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Fixed early ZTF r sine fits give larger errors than a constant on later data for two of three periodic quasar candidates

We test fixed sine forecasts for three published periodic quasar candidates. The sample is from Figure 11 of Chen et al. (2024). The sources are SDSS J083111.23+123107.8, J155304.40+240255.1, and J135830.46-012255.7. We use public ZTF DR24 photometry through 2025. We fit only observations with MJD at most 59647. The main r-filter test uses 20-day bins. The early fitted periods are 890.7, 883.6, and 755.9 days. On later data, root mean square errors are 0.222, 0.134, and 0.315 magnitude. The ratios to an early constant forecast are 1.00, 2.17, and 1.64. The last two forecasts also have larger errors than the constant in the g filter and under all four processing choices. From 2024 onward, their r error ratios are 2.22 and 2.10. Simulations repeat the full early period search under two correlated-noise assumptions. The measured errors are unusual under noise fitted to the early sine residuals. A larger noise budget makes the last two errors common. The early fits cover fewer than two cycles. This test uses new early ZTF fits and does not reproduce the original combined-survey fits. It does not exclude binary black holes. The package preserves exact archive responses, provenance, code, simulation draws, figures, and reproduction instructions.

quasars · time domain astronomy · Zwicky Transient Facility · periodic variability · forecast validation · correlated noise · binary black hole candidates · public photometry

Admitted 2 citing publications

References

5
  1. usesdoi:10.1093/mnras/stad3981

    Figure 11 defines the fixed sample of three periodic quasar candidates. We fit new early ZTF models and do not reproduce its combined-survey forecasts.

  2. doi:10.1093/mnras/stw1412

    This study explains how correlated quasar variation can produce apparent periods over short intervals.

  3. usesdoi:10.1088/1538-3873/aae8ac

    We use public ZTF PSF photometry served by IRSA and preserve the exact DR24 archive responses.

  4. usesdoi:10.3847/1538-4357/ac7c74

    Astropy supplies the early Lomb-Scargle search and coordinate checks.

  5. usesdoi:10.1038/s41592-019-0686-2

    SciPy supplies scalar optimization, covariance optimization, and linear algebra.

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