transit-least-squares
Transit Least Squares (TLS) algorithm for detecting exoplanet transits in light curves. Use when searching for transiting exoplanets specifically, as TLS is more sensitive than Lomb-Scargle for transit-shaped signals. Based on the transitleastsquares Python package.
Profile →npx skills add majiayu000/claude-skill-registry --skill transit-least-squares-benchflow-ai-skillsbench-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Transit Least Squares (TLS) Transit Least Squares is a specialized algorithm optimized for detecting exoplanet transits in light curves. It's more sensitive than Lomb-Scargle for transit-shaped signals because it fits actual transit models. ## Overview TLS searches for periodic transit-like dips in brightness by fitting transit models at different periods, durations, and epochs. It's the preferred method for exoplanet transit detection. ## Installation ```bash pip install transitleastsquares ``` ## Basic Usage **CRITICAL**: Always include `flux_err` (flux uncertainties) for best results! ```python import transitleastsquares as tls import lightkurve as lk import numpy as np # Example 1: Using Lightkurve (recommended) lc = lk.LightCurve(time=time, flux=flux, flux_err=error) lc_clean = lc.remove_outliers(sigma=3) lc_flat = lc_clean.flatten() # Create TLS object - MUST include flux_err! pg_tls = tls.transitleastsquares( lc_flat.time.value, # Time array lc_flat.flux.value, # Flux array lc_flat.flux_err.value # Flux uncertainties (REQUIRED!) ) # Search for transits (uses default period range if not specified) out_tls = pg_tls.power( show_progress_bar=False, # Set True for progress trac
- Overview
- Installation
- Basic Usage
- Example 2: With explicit period range
- Period Refinement Strategy
- Advanced Options
- Advanced Parameters
- Phase-Folding
- Transit Masking
- Interpreting Results
- Signal Detection Efficiency (SDE)
- Signal-to-Noise Ratio (SNR)
- Common Warnings
- Model Light Curve
pip install transitleastsquares pip install transitleastsquares lightkurve numpy matplotlib
What does the transit-least-squares skill do?
Transit Least Squares (TLS) algorithm for detecting exoplanet transits in light curves. Use when searching for transiting exoplanets specifically, as TLS is more sensitive than Lomb-Scargle for transit-shaped signals. Based on the transitleastsquares Python package.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill transit-least-squares-benchflow-ai-skillsbench-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.