into account lightcone effects and redshift space distortions (RSD) are left for future work. We make one of our highest ranking random forest models available on a public repository for future reference and reuse.

astro-ph.CO↗

arXiv2020

MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition

We introduce MANTRA, an annotated dataset of 4869 transient and 71207 non-transient object lightcurves built from the Catalina Real Time Transient Survey. We provide public access to this dataset as a plain text file to facilitate standardized quantitative comparison of astronomical transient event recognition algorithms. Some of the classes included in the dataset are: supernovae, cataclysmic variables, active galactic nuclei, high proper motion stars, blazars and flares. As an example of the tasks that can be performed on the dataset we experiment with multiple data pre-processing methods, feature selection techniques and popular machine learning algorithms (Support Vector Machines, Random Forests and Neural Networks). We assess quantitative performance in two classification tasks: binary (transient/non-transient) and eight-class classification. The best performing algorithm in both tasks is the Random Forest Classifier. It achieves an F1-score of 96.25% in the binary classification and 52.79% in the eight-class classification. For the eight-class classification, non-transients ( 96.83% ) is the class with the highest F1-score, while the lowest corresponds to high-proper-motion stars ( 16.79% ); for supernovae it achieves a value of 54.57% , close to the average across classes. The next release of MANTRA includes images and benchmarks with deep learning models.

astro-ph.IM↗