DarkNews 0.4.7

A lightweight nu-A scattering generator for heavy neutral lepton production

DarkNews

DarkNews is an event generator for new physics processes at accelerator neutrino experiments that simulates neutrino upscattering to heavy neutral leptons and their subsequent decays to single photons and di-lepton pairs.

Table of Contents

• Introduction
• Dependencies
• Installation
• Usage
  • Command line functionality
  • Scripting functionality
  • List of parameters
  • Specify parameters via a file
  • The experiments
  • Generated events dataframe

---

Introduction

DarkNews uses Vegas to generate weighted Monte Carlo samples of scattering and decay processes. Differential observables are implemented using analytical expressions with arbitrary interaction vertices, which are then specified at run-time based on the available models and the user's parameter choices. Processes involving heavy neutrinos N are calculated including contributions from the Standard Model Z and W bosons, as well as from a kinetically-mixed dark photon (Z'), and neutrino-N transition magnetic moments. DarkNews also computes the partial decay widths of heavy neutrinos for models with up to 3 heavy neutrinos.

Experiments as well as models are implemented on a case-by-case basis. The necessary ingredients to simulate upscattering or decay-in-flight rates are the active-neutrino flux, detector material, and geometry.

The full information of the event genration is saved to a pandas dataframes, but the user may also choose to print events to numpy ndarrays, as well as to HEPevt-formatted text files.

If you experience any problems or bugs, either open a new issue or contact [e-mail].

Dependencies

Required dependencies:

• Python 3.8 or above
• NumPy

The following dependencies (if missing) will be automatically installed during the main installation of the package:

• scipy
• pandas 1.0 or above
• Cython
• vegas 5.1.1 or above
• Particle
• pyparsing
• dill
• matplotlib

Additional optional dependencies for extras:

DarkNews[parquet]

• pyarrow

DarkNews[pyhepmc]

• pyhepmc

---

Installation

DarkNews is available on PyPI so from your python3.8+ (virtual environment or otherwise), run

python3 -m pip install DarkNews

or if your pip version is already set to your preferred python version, simply pip install DarkNews. This should install all dependencies for you. Installing DarkNews on a virtual environment (using conda for instance) can avoid several pitfalls, including issues with the Cython extension.

troubleshooting: If you have any problems, try creating a brand new (conda or pyenv) environment, install the latest version of pip, then pip install numpy first, and only then try to install pip install DarkNews.

If you experience any issues installing pyhepmc-ng, try installing numpy first, and then install pyhepmc-ng directly from Git using the following command: pip install git+https://[e-mail]/scikit-hep/pyhepmc@master. Then pip install DarkNews.

If your installation is successful, you should be able to

• import the module from your python scripts or notebook with import DarkNews
• run the command line tool dn_gen to generate events on the terminal.

Editable mode / Developing

To make changes to the package or contribute, you can clone the latest repository from GitHub, and from the main folder of the cloned directory, run:

python3 -m pip install -e .

The package will be installed locally in editable mode. Any changes to your local files in the repo will be automatically propagated to your DarkNews installation (except setup configurations). You may want to use Jupyter notebooks with auto reload.

If you experience any problems with pip, you can resort to a local manual installation. After downloading the repository, from the main folder, you can run

python3 setup.py develop

to install it in developer mode (similar to editable mode above).

Extras

If you would like to output events to .parquet files, you can install the following pip install DarkNews[parquet] or pip install "DarkNews[parquet]".

Similarly, to output events to any hepmc2/3 or hepevt formtats, you can install both extras via pip install DarkNews[parquet,pyhepmc] or pip install "DarkNews[parquet,pyhepmc]".

---

Usage

A lot of information on the usage of the generator is provided by the example Jupyter notebooks in the repository.

You can download example notebooks from the repository's folder examples/, or simply run

• dn_get_examples to download the examples folder in the current directory (requires git version >=2.19).

The main usage of DarkNews is covered in depth in the notebook Example_0_start_here.ipynb. We encourage you to read through the cells of the notebook.

Command line functionality

It is possible to run the generator through the script bin/dn_gen, passing the parameters as options.

dn_gen --mzprime=1.25 --m4=0.140 --neval=1000 --HNLtype=dirac --loglevel=INFO

Run dn_gen --help to inspect the meaning of each parameter.

Scripting functionality

It is possible to run the generator by creating an instance of the DarkNews.GenLauncher.GenLauncher class and calling its run method.

from DarkNews import GenLauncher
gen_object = GenLauncher(mzprime=1.25, m4=0.140, neval=1000, HNLtype="dirac")
gen_object.run(loglevel="INFO")

The parameters are passed directly while instantiating the GenLauncher object. Some parameters (loglevel, verbose, logfile, path, overwrite_path) related to the run itself can be passed also within the call of the run() method.

---

Output

Generated events dataframe

If the argument pandas = True (as it is by default), a dataframe containing the generated events is saved in the directory tree.

The dataframe is multi-indexed, where the second column is empty, then there is only the first index.
All dataframes contain the following process:

$\nu$ (P_projectile) + $\mathcal{H}$ (P_target) $\to$ $N_{\rm i}$ (P_decay_N_parent) + $\mathcal{H}$ (P_recoil)

which can be followed by a decay into di-leptons if decay_product=e+e- or decay_product=mu+mu-:

$N_i$ (P_decay_N_parent) $\to$ $N_j$(P_decay_N_daughter) + $\ell^+$ (P_decay_ell_plus) + $\ell^-$ (P_decay_ell_minus)

where $\ell = {e, \mu}$, or if decay_product='photon':

$N_i$ (P_decay_N_parent) $\to$ $N_j$(P_decay_N_daughter) + $\gamma$ (P_decay_photon). Only one of the above decays is allowed per generation.

Here follows a complete description of the pandas dataframe:

Column	Subcolumn	type	description
P_projectile	0, 1, 2, 3	float	4-momenta of beam neutrino
P_decay_N_parent	0, 1, 2, 3	float	4-momenta of HNL_parent
P_target	0, 1, 2, 3	float	4-momenta of nucleus
P_recoil	0, 1, 2, 3	float	4-momenta of recoiled nucleus
P_decay_photon	0, 1, 2, 3	float	4-momenta of photon (if it exists)
P_decay_ell_minus	0, 1, 2, 3	float	4-momenta of e- (if it exists)
P_decay_ell_plus	0, 1, 2, 3	float	4-momenta of e+ (if it exists)
P_decay_N_daughter	0, 1, 2, 3	float	4-momenta of HNL_daughter / nu_daughter
pos_scatt	0, 1, 2, 3	float	upscattering position
pos_decay	0, 1, 2, 3	float	decay position of primary particle (N_parent) -- no secondary decay position is saved.
w_decay_rate_0		float	Weight of the decay rate of primary unstable particle: Σi wi = ΓN
w_decay_rate_1		float	Weight of the decay rate of secondary unstable particle: Σi wi = ΓX
w_event_rate		float	Weight for the event rate: Σi wi = event rate
w_flux_avg_xsec		float	Weight of the flux averaged cross section: Σi wi = int(sigma ⋅ flux) ⋅ exposure
target		string	Name of the target object, it will typically be a nucleus
target_pdgid		int	PDG id of the target
scattering_regime		string	Regime can be coherent or p-elastic
helicity		string	Helicity process: can be flipping or conserving; flipping is suppressed
underlying_process		string	String of the underlying process, e.g, "nu(mu) + proton_in_C12 -> N4 + proton_in_C12 -> nu(mu) + e+ + e- + proton_in_C12"

Additional Attributes

The pandas DataFrame also contains several useful information in attrs. They can be accessed via

df.attrs['foo']

and are specific to the generation run. The attributes are detailed below:

Attrs	type	description
experiment	DarkNews.detector.Detector()	The experiment class of DarkNews containing all information on the experimental setup, including neutrino fluxes, targets, exposure, and geometry (if implemented).
model	DarkNews.model.HNLModel()	The model class of DarkNews with all the couplings and vertices calculated from the user input.
data_path	string	The path used to save the generation outputs.
N{i}_ctau0	float	The proper decay length of the i-th HNL in centimeters used in the generation of events, with i={4,5,6}.

---

Input

List of parameters

This is an exhaustive list of the parameters that can be passed to the program. They can be passed in the command line mode by prepending -- to the name. This summary can also be accessed by running

dn_gen --help

The first column is the name of the parameter, the second is the type or the list of allowed values, the third is a brief explanation and the fourth is the default. Parameters marked as internal can not be specified as they are automatically computed on the basis of other parameters.

Physics parameters

Dark sector spectrum

mzprime	float	Mass of Z'	1.25
m4	float	Mass of the fourth neutrino	0.14
m5	float	Mass of the fifth neutrino	None
m6	float	Mass of the sixth neutrino	None
HNLtype	["dirac", "majorana"]	Dirac or majorana	"majorana"

Mixings

ue4	float		0.0
ue5	float		0.0
ue6	float		0.0
umu4	float		0.0016202
umu5	float		0.0033912
umu6	float		0.0
utau4	float		0.0
utau5	float		0.0
utau6	float		0.0
ud4	float		1.0
ud5	float		1.0
ud6	float		1.0

Couplings

gD	float	U(1)d dark coupling gD	1.0
alphaD	float	U(1)d αdark = (gD2 / 4 π)	internal
epsilon	float	ε	0.01
epsilon2	float	ε2	internal
alpha_epsilon2	float	αQED ⋅ ε2	internal
chi	float	Kinetic mixing parameter	None

Transition magnetic moment

mu_tr_e4	float	TMM mu_tr_e4	0.0
mu_tr_e5	float	TMM mu_tr_e5	0.0
mu_tr_e6	float	TMM mu_tr_e6	0.0
mu_tr_mu4	float	TMM mu_tr_mu4	0.0
mu_tr_mu5	float	TMM mu_tr_mu5	0.0
mu_tr_mu6	float	TMM mu_tr_mu6	0.0
mu_tr_tau4	float	TMM mu_tr_tau4	0.0
mu_tr_tau5	float	TMM mu_tr_tau5	0.0
mu_tr_tau6	float	TMM mu_tr_tau6	0.0
mu_tr_44	float	TMM mu_tr_44	0.0
mu_tr_45	float	TMM mu_tr_45	0.0
mu_tr_46	float	TMM mu_tr_46	0.0
mu_tr_55	float	TMM mu_tr_54	0.0
mu_tr_56	float	TMM mu_tr_55	0.0
mu_tr_56	float	TMM mu_tr_66	0.0

Experiment

experiment	string	The experiment to consider: see this section	"miniboone_fhc"

Monte-Carlo scope

nopelastic	bool	Do not generate proton elastic events	False
nocoh	bool	Do not generate coherent events	False
noHC	bool	Do not include helicity conserving events	False
noHF	bool	Do not include helicity flipping events	False
decay_products	["e+e-","mu+mu-","photon"]	Decay process of interest	"e+e-"
enforce_prompt	bool	Force particles to decay promptly	False
nu_flavors	["nu_e","nu_mu","nu_tau","nu_e_bar","nu_mu_bar","nu_tau_bar"]	Projectile neutrino	["nu_mu"]
sample_geometry	sample_geometry	Whether to sample the detector geometry using DarkNews. If False or a geometry function is not found, the upscattering position is assumed to be (0,0,0,0).	True

Code behavior options

Verbose

loglevel	["INFO", "WARNING", "ERROR", "DEBUG"]	Logging level	"INFO"
verbose	bool	Verbose for logging	False
logfile	string	Path to log file; if not set, use std output	None

vegas integration arguments

neval	int	Number of evaluations of integrand	10000
nint	int	Number of adaptive iterations	20
neval_warmup	int	Number of evaluations of integrand in warmup	1000
nint_warmup	int	Number of adaptive iterations in warmup	10
seed	int	numpy random number generator seed used in vegas	None

Output formats

pandas	bool	Save pandas.DataFrame as .pckl file	True
parquet	bool	Save pandas.DataFrame as .parquet file (engine=pyarrow)	False
numpy	bool	Save events in .npy files	False
hepevt	bool	If true, print events to HEPEVT-formatted text files (does not save event weights)	False
hepevt_legacy	bool	If true, print events to a legacy HEPEVT format (saving weights next to the number of particle in the event and without linebreaks in particle entries)	False
hepmc2	bool	If true, prints events to HepMC2 format.	False
hepmc3	bool	If true, prints events to HepMC3 format.	False
hep_unweight	bool	Unweigh events when printing in HEPEVT format (needs large statistics)	False
unweighted_hep_events	int	number of unweighted events to accept in any of the standard HEP formats. Has to be much smaller than neval for unweight procedure to work.	100
sparse	int	Specify the level of sparseness of the internal dataframe and output. Not supported for HEPevt. Allowed values are 0--3, where: 0: keep all information, including event-by-event descriptions; 1: keep all particle momenta, scattering and decay positions, and all weights; 2: keep only neutrino energy (or 4-momentum in HEPMC/EVT), visible decay products and unstable particle momenta, scattering and decay positions, and all weights; 3: keep only neutrino energy (or 4-momentum in HEPMC/EVT), visible decay products and unstable particle momenta, and all weights; 4: keep only neutrino energy (or 4-momentum in HEPMC/EVT), unstable particle momenta, visible decay products momenta, and w_event_rate. Metadata is always kept if output is pickled.	0
path	string	Path where to save run's outputs	"./"
make_summary_plots	bool	if True, generates summary plots of kinematics in the path	False

Specify parameters via a file

It is possible to specify the parameters through a file, instead of writing each one as an option for the command line functionality or as an argument of GenLauncher instance. The parameter file should be provided as the argument param_file of GenLauncher or via the option --param-file of the command line interface.

A template file template_parameters_file.txt can be found in in the examples directory. In the following there are the main rules to specify the parameters:

• every line should be in the form of an assignment statement

<variable_name> = <variable_value>

• comments can be specified with with "#"

• the different parameters can be specified with:

  • string: by encapsulating each string with double or single quotes "<string>" or '<string>' are equivalent, the escape character is \ (backslash).
  • booleans: by writing True or False (it is case insensitive)
  • mathematical expression (which will results in float or int numbers): see section below
  • lists: by encapsulating them into square brackets, separating each element with a comma; elements can be string, numbers, mathematical expressions or all of them together.

• When using mathematical expression, the following rules should be applied:

  • numbers can be specified as usual: 5 is integer, 5.0 is float (but 5. will result in an error), 5e10 is the float number 5*10^10
  • +, -, *, / are the usual mathematical operators;
  • ^ is used to make powers (do not use **);
  • it is possible to use round brackets ( and )
  • e (case-insensitive, isolated: not inside float numbers) is understood as python math.e = 2.718281828
  • pi (case-insensitive) is understood as math.pi = 3.1415926535
  • sin(<expr>), cos(<expr>), tan(<expr>) are the usual trigonometric functions
  • exp(<expr>) is the usual exponentiation
  • abs(<expr>) is the absolute value
  • sgn(<expr>) = -1 if <expr> < -1e-100, +1 if <expr> > 1e-100, 0 otherwise
  • trunc(<expr>) returns the truncated float <expr> to integer
  • round(<expr>) is the integer part of the float number <expr>
  • sum(<list>) will sum each element of the list, returning a float number
  • any other string is intended as a variable which must have been previously defined (the file is scanned from top to bottom)
  • in general it is possible to define any kind of variable, independently on those that will be actually used by the program, following the usual conventions for the variable name (use only letters, digits and underscores). Moreover, it's not possible to name variables after program-defined names, as for example the name of the functions.

Example 1

The following lines

hbar = 6.582119569e-25 # GeV s
c = 299792458.0 # m s^-1

will define two variables, named hbar and c with their values.

Example 2

It is possible to write

a_certain_constant = hbar * c

to define a variable named a_certain_constant with the value of the product between the pre-defined hbar and c variables from the example above.

Example 3

It is possible to write any kind of possible expression, for example

a_variable = c^2 * 3.2e-4 / sin(PI/7) + 12 * exp( -2 * abs(hbar) )

obtaining a new variable a_variable with the value of 66285419633555.3

Example 4

The line

path = "my_directory/projects/this_project"

defines the path variable, stored as the string "my_directory/projects/this_project".

Example 5

The following lines are defining booleans (they are case insensitive), used to set the various switches:

pandas = True
numpy = false

The experiments

The experiment to use can be specified in two ways through the experiment argument (or --experiment option accordingly):

1. specifying a keyword for a pre-defined experiment among:

   • DUNE near detector FHC ("dune_nd_fhc")
   • DUNE near detector RHC ("dune_nd_rhc")
   • SBND detector ("sbnd")
   • SBND dirt-cylinder ("sbnd_dirt")
   • SBND dirt-cone ("sbnd_dirt_cone")
   • MicroBooNE detector ("microboone")
   • MicroBooNE detector TPC volume only ("microboone_tpc")
   • MicroBooNE dirt-cone ("microboone_dirt")
   • MINERvA detector low-energy NuMI FHC ("minerva_le_fhc")
   • MINERvA detector medium-energy NuMI FHC ("minerva_me_fhc")
   • MINERvA detector medium-energy NuMI RHC ("minerva_me_rhc")
   • MiniBooNE detector FHC ("miniboone_fhc")
   • MiniBooNE dirt-cone FHC ("miniboone_fhc_dirt")
   • ICARUS detector ("icarus")
   • ICARUS dirt-cone ("icarus_dirt")
   • MiniBooNE detector RHC ("miniboone_rhc")
   • MiniBooNE dirt-cone RHC ("miniboone_rhc_dirt")
   • MINOS low-energy NuMI FHC ("minos_le_fhc")
   • T2K ND280 detector FHC ("nd280_fhc")
   • NOVA low-energy NuMI FHC ("nova_le_fhc")
   • FASERnu detector ("fasernu")
   • NuTeV FHC ("nutev_fhc")

2. specifying the file path of an experiment file: every file should be specified using the same rules as for the parameters file, listed in the previous section. A template file template_custom_experiment.txt can be found in in the examples directory. The following parameters must be present (in general it is possible to specify any number of parameters, but only the ones below would be relevant).

name	string	Name of the experiment (your are free to use capital letters, when needed)
fluxfile	string	Path of the fluxes file with respect to the experiment file directory
flux_norm	float	Flux normalization factor: all fluxes should be normalized so that the units are nus/cm²/GeV/POT
erange	list of float	Neutrino energy range [<min>, <max>] in GeV
nuclear_targets	list of string	Detector materials in the form of "<element_name><mass_number>" (e.g. "Ar40")
fiducial_mass	float	Fiducial mass in tons
fiducial_mass_per_target	list of float	Fiducial mass for each target in the same order as the nuclear_targets parameter
POTs	float	Protons on target

Detector geometries

The only geometries currently implemented in DarkNews are:

• MiniBooNE -- a sphere with origin (0,0,0,0) and radius 574.6 cm.
• SBND -- a box with sides 4m x 4m x 5m.
• MicroBooNE -- the geometry of the cryostat. This is just a junction of a tube with two spherical caps.

Times of interactions are always set to 0, and any additional delay due to the N lifetime is taken in to account. All other experiments spit out events with spatial coordinates (0,0,0).

Additional information on the generator engine

DarkNews relies on vegas to integrate and sample differential cross sections and decay rates. The main point of contact with vegas is through the vegas.Integrator class, which will receive the DarkNews integrands (e.g. DarkNews.integrands.UpscatteringHNLDecay()), whose __call__() method will compute the differential rates.

It is possible to set a seed for numpy's random number with the --seed argument, which accepts integer values. This integer seed is then passed to numpy.random.default_rng(), which will then be used by vegas as its random number generator. By default, vegas uses numpy's random number generator ``numpy.random.random()```, which is based on the Mersenne Twister pseudo-random number generator method.

The reproducibility of the generator output (i.e., same vegas samples) is only guaranteed for the same parameters and number of integrand evaluations, which effectively means that the user has to specify the same scope, model parameters, as well as the same number of neval, nint, neval_warmup and nint_warmup.