fBLS – a fast-folding BLS algorithm

Shahaf, Sahar; Zackay, Barak; Mazeh, T.; Faigler, S.; Ivashtenko, Oryna

doi:10.1093/mnras/stac960

Cited by 8 publications

(4 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The figure illustrates that TCF is somewhat faster than the BLS algorithm and scales as ( )  N 3 , somewhat better than BLS with ( )  N 3.7 . We have not examined alternative, faster versions of the BLS algorithm such as SparseBLS (Panahi & Zucker 2021) and fBLS (Shahaf et al 2022).…”

Section: Discussionmentioning

confidence: 99%

A Study of Two Periodogram Algorithms for Improving the Detection of Small Transiting Planets

Gondhalekar,

Feigelson,

Caceres

et al. 2023

ApJL

View full text Add to dashboard Cite

The sensitivities of two periodograms are compared for weak signal planet detection in transit surveys: the widely used Box Least Squares (BLS) algorithm following light curve detrending and the Transit Comb Filter (TCF) algorithm following autoregressive ARIMA modeling. Small depth transits are injected into light curves with different simulated noise characteristics. Two measures of spectral peak significance are examined: the periodogram signal-to-noise ratio (S/N) and a false alarm probability (FAP) based on the generalized extreme value distribution. The relative performance of the BLS and TCF algorithms for small planet detection is examined for a range of light curve characteristics, including orbital period, transit duration, depth, number of transits, and type of noise. We find that the TCF periodogram applied to ARIMA fit residuals with the S/N detection metric is preferred when short-memory autocorrelation is present in the detrended light curve and even when the light curve noise had white Gaussian noise. BLS is more sensitive to small planets only under limited circumstances with the FAP metric. BLS periodogram characteristics are inferior when autocorrelated noise is present due to heteroscedastic noise and false period detection. Application of these methods to TESS light curves with known small exoplanets confirms our simulation results. The study ends with a decision tree that advises transit survey scientists on procedures to detect small planets most efficiently. The use of ARIMA detrending and TCF periodograms can significantly improve the sensitivity of any transit survey with regularly spaced cadence.

show abstract

Section: Discussionmentioning

confidence: 99%

A Study of Two Periodogram Algorithms for Improving the Detection of Small Transiting Planets

Gondhalekar,

Feigelson,

Caceres

et al. 2023

ApJL

View full text Add to dashboard Cite

show abstract

“…Another possibility is that Gaia BH1 formed through isolated binary evolution via a channel not captured in vanilla population synthesis models (e.g., Hirai & Mandel 2022). The large populations of wide white dwarf + main sequence (Shahaf et al 2023;Yamaguchi et al 2024) and neutron star + main sequence (K. E. El-Badry et al 2024, in preparation) binaries discovered by Gaia with orbits similar to that of Gaia BH1 lends some credibility to this possibility, but more work is required to explore it.…”

Section: Implications For the Formation History Of Gaia Bh1mentioning

confidence: 99%

ESPRESSO Observations of Gaia BH1: High-precision Orbital Constraints and no Evidence for an Inner Binary

Nagarajan,

El-Badry,

Triaud

et al. 2024

PASP

View full text Add to dashboard Cite

We present high-precision radial velocity observations of Gaia BH1, the nearest known black hole (BH). The system contains a solar-type G star orbiting a massive dark companion, which could be either a single BH or an inner BH + BH binary. A BH + BH binary is expected in some models where Gaia BH1 formed as a hierarchical triple, which is attractive because they avoid many of the difficulties associated with forming the system through isolated binary evolution. Our observations test the inner binary scenario. We have measured 115 precise RVs of the G star, including 40 from ESPRESSO with a precision of 3–5 m s−1, and 75 from other instruments with a typical precision of 30–100 m s−1. Our observations span 2.33 orbits of the G star and are concentrated near a periastron passage, when perturbations due to an inner binary would be largest. The RVs are well-fit by a Keplerian two-body orbit and show no convincing evidence of an inner binary. Using REBOUND simulations of hierarchical triples with a range of inner periods, mass ratios, eccentricities, and orientations, we show that plausible inner binaries with periods P inner ≳ 1.5 days would have produced larger deviations from a Keplerian orbit than observed. Binaries with P inner ≲ 1.5 days are consistent with the data, but these would merge within a Hubble time and would thus imply fine-tuning. We present updated parameters of Gaia BH1's orbit. The RVs yield a spectroscopic mass function f M BH = 3.9358 ± 0.0002 M ⊙ —about 7000σ above the ∼2.5 M ⊙ maximum neutron star mass. Including the inclination constraint from Gaia astrometry, this implies a BH mass of M BH = 9.27 ± 0.10 M ⊙.

show abstract

“…For all parameter values, BLS fits a box to the folded light curve and returns the signal strength in a periodogram. Variants to BLS include the transit least squares (TLS) algorithm that considers the effect of stellar limb darkening during planetary ingress and egress (Hippke & Heller 2019) and the fast BLS computational algorithm (Shahaf et al 2021). However, the BLS periodogram is vulnerable to strong trends, high noise levels, and complex alias structures that impede detection of smaller planets (Ofir 2014, , Gondhalekar et al in preparation).…”

Section: Past Efforts For Detrending and Transit Identificationmentioning

confidence: 99%

DIAmante TESS AutoRegressive Planet Search (DTARPS): I. Analysis of 0.9 Million Light Curves

Melton¹,

Feigelson²,

Montalto³

et al. 2023

Preprint

View full text Add to dashboard Cite

Nearly one million light curves from the TESS Year 1 southern hemisphere extracted from Full Frame Images with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. ARIMA models remove trends and lingering autocorrelated noise, the Transit Comb Filter identifies the strongest periodic signal in the light curve, and a Random Forest machine learning classifier is trained and applied to identify the best potential candidates. Classifier training sets include injections of both planetary transit signals and contaminating eclipsing binaries. The optimized classifier has a True Positive Rate of 92.8% and a False Positive Rate of 0.37% from the labeled training set. The result of this DIAmante TESS autoregressive planet search (DTARPS) analysis is a list of 7,377 potential exoplanet candidates. The classifier has a False Positive Rate of 0.3%, a 64% recall rate for previously confirmed exoplanets, and a 78% negative recall rate for known False Positives. The completeness map of the injected planetary signals shows high recall rates for planets with 8 − 30 R ⊕ radii and periods 0.6 − 13 days and poor completeness for planets with radii < 2 R ⊕ or periods < 1 day. The list has many False Alarms and False Positives that need to be culled with multifaceted vetting operations (Paper II).

show abstract

fBLS – a fast-folding BLS algorithm

Cited by 8 publications

References 67 publications

A Study of Two Periodogram Algorithms for Improving the Detection of Small Transiting Planets

A Study of Two Periodogram Algorithms for Improving the Detection of Small Transiting Planets

ESPRESSO Observations of Gaia BH1: High-precision Orbital Constraints and no Evidence for an Inner Binary

DIAmante TESS AutoRegressive Planet Search (DTARPS): I. Analysis of 0.9 Million Light Curves

Contact Info

Product

Resources

About