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LDMX.Biasing package

Submodules

LDMX.Biasing.eat module

Example configurations for producing biased interactions in the ECal.

This module is built for the ECal as Target (EaT) analysis channel where we are studying the events that have a primary electron reaching the ECal at nearly full energy.

Example

from LDMX.Biasing import eat

LDMX.Biasing.eat.dark_brem(ap_mass, db_event_lib, detector, generator, scale_APrime=False, decay_mode='no_decay', ap_tau=-1.0, dist_decay_min=0.0, dist_decay_max=1.0)

Example configuration for producing dark brem interactions in the ECal.

This configures the simulator to fire a 4 GeV electron upstream of the tagger tracker. The electron is allowed to propagate into the ECal where the dark-photon production cross-section is biased up. Only events that result in a dark-photon being produced in the ECal are kept.

Parameters:

• ap_mass (float) – The mass of the A’ in MeV.

• db_event_lib (str) – The path to the reference library to use as vertices of the dark brem.

• detector (str) – Path to the detector.

• generator (simcfg.PrimaryGenerator) – Beam generator for this simulation which should be a ParticleGun so we can configure the PrimaryToEcalFilter to select events where beam electrons retain 87.5% of their energy.

• scale_APrime (bool) – Whether to scale the A’ momentum along with the recoil electron.

• decay_mode (str) – The A’ decay mode. Either no_decay, flat_decay, or geant_decay

• ap_tau (float) – The A’ decay lifetime in seconds

• dist_decay_min (float) – The minimum lab-frame distance at which to decay the A’

• dist_decay_max (float) – The maximum lab-frame distance at which to decay the A’

Return type:

Instance of the simulator configured for dark-brem production in the ECal.

Example

from LDMX.SimCore import generators eat_ap_sim = eat.dark_brem(

1000., ‘path/to/1GeV_mA_library’, ‘ldmx-det-v14’, generators.single_e_4gev_upstream_tagger()

)

LDMX.Biasing.eat.midshower_dimuon(detector, generator, bias_factor, bias_threshold, min_dimuon_energy)

Example configuration for producing mid-shower dimuon interactions in the ECal that fake a missing energy (ME) signal.

In this particular example, 4 GeV electrons are fired upstream of the tagger tracker. Then simulation events are then put through a series of filters.

Parameters:

• detector (str) – Name of the detector

• generator (simcfg.PrimaryGenerator) – Beam generator for this simulation which should be a ParticleGun so we can configure the PrimaryToEcalFilter to select events where beam electrons retain 87.5% of their energy.

• bias_factor (float) – Factor to multiply GammaToMuPair xsec by within the ECal

• bias_threshold (float) – Minium energy [MeV] to bias a photon

• min_dimuon_energy (float) – Minium total energy [MeV] that went to muons to keep event

Returns:

configured for mid-shower dimuon interactions and far-upstream generator

Return type:

simulator

Example

from LDMX.Biasing import eat from LDMX.SimCore import generators eat_dimuon = eat.midshower_dimuon(

‘ldmx-det-v14’, generators.single_e_4gev_upstream_tagger(), 1e4, 500., 1000.

)

LDMX.Biasing.eat.midshower_nuclear(detector, generator, bias_factor, bias_threshold, min_nuclear_energy)

Example configuration for producing mid-shower nuclear interactions in the ECal that fake a missing energy (ME) signal.

In this particular example, 4 GeV electrons are fired upstream of the tagger tracker. Then simulation events are then put through a series of filters.

Parameters:

• detector (str) – Name of the detector

• generator (simcfg.PrimaryGenerator) – Beam generator for this simulation which should be a ParticleGun so we can configure the PrimaryToEcalFilter to select events where beam electrons retain 87.5% of their energy.

• bias_factor (float) – Factor to multiply EN/PN xsecs by

• bias_threshold (float) – Minium energy [MeV] to bias a particle

• min_nuclear_energy (float) – Minium total energy [MeV] that went nuclear to keep event

Returns:

configured for mid-shower nuclear interactions and far-upstream generator

Return type:

simulator

Example

from LDMX.Biasing import eat from LDMX.SimCore import generators eat_dimuon = eat.midshower_nuclear(

‘ldmx-det-v14’, generators.single_e_4gev_upstream_tagger(), 200, 1500., 2500.

)

LDMX.Biasing.ecal module

Example configurations for producing biased interactions in the ECal.

Example

from LDMX.Biasing import ecal

LDMX.Biasing.ecal.deep_photo_nuclear(detector, generator, bias_threshold, processes, ecal_min_Z, require_photon_fromTarget=False)

LDMX.Biasing.ecal.gamma_mumu(detector, generator)

Example configuration for biasing gamma to mu+ mu- conversions in the ecal.

In this particular example, 4 GeV elecrons are fired upstream of the tagger tracker. The TargetBremFilter filters out all events that don’t produce a brem in the target with an energy greater than 2.5 GeV.

Parameters:

detector (str) – Path to the detector

Return type:

Instance of the sim configured for target gamma to muon conversions.

Example

ecal_mumu_sim = ecal.gamma_mumu(‘ldmx-det-v12’)

LDMX.Biasing.ecal.nonfiducial_photo_nuclear(detector, generator)

LDMX.Biasing.ecal.photo_nuclear(detector, generator)

Example configuration for producing photo-nuclear reactions in the ECal.

In this particular example, 4 GeV electrons are fired upstream of the tagger tracker. The TargetBremFilter filters out all events that don’t produced a brem in the target with an energy greater than 2.5 GeV. The brems are allowed to propagate to the ECal at which point they are checked by the EcalProcessFilter. Only events that see the brem photon undergo a photo-nucler reaction in the ECal are kept.

Parameters:

• detector (str) – Path to the detector

• generator (PrimaryGenerator) – generator to use

Return type:

Instance of the simulator configured for ECal photo-nuclear.

Example

ecal_pn_sim = ecal.photo_nuclear(‘ldmx-det-v12’)

LDMX.Biasing.filters module

filters Examples of how each filter is configured for biased MC generation.

The configurations below reflect the nominal values currently being used for large scale production. When debugging, it’s best to create these generators directly.

class LDMX.Biasing.filters.DeepEcalProcessFilter(bias_threshold, processes, ecal_min_Z, require_photon_fromTarget)

Bases: UserAction

Configuration for keeping events where the pn happens deep in the ECAL.

Parameters:

• bias_threshold (double) – Threshold for minimum energy that the products should have

• processes (vector of str) – The allowed processes that can happen deep inside the ECAL, default is conversion (conv) and photoelectron (photo)

• ecal_min_Z (double) – Minimum Z location where the deep process should happen

• require_photon_fromTarget (bool) – Require that the hard brem photon originates from the target Default is False

class LDMX.Biasing.filters.EcalDarkBremFilter(minApEnergy)

Bases: UserAction

Configuration for filtering A’ events

Parameters:

minApEnergy (float) – Minimum A’ energy to keep the event [MeV]

class LDMX.Biasing.filters.EcalProcessFilter(process='photonNuclear')

Bases: UserAction

Configuration for filtering events that don’t see a hard brem undergo a photo-nuclear reaction in the ECal.

Parameters:

process (str) – Geant4 process to look for in the ecal

class LDMX.Biasing.filters.MidShowerDiMuonBkgdFilter(thresh)

Bases: UserAction

Configuration used to reject events that don’t have enough energy given to muon conversion

Parameters:

thresh (float) – Minimum energy [MeV] that needs to go into creating the muons

class LDMX.Biasing.filters.MidShowerNuclearBkgdFilter(thresh)

Bases: UserAction

Configuration used to reject events that don’t have enough energy given to the input process.

Parameters:

thresh (float) – Minimum energy [MeV] that the kinetic energy of the products needs to sum to

class LDMX.Biasing.filters.NonFiducialFilter(recoil_max_momentum=1500.0, abort_fid_event=True)

Bases: UserAction

Configuration for rejecting events that are fiducial.

Parameters:

• recoil_max_p (float) – Maximum momentum the recoil electron can have [MeV]

• abort_fiducial (bool) – If true, aborts fiducial events. Otherwise, the fiducial events will be only tagged

class LDMX.Biasing.filters.PrimaryToEcalFilter(thresh)

Bases: UserAction

Configuration used to reject events where the primary doesn’t reach the ecal with a mimimum energy

Parameters:

thresh (float) – Minimum energy [MeV] that primary electron should have when hitting ecal

class LDMX.Biasing.filters.TaggerHitFilter(layersHit=8)

Bases: UserAction

Configuration used to reject off-energy electrons in the tagger tracker. :param layers_hit: Minimum number of tagger layers with a hit needed to persist the event. :type layers_hit: int

class LDMX.Biasing.filters.TaggerVetoFilter(thresh=3800.0, reject_events_missing_tagger=True)

Bases: UserAction

Configuration used to reject off-energy electrons in the tagger tracker.

Parameters:

• thresh (float) – Minimum energy [MeV] that electron should have

• reject_events_missing_tagger (bool) – Also veto events where the primary particle misses the tagger region

class LDMX.Biasing.filters.TargetBremFilter(recoil_max_p=1500.0, brem_min_e=2500.0)

Bases: UserAction

Configuration for filtering events that don’t see a hard brem in the target.

An event is vetoed if one of two conditions is satisfied: 1) The recoil electron exits the target area with an energy above 1500 MeV 2) The recoil electorn brems, but the energy of at least one of the brems isn’t above 2500 MeV.

Parameters:

• recoil_max_p (float) – Maximum momentum the recoil electron can have [MeV]

• brem_min_e (float) – Minimum energy the brem photon can have [MeV]

kill_recoil_track

Should we kill the recoil electron track for a worst case scenario?

Type:

bool

class LDMX.Biasing.filters.TargetDarkBremFilter(minApEnergy)

Bases: UserAction

Configuration for filtering A’ events

Parameters:

minApEnergy (float) – Minimum A’ energy to keep the event [MeV]

class LDMX.Biasing.filters.TargetENFilter(recoil_thresh=2500.0)

Bases: UserAction

Configuration for filtering electro-nuclear events in the target.

Parameters:

recoil_thresh (float) – Maximum energy recoil electron is allowed to have [MeV]

class LDMX.Biasing.filters.TargetGammaMuMuFilter

Bases: UserAction

Configuration for filtering muon conversion events in the target.

class LDMX.Biasing.filters.TargetPNFilter

Bases: UserAction

Configuration for filtering photo-nuclear events in the target.

LDMX.Biasing.include module

Include the Biasing module

LDMX.Biasing.include.library()

Attach the name of Biasing library to the process

LDMX.Biasing.particle_filter module

Example configurations for filtering out PN events by the properties of of the reaction products.

class LDMX.Biasing.particle_filter.PhotoNuclearProductsFilter(name)

Bases: UserAction

Configuration for keeping events with specific products of PN interactions

Parameters:

name (str) – Name for this filter

pdg_ids

List of PDG product IDs to look for in PN products

Type:

list of ints

kaon()

Configuration for filtering photo-nuclear events whose products don’t contain a kaon.

Return type:

Instance of configured PhotoNuclearProductsFilter

class LDMX.Biasing.particle_filter.PhotoNuclearTopologyFilter(name, filter_class)

Bases: UserAction

Configuration for keeping events with a PN reaction producing a particular event topology.

Parameters:

• name (str) – Name for this filter

• filter_class – Name of the class that implements this filter. Should correspond to the invocation of DECLARE_ACTION in PhotoNuclearTopologyFilters.cxx

hard_particle_threshold

The kinetic energy threshold required to count a particle as “hard”

Type:

float

count_light_ions

Whether or not light ions (e.g. deutrons) should be considered when applying the filter. Setting this to False can produce misleading results such as classifying events with very high kinetic energy light ions as “Nothing Hard” but it is the current default choice in the PhotoNuclearDQM.

Type:

bool

NothingHardFilter()

Configuration for keeping photonuclear events no particles received significant kinetic energy.

Return type:

Instance of configured PhotoNuclearTopologyFilter

SingleNeutronFilter()

Configuration for keeping photonuclear events where a single neutron carries most of the kinetic energy from the interaction

Return type:

Instance of configured PhotoNuclearTopologyFilter

LDMX.Biasing.target module

Example configurations for producing biased interactions in the target.

Example

from LDMX.Biasing import target

LDMX.Biasing.target.dark_brem(ap_mass, lhe, detector)

Example configuration for producing dark brem interactions in the target.

This configures the sim to fire a 4 GeV electron upstream of the tagger tracker. The dark-photon production cross-section is biased up in the target. Only events that result in a dark-photon being produced in the target are kept.

Parameters:

• ap_mass (float) – The mass of the A’ in MeV.

• lhe (str) – The path to the directory containing LHE files to use as events of the dark brem.

• detector (str) – Name of detector to simulate in

Return type:

Instance of the sim configured for dark-brem production in the target.

Example

Here we use the example vertex library. This should not be used for large (>50k) event samples.

from LDMX.SimCore import makePath target_ap_sim = target.dark_brem(1000., makePath.makeLHEPath(1000.), ‘ldmx-det-v12’)

In general, the second argument should be the full path to the LHE event library.

target_ap_sim = target.dark_brem(1000, ‘path/to/lhe’, ‘ldmx-det-v12’)

LDMX.Biasing.target.electro_nuclear(detector, generator)

Example configuration for producing electro-nuclear reactions in the target.

In this particular example, 4 GeV electrons are fired upstream of the tagger tracker. TargetENFilter filters out events that don’t see an electro-nuclear reaction take places in the target.

Parameters:

detector (str) – Path to the detector

Return type:

Instance of the sim configured for target electro-nuclear.

Example

target_en_sim = target.electro_nuclear(‘ldmx-det-v12’)

LDMX.Biasing.target.gamma_mumu(detector, generator)

Example configuration for biasing gamma to mu+ mu- conversions in the target.

In this particular example, 4 GeV electrons are fired upstream of the tagger tracker. The TargetBremFilter filters out all events that don’t produced a brem in the target with an energy greater than 2.5 GeV.

Parameters:

detector (str) – Path to the detector

Return type:

Instance of the sim configured for target gamma to muon conversions.

Example

target_mumu_sim = target.gamma_mumu(‘ldmx-det-v12’)

LDMX.Biasing.target.photo_nuclear(detector, generator)

Example configuration for producing photo-nuclear reactions in the ECal.

In this particular example, 4 GeV electrons are fired upstream of the tagger tracker. The TargetBremFilter filters out all events that don’t produced a brem in the target with an energy greater than 2.5 GeV. TargetBremFilter filters out events that don’t see the brem photon undergo a photo-nuclear reaction in the target.

Parameters:

detector (str) – Path to the detector

Return type:

Instance of the sim configured for target photo-nuclear.

Example

target_pn_sim = target.photo_nuclear(‘ldmx-det-v12’)

LDMX.Biasing.util module

Utility classes for Simulation

class LDMX.Biasing.util.BiasingUtilityAction(instance_name, class_name)

Bases: UserAction

Helpful derived class for this submodule, makes sure the library and namespace are set correctly.

Parameters:

• instance_name (str) – name of this instance

• class_name (str) – name of the class within this submodule

class LDMX.Biasing.util.DecayChildrenKeeper(parents)

Bases: BiasingUtilityAction

Configuration used to store children of specific particle decays

Parameters:

parents (list[int]) – list of PDG ID of particles whose decay products we want to keep

class LDMX.Biasing.util.PartialEnergySorter(thresh)

Bases: BiasingUtilityAction

Process particles such that all particles above the input threshold are processed first.

Parameters:

thresh (float) – Minimum energy [MeV] to process a track first

class LDMX.Biasing.util.StepPrinter(track_id=1, process_name='', depth=0)

Bases: BiasingUtilityAction

Print each step of the input track ID

The default track ID is 1 (the primary particle).

Parameters:

track_id (int, optional) – Geant4 track ID to print each step of

class LDMX.Biasing.util.TrackProcessFilter(process_name)

Bases: BiasingUtilityAction

Configuration used to tag all tracks produced via the given process to persist them to the event.

Parameters:

process_name (str) – The Geant4 process name (e.g. photonNuclear) via which the tracks were produced.

conversion()

Configuration used to tag all electron conversion tracks to persist them to the event.

Return type:

Instance of TrackProcessFilter configured to tag conversion tracks.

dark_brem()

Configuration used to tag all dark brem tracks to persist them to the event.

Return type:

Instance of TrackProcessFilter configured to tag dark brem tracks.

electro_nuclear()

Configuration used to tag all electro-nuclear tracks to persist them to the event.

Return type:

Instance of TrackProcessFilter configured to tag electro-nuclear tracks.

electron_brem()

Configuration used to tag all electron brem tracks to persist them to the event.

Return type:

Instance of TrackProcessFilter configured to tag eBrem tracks.

gamma_mumu()

Configuration used to tag all gamma –> mu+ mu- tracks to persist them to the event.

Return type:

Instance of TrackProcessFilter configured to tag gamma –> mu+ mu- tracks.

photo_nuclear()

Configuration used to tag all photo-nuclear tracks to persist them to the event.

Return type:

Instance of TrackProcessFilter configured to tag photo-nuclear tracks.

Module contents