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Guangping Ye

Publications and source records attributed to Guangping Ye.

3 recordsLinked to original sources

Probing Dark Matter Halos of High-redshift Quasars via Wide-Field Clustering

High-redshift quasars are powerful tracers of both astrophysical processes and large-scale structure in the early Universe. Using a sample of 1,251 spectroscopically confirmed quasars and 827 highly reliable photometric quasar candidates selected with a machine-learning framework, all at $4.7 \leq z < 6.3$ (excluding $5.4 < z < 5.6$) and with a median luminosity of $M_{1450}\sim -25.5$, we investigate the large-scale environments of quasars near the end of cosmic reionization. We measure the projected auto-correlation function of the quasar population and derive bias parameters of $b=22.11^{+2.17}_{-2.19}$ and $28.55^{+5.76}_{-5.75}$ for the redshift intervals $4.7\leq z<5.4$ and $5.6\leq z<6.3$, respectively. These correspond to characteristic dark matter halo masses of $\log(M_{ h}/M_\odot)=12.66^{+0.10}_{-0.11}$ and $12.62^{+0.21}_{-0.21}$, respectively. We further estimate quasar duty cycles of $0.011^{+0.017}_{-0.007}$ and $0.012^{+0.104}_{-0.011}$ for the two redshift bins. These measurements are consistent with previous studies and the observed $f_{\rm duty}$--$M_{\rm halo}$ relation, indicating that only a small fraction of suitable dark matter halos host optically luminous quasars at any given time. The inferred low duty cycles suggest that a substantial fraction of supermassive black hole growth may occur during obscured accretion phases. Our results provide new constraints on the connection between high-redshift quasars and their host dark matter halos, offering valuable insights into the co-evolution of supermassive black holes and large-scale structure in the early Universe.

astro-ph.GA

A Local Dwarf Galaxy Search Using Machine Learning

We present a machine learning search for local, low-mass galaxies ($z < 0.02$ and $10^6 M_\odot < M_* < 10^9 M_\odot$) using the combined photometric data from the DESI Imaging Legacy Surveys and the WISE survey. We introduce the spectrally confirmed training sample, discuss evaluation metrics, investigate the features, compare different machine learning algorithms, and find that a 7-class neural network classification model is highly effective in separating the signal (local, low-mass galaxies) from various contaminants, reaching a precision of $95\%$ and a recall of $76\%$. The principal contaminants are nearby sub-$L^*$ galaxies at $0.02 < z < 0.05$ and nearby massive galaxies at $0.05 < z < 0.2$. We find that the features encoding surface brightness information are essential to achieving a correct classification. Our final catalog, which we make available, consists of 112,859 local, low-mass galaxy candidates, where 36,408 have high probability ($p_{\rm signal} > 0.95$), covering the entire Legacy Surveys DR9 footprint. Using DESI-EDR public spectra and data from the SAGA and ELVES surveys, we find that our model has a precision of $\sim 100\%$, $96\%$, and $97\%$, respectively, and a recall of $\sim 51\%$, $68\%$ and $53\%$, respectively. The results of those independent spectral verification demonstrate the effectiveness and efficiency of our machine learning classification model.

astro-ph.GA

Machine Learning-based Search of High-redshift Quasars

We present a machine learning search for high-redshift ($5.0 < z < 6.5$) quasars using the combined photometric data from the DESI Imaging Legacy Surveys and the WISE survey. We explore the imputation of missing values for high-redshift quasars, discuss the feature selections, compare different machine learning algorithms, and investigate the selections of class ensemble for the training sample, then we find that the random forest model is very effective in separating the high-redshift quasars from various contaminators. The 11-class random forest model can achieve a precision of $96.43\%$ and a recall of $91.53\%$ for high-redshift quasars for the test set. We demonstrate that the completeness of the high-redshift quasars can reach as high as $82.20\%$. The final catalog consists of 216,949 high-redshift quasar candidates with 476 high probable ones in the entire Legacy Surveys DR9 footprint, and we make the catalog publicly available. Using MUSE and DESI-EDR public spectra, we find that 14 true high-redshift quasars (11 in the training sample) out of 21 candidates are correctly identified for MUSE, and 20 true high-redshift quasars (11 in the training sample) out of 21 candidates are correctly identified for DESI-EDR. Additionally, we estimate photometric redshift for the high-redshift quasar candidates using random forest regression model with a high precision.

astro-ph.GA