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This package may be used to evaluate MT2 in all its variants. This includes both symmetric and asymmetric MT2. MT2 is also known as the “stransverse mass”.

This package provides an interface to the bisection algorithm of http://arxiv.org/abs/1411.4312, via an implementation detailed below. The variable MT2 itself is described here. Related information may be found in papers relating to MT2 linked from here.

Getting started

Install from PyPI with e.g. pip:

pip install mt2

One can then compute MT2 as follows; here for the “symmetric” case, where both invisible particles have the same mass:

from mt2 import mt2

# The units of all quantities are the same, e.g. GeV
val = mt2(
    100, 410, 20,  # Visible 1: mass, px, py
    150, -210, -300,  # Visible 2: mass, px, py
    -200, 280,  # Missing transverse momentum: x, y
    100, 100)  # Invisible 1 mass, invisible 2 mass
print("Expected mT2 = 412.628.  Computed mT2 = ", val)

mt2 is also available on conda-forge.

Examples

Vectorisation

The mt2 function supports broadcasting over its arguments if they are array-like. For example, one could scan over a grid of invisible particle masses like so:

n1 = 20
n2 = 20
mass_1 = numpy.linspace(10, 200, n1).reshape((-1, 1))
mass_2 = numpy.linspace(10, 200, n2).reshape((1, -1))

# `val` has shape (n1, n2)
val = mt2(
    100, 410, 20,  # Visible 1: mass, px, py
    150, -210, -300,  # Visible 2: mass, px, py
    -200, 280,  # Missing transverse momentum: x, y
    mass_1, mass_2)  # Invisible 1 mass, invisible 2 mass

Note on performance

With full precision, the main reason to use vectorisation as above is convenience. The time spent in the C++ MT2 calculation is somewhat larger than the overhead introduced by a Python for loop. Vectorising can give a runtime reduction of ⪅30% in this case.

However, the benefit can be more significant when using a lower precision. This corresponds to a larger value for the desired_precision_on_mt2 argument. This is because less time is spent in C++, so proportionally the Python overhead of a for loop is more significant.

Toy MC

A fun example using a toy Monte-Carlo simulation can be viewed in this notebook

Other notes

For further information, see the documentation:

help(mt2)

Also exported is mt2_ufunc. This is the raw implementation as a numpy ufunc. Usage is the same as for mt2, but it supports some additional arguments, like where. The reader should refer to the numpy documentation for a description of these.

Implementation

The underlying implementation of the Lester-Nachman algorithm used in this package is by Rupert Tombs, found in src/mt2_bisect.h. It provides results consistent with the implementation provided with http://arxiv.org/abs/1411.4312, but is 3x to 4x faster. Note that this does not implement the “deci-sectioning” described in the paper, since it is found to provide a more significant performance penalty in the majority of cases. Our version is also scale invariant, and is suitable for large ranges of input magnitude.

The legacy implementation, as it appears on arXiv, is also wrapped and exposed as mt2_arxiv for those that wish to independently cross-check the re-implementation. If you find any discrepancies, please file a bug report! We strongly encourage all users to use the primary mt2 method, due to the higher performance and scale invariance.

Performance

The default installation method via pip uses a precompiled wheel for your platform. If you wish to compile from source for your platform, you could instead install like so:

pip install mt2 --no-binary :all:

Since this can allow use of newer compilers, and code more optimised for your architecture, this can give a small speedup. On the author’s computer, there was 1% runtime reduction as measured with examples/benchmark.py.

License

Please cite:

All files in this repository are released under the MIT license.

Other implementations

A list of alternative implementations of the MT2 calculation can be found here:

https://www.hep.phy.cam.ac.uk/~lester/mt2/#Alternatives

In Python, the other wrapper of the same algorithm known to the authors is by Nikolai Hartmann, here: https://gitlab.cern.ch/nihartma/pymt2

Authors

  • @kesterlester: Original C++ implementation of mT2.

  • @rupt: Current C++ implementation used in this package.

  • @tpgillam: Python packaging

Project links

Data verified by PyPI on Oct 9, 2025
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Latest release

1 maintainer

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Avatar for tpgillam from gravatar.com tpgillam

Credits

Author: Rupert Tombs, Christopher Lester

GitHub Statistics

Data verified by PyPI on Oct 9, 2025
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.9

Tags

mt2

Classifiers

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Release files for mt2 1.3.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mt2 1.3.1
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mt2-1.3.1.tar.gz 36.9 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for mt2 1.3.1
File
mt2-1.3.1-cp314-cp314t-win_arm64.whl 52.0 kB CPython 3.14 CPython 3.14 free-threading Windows ARM64 Details
mt2-1.3.1-cp314-cp314t-win_amd64.whl 55.4 kB CPython 3.14 CPython 3.14 free-threading Windows x86-64 Details
mt2-1.3.1-cp314-cp314t-musllinux_1_2_x86_64.whl 1.1 MB CPython 3.14 CPython 3.14 free-threading Linux musl 1.2+ x86-64 Details
mt2-1.3.1-cp314-cp314t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl 122.0 kB CPython 3.14 CPython 3.14 free-threading Linux glibc 2.12+ x86-64, Linux glibc 2.28+ x86-64 Details
mt2-1.3.1-cp314-cp314t-macosx_11_0_arm64.whl 51.7 kB CPython 3.14 CPython 3.14 free-threading macOS 11.0+ ARM64 Details
mt2-1.3.1-cp314-cp314t-macosx_10_13_universal2.whl 73.6 kB CPython 3.14 CPython 3.14 free-threading macOS 10.13+ universal2 (ARM64, x86-64) Details
mt2-1.3.1-cp314-cp314-win_arm64.whl 51.8 kB CPython 3.14 CPython 3.14 Windows ARM64 Details
mt2-1.3.1-cp314-cp314-win_amd64.whl 55.1 kB CPython 3.14 CPython 3.14 Windows x86-64 Details
mt2-1.3.1-cp314-cp314-musllinux_1_2_x86_64.whl 1.1 MB CPython 3.14 CPython 3.14 Linux musl 1.2+ x86-64 Details
mt2-1.3.1-cp314-cp314-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl 120.1 kB CPython 3.14 CPython 3.14 Linux glibc 2.28+ x86-64, Linux glibc 2.12+ x86-64 Details
mt2-1.3.1-cp314-cp314-macosx_11_0_arm64.whl 51.5 kB CPython 3.14 CPython 3.14 macOS 11.0+ ARM64 Details
mt2-1.3.1-cp314-cp314-macosx_10_13_universal2.whl 73.3 kB CPython 3.14 CPython 3.14 macOS 10.13+ universal2 (ARM64, x86-64) Details
mt2-1.3.1-cp313-cp313t-win_arm64.whl 51.0 kB CPython 3.13 CPython 3.13 free-threading Windows ARM64 Details
mt2-1.3.1-cp313-cp313t-win_amd64.whl 54.5 kB CPython 3.13 CPython 3.13 free-threading Windows x86-64 Details
mt2-1.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl 1.1 MB CPython 3.13 CPython 3.13 free-threading Linux musl 1.2+ x86-64 Details
mt2-1.3.1-cp313-cp313t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl 122.1 kB CPython 3.13 CPython 3.13 free-threading Linux glibc 2.28+ x86-64, Linux glibc 2.12+ x86-64 Details
mt2-1.3.1-cp313-cp313t-macosx_11_0_arm64.whl 51.8 kB CPython 3.13 CPython 3.13 free-threading macOS 11.0+ ARM64 Details
mt2-1.3.1-cp313-cp313t-macosx_10_13_universal2.whl 73.7 kB CPython 3.13 CPython 3.13 free-threading macOS 10.13+ universal2 (ARM64, x86-64) Details
mt2-1.3.1-cp313-cp313-win_arm64.whl 50.9 kB CPython 3.13 CPython 3.13 Windows ARM64 Details
mt2-1.3.1-cp313-cp313-win_amd64.whl 54.2 kB CPython 3.13 CPython 3.13 Windows x86-64 Details
mt2-1.3.1-cp313-cp313-musllinux_1_2_x86_64.whl 1.1 MB CPython 3.13 CPython 3.13 Linux musl 1.2+ x86-64 Details
mt2-1.3.1-cp313-cp313-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl 120.1 kB CPython 3.13 CPython 3.13 Linux glibc 2.28+ x86-64, Linux glibc 2.12+ x86-64 Details
mt2-1.3.1-cp313-cp313-macosx_11_0_arm64.whl 51.6 kB CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
mt2-1.3.1-cp313-cp313-macosx_10_13_universal2.whl 73.3 kB CPython 3.13 CPython 3.13 macOS 10.13+ universal2 (ARM64, x86-64) Details
mt2-1.3.1-cp312-cp312-win_arm64.whl 50.9 kB CPython 3.12 CPython 3.12 Windows ARM64 Details
mt2-1.3.1-cp312-cp312-win_amd64.whl 54.2 kB CPython 3.12 CPython 3.12 Windows x86-64 Details
mt2-1.3.1-cp312-cp312-musllinux_1_2_x86_64.whl 1.1 MB CPython 3.12 CPython 3.12 Linux musl 1.2+ x86-64 Details
mt2-1.3.1-cp312-cp312-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl 120.2 kB CPython 3.12 CPython 3.12 Linux glibc 2.28+ x86-64, Linux glibc 2.12+ x86-64 Details
mt2-1.3.1-cp312-cp312-macosx_11_0_arm64.whl 51.6 kB CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
mt2-1.3.1-cp312-cp312-macosx_10_13_universal2.whl 73.3 kB CPython 3.12 CPython 3.12 macOS 10.13+ universal2 (ARM64, x86-64) Details
mt2-1.3.1-cp311-cp311-win_arm64.whl 50.8 kB CPython 3.11 CPython 3.11 Windows ARM64 Details
mt2-1.3.1-cp311-cp311-win_amd64.whl 54.2 kB CPython 3.11 CPython 3.11 Windows x86-64 Details
mt2-1.3.1-cp311-cp311-musllinux_1_2_x86_64.whl 1.1 MB CPython 3.11 CPython 3.11 Linux musl 1.2+ x86-64 Details
mt2-1.3.1-cp311-cp311-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl 119.7 kB CPython 3.11 CPython 3.11 Linux glibc 2.12+ x86-64, Linux glibc 2.28+ x86-64 Details
mt2-1.3.1-cp311-cp311-macosx_11_0_arm64.whl 51.6 kB CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
mt2-1.3.1-cp311-cp311-macosx_10_9_universal2.whl 73.2 kB CPython 3.11 CPython 3.11 macOS 10.9+ universal2 (ARM64, x86-64) Details
mt2-1.3.1-cp310-cp310-win_amd64.whl 54.2 kB CPython 3.10 CPython 3.10 Windows x86-64 Details
mt2-1.3.1-cp310-cp310-musllinux_1_2_x86_64.whl 1.1 MB CPython 3.10 CPython 3.10 Linux musl 1.2+ x86-64 Details
mt2-1.3.1-cp310-cp310-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl 119.6 kB CPython 3.10 CPython 3.10 Linux glibc 2.12+ x86-64, Linux glibc 2.28+ x86-64 Details
mt2-1.3.1-cp310-cp310-macosx_11_0_arm64.whl 51.6 kB CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details
mt2-1.3.1-cp310-cp310-macosx_10_9_universal2.whl 73.2 kB CPython 3.10 CPython 3.10 macOS 10.9+ universal2 (ARM64, x86-64) Details
mt2-1.3.1-cp39-cp39-win_amd64.whl 54.2 kB CPython 3.9 CPython 3.9 Windows x86-64 Details
mt2-1.3.1-cp39-cp39-musllinux_1_2_x86_64.whl 1.1 MB CPython 3.9 CPython 3.9 Linux musl 1.2+ x86-64 Details
mt2-1.3.1-cp39-cp39-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl 119.5 kB CPython 3.9 CPython 3.9 Linux glibc 2.12+ x86-64, Linux glibc 2.28+ x86-64 Details
mt2-1.3.1-cp39-cp39-macosx_11_0_arm64.whl 51.5 kB CPython 3.9 CPython 3.9 macOS 11.0+ ARM64 Details
mt2-1.3.1-cp39-cp39-macosx_10_9_universal2.whl 73.2 kB CPython 3.9 CPython 3.9 macOS 10.9+ universal2 (ARM64, x86-64) Details

Total release size: 11.9 MB

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Release files / mt2-1.3.1-cp311-cp311-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl

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Release files / mt2-1.3.1-cp39-cp39-win_amd64.whl

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47 release files

1.3.0

45 release files

1.2.3

41 release files

1.2.2

33 release files

1.2.1

33 release files

1.2.0

29 release files

1.1.0

29 release files

1.0.0

29 release files

0.2.0

29 release files

0.1.6

36 release files

0.1.5

1 release file

0.1.4

1 release file

0.1.3

1 release file

0.1.2

1 release file

0.1.1

1 release file

0.1.0

1 release file

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