DESI DR2 quasar sample yields bias b_Q(z=2.48)=3.61 that evolves as b(z)=0.230[(1+z)^2-6.565]+2.394, consistent with constant ~10^12 M_sun halo mass and weak luminosity dependence at fixed redshift.
Constraining the Lifetime of Quasars from their Spatial Clustering
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The lifetime t_Q of the luminous phase of quasars is constrained by current observations to be between 10^6 and 10^8 years, but is otherwise unkown. We model the quasar luminosity function in detail in the optical and X-ray bands using the Press-Schechter formalism, and show that the expected clustering of quasars depends strongly on their assumed lifetime. We quantify this dependence, and find that existing measurements of the correlation length of quasars are consistent with the range 10^6 < t_Q < 10^8 years. We then show that future measurements of the power spectrum of quasars out to z=3, from the 2dF or Sloan Digital Sky Survey, can significantly improve this constraint, and in principle allow a precise determination of t_Q. We estimate the systematic errors introduced by uncertainties in the modeling of the quasar-halo relationship, as well as by the possible existence of obscured quasars.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Calypso is a parameter-conditioned stochastic surrogate model for circumbinary accretion flows using PCA and multivariate Gaussian modeling, released as open-source software with a closed-form likelihood for parameter inference from time series.
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Clustering of high-redshift quasars with DESI DR2
DESI DR2 quasar sample yields bias b_Q(z=2.48)=3.61 that evolves as b(z)=0.230[(1+z)^2-6.565]+2.394, consistent with constant ~10^12 M_sun halo mass and weak luminosity dependence at fixed redshift.
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\texttt{calypso}: a Parameter-Conditioned Stochastic Surrogate Model for Circumbinary Accretion Time-Series
Calypso is a parameter-conditioned stochastic surrogate model for circumbinary accretion flows using PCA and multivariate Gaussian modeling, released as open-source software with a closed-form likelihood for parameter inference from time series.