Releases: flav-io/flavio
v0.17
The main new feature in this release is the addition of the branching ratio of the inclusive semi-leptonic B decay B→Xclν, with l=e or μ (or their average). Many thanks to Paolo Gambino for useful discussions and for sharing numerical results necessary for implementing and validating this! See arXiv:1411.6560 and references therein for details on the theory.
An additional, minor improvement since v0.16.1 concerns the treatment of correlated ucertainties in Bayesian fits; see commit a32c80d for details.
Finally, the default values of masses and widths have been updated from PDG 2015 to PDG 2016 values. There has not been any numerically relevant change for the currently implemented observables.
v0.16.1
This bug fix release fixes
- a regression introduced by the recent internal change in the
Constraints
class in v0.16 that broke the fit functions, - an inconsistency in the combination of experimental likelihoods in the presence of multiple experiments measuring the same observable when using the
FastFit
class.
Additional unit tests have been added to improve the stability of these fitting functions.
v0.16
This release focuses on stability improvements with only few new features.
Improvements on the physics side:
- Added lepton flavour ratios for B->Plnu decays, e.g.
<Rtaumu>(B0->Dlnu)
- Added lepton flavour ratios for B->Xll decays, e.g.
<Remu>(B->Xsll)
- Added measurements of B->K*mumu "optimised" angular observables (P') by LHCb
Other improvements:
- More consistent behaviour of
flavio.plots.band_plot
when using thepre_calculated_z
andinterpolation_factor
options (see API docs) flavio.plots.q2_plot_exp
now properly handles asymmetric experimental uncertainties
In addition there have been a few under-the-hood changes in preparation for future features.
v0.15.1
This minor release fixes the following bugs:
- An erroneous factor of 1/2 in the angular observables AFB, FH, as well as the direct CP asymmetry in B->Pll decays was removed. Note that these quantities are compatible with zero in the SM, so this is relevant in the presence of new physics. Thanks to Christoph Niehoff for pointing this out.
- The plot function
q2_plot_th_bin
now also works for non-SM Wilson coefficients (#24, thanks @f-thiele)
As a new feature, a function np_uncertainty
was added that works just like sm_uncertainty
but allows to specify non-SM Wilson coefficients. This allows to compute the uncertainties in the presence of new physics (see API docs).
v0.15
The major new physics feature in this release is the addition of the branching ratio of the rare inclusive B→Xql+l- (l=e or μ) decay. Many thanks to Tobias Huber for providing analytical expressions and making numerical cross-checks that made the implementation of (most of) the corrections in arXiv:1503.04849 possible. Thanks also to the Munich Institute for Astro- and Particle Physics (MIAPP) for hospitality during this effort.
Another new feature is more cosmetic: metadata have been added to all observables, making it possible to split the list of observables into categories rather than one huge list.
Finally, thanks to Christoph Niehoff for improvements and cross-checks in Bq→l+l-.
v0.14.1
This release does not bring any new features but various speed optimisations, most significantly:
v0.14
News in this release:
- New observable: effective lifetime in Bs→ll added. Thanks to @ChristophNiehoff for this addition!
- Improvements in the plotting function
flavio.plots.band_plot
- It is now possible to save the data corresponding to any individual likelihood plot, which allows to easily change or rearrange plots later without having to recompute the likelihood. Thanks to Albert Puig for this addition!
- It is now possible to interpolate the likelihood in between computed points to obtain smoother contours without computing on a finer grid. For the time being, this feature must be activated manually by setting
interpolation_factor
to an integer greater than 1.
- Typos in measurements fixed and measurements of Λb→Λμμ added
v0.13.1
This point release features a few minor improvements:
- Two typos in measurements fixed (thanks to S. Reichert for spotting one of them)
- Improvement in plot functions:
band_plot
now plots multiple confidence levels and supports legends out of the box - Stability improvements for
FastFit
v0.13
The focus in this release is on experimental measurements and treatment of likelihoods:
- A large number of measurements by LHCb have been added - thanks to Stefanie Reichert for that!
- While by default only the most recent measurements of an observable by a given experiment are included in the code, data files for older measurements can be found in a separate repository now.
- A new function to read measurements from a URL can be useful for this purpose: for instance
flavio.measurements.read_url(
'https://github.com/flav-io/measurements/blob/master/2011-lhcb-bksmumu.yaml')
All existing mesurements can be cleared with the new method flavio.Measurement.clear_all()
.
- The combination of univariate likelihoods has been completely rewritten. For parameters or measurements with several uncertainties, these are now combined by computing the convolution of the PDFs. Here is an example for a symmetric and an asymmetric uncertainty. Thanks to Jens Jasche for discussions.
- For measurements, instead of central values and uncertainties, now also upper limits can be specified. In the YAML data file, they are written simply as a string of the form
< 1.5e-8 @95% CL
- this works for arbitrary confidence levels.
Finally, there are two new observables: the direct CP asymmetries in B→K*ll and B→Kll.
v0.12
This release adds observables in three more decays:
- Branching ratios of D→lν and Ds→lν decays (where l=e or μ) that are used to measure the CKM elements Vcs and Vcd
- Branching ratio and angular observables of Λb→Λll (where l=e or μ) that is based on the b→sll transition and is closely related to B→(K, K*)ll.
The Λb decay is based on the lattice form factors of arXiv:1602.01399 and the angular distribution derived in arXiv:1410.2115. Special thanks to Danny van Dyk for discussions.
On an unrelated note, there is now a repository with example Jupyter notebooks that will showcase some of the features in the future (and contributions are of course welcome).