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Abstract We report the discovery of an isolated white dwarf with a spin period of 70 s. We obtained high-speed photometry of three ultramassive white dwarfs within 100 pc and discovered significant variability in one. SDSS J221141.80+113604.4 is a 1.27
M ⊙(assuming a CO core) magnetic white dwarf that shows 2.9% brightness variations in the BG40 filter with a 70.32 ± 0.04 s period, becoming the fastest spinning isolated white dwarf currently known. A detailed model atmosphere analysis shows that it has a mixed hydrogen and helium atmosphere with a dipole field strength ofB d = 15 MG. Given its large mass, fast rotation, strong magnetic field, unusual atmospheric composition, and relatively large tangential velocity for its cooling age, J2211+1136 displays all of the signatures of a double white dwarf merger remnant. Long-term monitoring of the spin evolution of J2211+1136 and other fast-spinning isolated white dwarfs opens a new discovery space for substellar and planetary mass companions around white dwarfs. In addition, the discovery of such fast rotators outside of the ZZ Ceti instability strip suggests that some should also exist within the strip. Hence, some of the monoperiodic variables found within the instability strip may be fast-spinning white dwarfs impersonating ZZ Ceti pulsators. -
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Abstract The aim of our paper is to investigate the properties of the classical phase-dispersion minimization (PDM), analysis of variance (AOV), string-length (SL), and Lomb–Scargle (LS) power statistics from a statistician’s perspective. We confirm that when the data are perturbations of a constant function, i.e. under the null hypothesis of no period in the data, a scaled version of the PDM statistic follows a beta distribution, the AOV statistic follows an F distribution, and the LS power follows a chi-squared distribution with two degrees of freedom. However, the SL statistic does not have a closed-form distribution. We further verify these theoretical distributions through simulations and demonstrate that the extreme values of these statistics (over a range of trial periods), often used for period estimation and determination of the false alarm probability (FAP), follow different distributions than those derived for a single period. We emphasize that multiple-testing considerations are needed to correctly derive FAP bounds. Though, in fact, multiple-testing controls are built into the FAP bound for these extreme-value statistics, e.g. the FAP bound derived specifically for the maximum LS power statistic over a range of trial periods. Additionally, we find that all of these methods are robust to heteroscedastic noise aimed to mimic the degradation or miscalibration of an instrument over time. Finally, we examine the ability of these statistics to detect a non-constant periodic function via simulating data that mimics a well-detached binary system, and we find that the AOV statistic has the most power to detect the correct period, which agrees with what has been observed in practice.
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3847/1538-3881/ab9f95
