Bayesian Blocks Analysis

Bayesian Blocks, provide an adaptive, data-driven method for optimal segmentation of time series or event data. Instead of relying on fixed bin sizes, Bayesian Blocks dynamically determine change points in the data, dividing it into piecewise constant segments (blocks) that best represent the structure of the signal. We follow the formalization of Scargle et al. (2013).

Key features of the Bayesian Blocks algorithm include:

• Model selection via Bayesian inference: The algorithm selects the most probable segmentation under a Bayesian framework, balancing goodness-of-fit with model complexity.

• Applicability to different data modes: It works with event data (e.g., photon arrival times), regularly sampled time series, and point measurements with errors.

• Non-parametric nature: The method does not assume a particular functional form for the signal, making it robust and flexible.

Agilepy implements a AGBayesianBlocks class that works with: - Binned data, i.e. Light Curves. Input data can be provided in 3 different formats: the agile Aperture Photometry Light Curve format, the agile Maximum Likelihood Light Curve format, a custom light curve format. - Unbinned data, i.e. a list of arrival times of detected photons.

The Bayesian Blocks algorithm has the following steps: 1. Prepare a YAML configuration file for AGBayesianBlocks. You can do it manually or by using AGBayesianBlocks.getConfiguration(). 2. Define a AGBayesianBlocks object, and load the data with AGBayesianBlocks.selectEvents(). 3. Compute the Bayesian blocks with AGBayesianBlocks.bayesianBlocks().

Configuration

We describe here the parameters of the configuration file section by section.

Output

These parameters are common to all Agilepy classes.

Option	Type	Default	Required	Description
outdir	string		yes	Output Directory.
filenameprefix	string	analysis_product	no	Prefix for files.
sourcename	string	bb-source	no	Tag with source name.
username	string	my_name	no	Tag with user name.
verboselvl	int	0	yes	0 for no extra logs, 1 for INFO, 2 for DEBUG.

Selection

Option	Type	Default	Required	Description
file_path	string		yes	Path to the input file.
file_mode	string		yes	Input file format: AGILE_PH (unbinned data), AGILE_AP, AGILE_MLE or CUSTOM_LC (binned data).
tstart	float	null	no	If definend, select events according to the start time.
tstop	float	null	no	If definend, select events according to the stop time.
ratecorrection	float	0	no	Flux correction.

For AGILE_AP and AGILE_MLE, tstart and tstop must be in MJD format. For CUSTOM_LC and AGILE_PH, in the same format of the times provided.

Note

Note on ratecorrection. The Bayesian Blocks algorithm works with integers, so fluxes need a correction factor for a correct statistical treatment. If 0 (default), the correction factor is the mean exposure. If a float is provided it will be applied as a correction factor for the flux. If -1, the algorithm will work with counts instead of fluxes.

Bayesianblocks

Option	Type	Default	Required	Description
fitness	string	events	no	Fitness function of the algorithm: events, measures or regular_events.
p0	string	0.35	no	Prior parameter, suggested for unbinned data, gives the false alarm probability to compute the prior.
gamma	float	null	no	Prior parameter, suggested for binned data, gives the slope of the prior on the number of bins.
useerror	bool	true	no	If true, account for error in blocks computation.

Note

Note on fitness. It sets the type of algorithm. Possible values are events (binned light curve or unbinned event data), measures (sequence of flux measurements with Gaussian errors), `regular_events`(0/1 data measured regularly).