rebin

Rebin, or rebinning, is the process of transforming a dataset from one set of bin boundaries to another. It is commonly applied to histograms, spectra, images, and time series to modify resolution, combine data points, or align datasets collected on different grids. Rebinning can help improve signal-to-noise by aggregating data or enable comparison across datasets with different binning schemes.

Methods to rebin include several approaches. Simple binning aggregates data within each new bin, typically by summing counts and optionally averaging. Flux-conserving rebinning uses the overlap between old and new bins: each original bin contributes a fraction proportional to its overlap with each new bin, preserving total counts. Interpolation-based rebinning distributes original bin values onto the new grid using interpolation, which can improve continuity but may not conserve total counts. Kernel-based rebinning applies a smoothing kernel to estimate new bin values, balancing resolution and noise.

Applications and considerations. In spectroscopy and astronomy, rebinning is used to convert data to a uniform linear or logarithmic scale, or to match the binning of another instrument. In imaging and time-series analysis, it helps standardize resolution across datasets. Uncertainties must be propagated, and correlations between bins can arise, especially when using overlap- or kernel-based methods. Edge handling and the choice between preserving flux versus preserving peak structure influence results.

Software commonly provides rebinning facilities as part of scientific libraries, with attention to grid geometry, conservation properties, and error propagation.