Metadata-Version: 2.1
Name: minimal-lagrangians
Version: 1.0.0
Summary: A Python program to generate the Lagrangians for dark matter models
Home-page: https://gitlab.com/Socob/minimal-lagrangians
Author: Simon May
Author-email: simon.may@mpa-garching.mpg.de
License: GNU GPLv3
Description: # minimal-lagrangians
        This is a Python program which allows one to specify the field content of an extension
        of the Standard Model of particle physics (SM) and, using this information, generates the
        most general renormalizable Lagrangian that describes such a model. As the program was
        written for the study of minimal dark matter models with radiative neutrino masses,
        it can handle additional fields with the following properties:
        - scalar or Weyl fermion fields
        - SU(3) singlets
        - SU(2) singlets, doublets or triplets
        - arbitrary hypercharge
        - charged under a global ℤ₂ symmetry
        - charged under an arbitrary number of global U(1) symmetries
        
        ## Requirements
        The program requires Python 3 (tested with Python ≥ 3.4).
        No external libraries are necessary.
        
        ## Installation
        `minimal-lagrangians` is [available on pip](https://pypi.org/project/minimal-lagrangians/), so it can simply be installed by running
        
            pip install minimal-lagrangians
        
        ## Usage
        `minimal-lagrangians` only prints the potential involving at least one new (i. e. non-SM) field,
        i. e. the kinetic terms and the Standard Model Lagrangian are omitted. The models are
        not checked for anomalies (tools like SARAH can be used for this purpose).
        
        The new models are currently defined in the file [`data.py`](data.py). Models can be
        added in a user-defined file (to be used with the `--model-file` option, see below) in
        the following form:
        ```python
        [
        	BSMModel('<model_name>', (
        		    # list of fields
        		    # type       name   SU(2) rep.  hypercharge
        		    # for a scalar field, e.g. a scalar doublet with hypercharge 1:
        		    ScalarField ('S',   2,          Y=1),
        		    # for a fermion field, e.g. a fermion singlet with hypercharge 0:
        		    FermionField('F',   1,          Y=0),
        		    # ℤ₂-even scalar field:
        		    ScalarField ('S',   1,          Y=0, z2=1),
        		    # …
        		),
        		# optional: parameter values for different hypercharge assignments (offsets), e.g.
        		(0, 2, …)
        	),
        	# …
        ]
        ```
        
        To add fields with global U(1) charges, use the optional parameter `u1`:
        ```python
                # type       name   SU(2) rep.  hypercharge  U(1) charges
                # for a scalar field, e.g. a scalar doublet with hypercharge 1:
                ScalarField ('S',   2,          Y=1,         u1=[ 1, 3, …]),
                # for a fermion field, e.g. a fermion singlet with hypercharge 0:
                FermionField('F',   1,          Y=0,         u1=[-1, 2, …]),
        ```
        The Standard Model fields are assumed to be neutral (`z2=1`, U(1) charges zero) under the new global symmetries.
        
        Information on how to run the program on the command line can be obtained with
        `minimal-lagrangians -h`:
        >     usage: minimal-lagrangians [-h] [--format {LaTeX,SARAH,plain}]
        >                                [--model-file [path/to/file.py]] [--omit-equivalent-scalars]
        >                                [--omit-self-interaction] [--list-discarded-terms]
        >                                [--sarah-no-scalar-cpv]
        >                                model [parameter α]
        >     
        >     A Python program to generate the Lagrangians for dark matter models
        >     
        >     positional arguments:
        >       model                 name of the model whose Lagrangian is to be generated (specify
        >                             “list” in order to list all available models)
        >       parameter α           value of the model parameter α (determines hypercharges of the
        >                             fields)
        >     
        >     optional arguments:
        >       -h, --help            show this help message and exit
        >       --format {LaTeX,SARAH,plain}
        >                             output format for the generated Lagrangian (default: plain)
        >       --model-file [path/to/file.py]
        >                             file containing user-defined models; a file is only read if this
        >                             option is present (default: ./models.py)
        >       --omit-equivalent-scalars
        >                             keep only scalar fields from the model which have unique quantum
        >                             numbers and absolute hypercharge values (omit duplicates)
        >       --omit-self-interaction
        >                             omit pure self-interactions of the new fields in the Lagrangian,
        >                             that is, output only interaction terms which involve both SM and
        >                             new fields (default: output all terms)
        >       --list-discarded-terms
        >                             list redundant terms which were discarded from the Lagrangian
        >                             due to identities
        >       --sarah-no-scalar-cpv
        >                             assume that there is no CP violation causing mixing between
        >                             scalar and pseudoscalar fields for SARAH output
        
        
        Test cases can be run using
        
            ./test.py
        
        Among other checks, this currently tests whether the program produces the correct
        Lagrangian for the following models:
        - T1-3-B with α = 0, which is studied in [(Fiaschi, Klasen, May; arXiv:1812.11133 [hep-ph])][1].
        - T1-1-A with α = 0, as given in [(Farzan; arXiv:0908.3729 [hep-ph])][2], which
          presents an implementation of this model.
        - The models given in [(Cheung, Sanford; arXiv:1311.5896 [hep-ph])][3]:
          - singlet–doublet fermion model (SDF, “model A”)
          - singlet–doublet scalar model (SDS, “model B”)
          - singlet–triplet scalar model (STS, “model C”)
        - The Higgs triplet model (→ seesaw type II), see e. g.
          [(Kanemura, Yagyu; arXiv:1201.6287 [hep-ph])][4].
        
        ## Examples
        For example, running
        
            minimal-lagrangians --omit-equivalent-scalars T1-1-A 0
        
        prints the Lagrangian for the model T1-1-A with α = 0 from
        [(Restrepo, Zapata, Yaguna; arXiv:1308.3655 [hep-ph])][5]:
        >      - M_ϕ'² ϕ'^† ϕ' - ½ M_φ² φ²
        >      - (λ₁ (H ϕ') φ + H.c.)
        >      - λ₂ (H^† H) (ϕ'^† ϕ') - λ₃ (H^† ϕ') (ϕ'^† H) - λ₄ (ϕ'^† ϕ')² - λ₅ (H^† H) φ² - λ₆ (ϕ'^† ϕ') φ² - (λ₇ (H ϕ')² + H.c.) - λ₈ φ⁴
        >      - (½ M_ψ ψ ψ + H.c.)
        >      - (y₁ (ϕ'^† L) ψ + H.c.)
        
        Running
        
            minimal-lagrangians STS
        
        prints the Lagrangian for model C (singlet–triplet scalar) from
        [(Cheung, Sanford; arXiv:1311.5896 [hep-ph])][3]:
        >      - ½ M_T² Tr(T²) - ½ M_S² S²
        >      - λ₁ H^† T² H - λ₂ (H^† T H) S - λ₃ (H^† H) S² - λ₄ Tr(T²)² - λ₅ Tr(T²) S² - λ₆ S⁴
        
        
        
        [1]: https://arxiv.org/abs/1812.11133
        [2]: https://arxiv.org/abs/0908.3729
        [3]: https://arxiv.org/abs/1311.5896
        [4]: https://arxiv.org/abs/1201.6287
        [5]: https://arxiv.org/abs/1308.3655
        
        
Keywords: particle physics,beyond the standard model,dark matter
Platform: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Requires-Python: >=3.4
Description-Content-Type: text/markdown
