Package madgraph :: Package loop :: Module loop_diagram_generation
[hide private]
[frames] | no frames]

Source Code for Module madgraph.loop.loop_diagram_generation

   1  # 
   2  ################################################################################ 
   3  # Copyright (c) 2009 The MadGraph5_aMC@NLO Development team and Contributors 
   4  # 
   5  # This file is a part of the MadGraph5_aMC@NLO project, an application which  
   6  # automatically generates Feynman diagrams and matrix elements for arbitrary 
   7  # high-energy processes in the Standard Model and beyond. 
   8  # 
   9  # It is subject to the MadGraph5_aMC@NLO license which should accompany this  
  10  # distribution. 
  11  # 
  12  # For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch 
  13  # 
  14  ################################################################################ 
  15  """Classes for diagram generation with loop features. 
  16  """ 
  17   
  18  import array 
  19  import copy 
  20  import itertools 
  21  import logging 
  22   
  23  import madgraph.loop.loop_base_objects as loop_base_objects 
  24  import madgraph.core.base_objects as base_objects 
  25  import madgraph.core.diagram_generation as diagram_generation 
  26  import madgraph.various.misc as misc 
  27   
  28  from madgraph import MadGraph5Error 
  29  from madgraph import InvalidCmd 
  30  logger = logging.getLogger('madgraph.loop_diagram_generation') 
31 32 -def ldg_debug_info(msg,val, force=False):
33 # This subroutine has typically quite large DEBUG info. 34 # So even in debug mode, they are turned off by default. 35 # Remove the line below for loop diagram generation diagnostic 36 if not force: return 37 38 flag = "LoopGenInfo: " 39 if len(msg)>40: 40 logger.debug(flag+msg[:35]+" [...] = %s"%str(val)) 41 else: 42 logger.debug(flag+msg+''.join([' ']*(40-len(msg)))+' = %s'%str(val))
43
44 #=============================================================================== 45 # LoopAmplitude 46 #=============================================================================== 47 -class LoopAmplitude(diagram_generation.Amplitude):
48 """NLOAmplitude: process + list of diagrams (ordered) 49 Initialize with a process, then call generate_diagrams() to 50 generate the diagrams for the amplitude 51 """ 52
53 - def default_setup(self):
54 """Default values for all properties""" 55 56 # The 'diagrams' entry from the mother class is inherited but will not 57 # be used in NLOAmplitude, because it is split into the four following 58 # different categories of diagrams. 59 super(LoopAmplitude, self).default_setup() 60 self['born_diagrams'] = None 61 self['loop_diagrams'] = None 62 self['loop_UVCT_diagrams'] = base_objects.DiagramList() 63 # This is in principle equal to self['born_diagram']==[] but it can be 64 # that for some reason the born diagram can be generated but do not 65 # contribute. 66 # This will decide wether the virtual is squared against the born or 67 # itself. 68 self['has_born'] = True 69 # This where the structures obtained for this amplitudes are stored 70 self['structure_repository'] = loop_base_objects.FDStructureList() 71 72 # A list that registers what Lcut particle have already been 73 # employed in order to forbid them as loop particles in the 74 # subsequent diagram generation runs. 75 self.lcutpartemployed=[]
76
77 - def __init__(self, argument=None, loop_filter=None):
78 """Allow initialization with Process. 79 If loop_filter is not None, then it will be applied to all subsequent 80 diagram generation from this LoopAmplitude.""" 81 82 self.loop_filter = loop_filter 83 84 if isinstance(argument, base_objects.Process): 85 super(LoopAmplitude, self).__init__() 86 self.set('process', argument) 87 self.generate_diagrams() 88 elif argument != None: 89 # call the mother routine 90 super(LoopAmplitude, self).__init__(argument) 91 else: 92 # call the mother routine 93 super(LoopAmplitude, self).__init__()
94
95 - def get_sorted_keys(self):
96 """Return diagram property names as a nicely sorted list.""" 97 98 return ['process', 'diagrams', 'has_mirror_process', 'born_diagrams', 99 'loop_diagrams','has_born', 100 'structure_repository']
101
102 - def filter(self, name, value):
103 """Filter for valid amplitude property values.""" 104 105 if name == 'diagrams': 106 if not isinstance(value, base_objects.DiagramList): 107 raise self.PhysicsObjectError, \ 108 "%s is not a valid DiagramList" % str(value) 109 for diag in value: 110 if not isinstance(diag,loop_base_objects.LoopDiagram) and \ 111 not isinstance(diag,loop_base_objects.LoopUVCTDiagram): 112 raise self.PhysicsObjectError, \ 113 "%s contains a diagram which is not an NLODiagrams." % str(value) 114 if name == 'born_diagrams': 115 if not isinstance(value, base_objects.DiagramList): 116 raise self.PhysicsObjectError, \ 117 "%s is not a valid DiagramList" % str(value) 118 for diag in value: 119 if not isinstance(diag,loop_base_objects.LoopDiagram): 120 raise self.PhysicsObjectError, \ 121 "%s contains a diagram which is not an NLODiagrams." % str(value) 122 if name == 'loop_diagrams': 123 if not isinstance(value, base_objects.DiagramList): 124 raise self.PhysicsObjectError, \ 125 "%s is not a valid DiagramList" % str(value) 126 for diag in value: 127 if not isinstance(diag,loop_base_objects.LoopDiagram): 128 raise self.PhysicsObjectError, \ 129 "%s contains a diagram which is not an NLODiagrams." % str(value) 130 if name == 'has_born': 131 if not isinstance(value, bool): 132 raise self.PhysicsObjectError, \ 133 "%s is not a valid bool" % str(value) 134 if name == 'structure_repository': 135 if not isinstance(value, loop_base_objects.FDStructureList): 136 raise self.PhysicsObjectError, \ 137 "%s is not a valid bool" % str(value) 138 139 else: 140 super(LoopAmplitude, self).filter(name, value) 141 142 return True
143
144 - def set(self, name, value):
145 """Redefine set for the particular case of diagrams""" 146 147 if name == 'diagrams': 148 if self.filter(name, value): 149 self['born_diagrams']=base_objects.DiagramList([diag for diag in value if \ 150 not isinstance(diag,loop_base_objects.LoopUVCTDiagram) and diag['type']==0]) 151 self['loop_diagrams']=base_objects.DiagramList([diag for diag in value if \ 152 not isinstance(diag,loop_base_objects.LoopUVCTDiagram) and diag['type']!=0]) 153 self['loop_UVCT_diagrams']=base_objects.DiagramList([diag for diag in value if \ 154 isinstance(diag,loop_base_objects.LoopUVCTDiagram)]) 155 156 else: 157 return super(LoopAmplitude, self).set(name, value) 158 159 return True
160
161 - def get(self, name):
162 """Redefine get for the particular case of '*_diagrams' property""" 163 164 if name == 'diagrams': 165 if self['process'] and self['loop_diagrams'] == None: 166 self.generate_diagrams() 167 return base_objects.DiagramList(self['born_diagrams']+\ 168 self['loop_diagrams']+\ 169 self['loop_UVCT_diagrams']) 170 171 if name == 'born_diagrams': 172 if self['born_diagrams'] == None: 173 # Have not yet generated born diagrams for this process 174 if self['process']['has_born']: 175 if self['process']: 176 self.generate_born_diagrams() 177 else: 178 self['born_diagrams']=base_objects.DiagramList() 179 180 return LoopAmplitude.__bases__[0].get(self, name) #return the mother routine
181 182 # Functions of the different tasks performed in generate_diagram
183 - def choose_order_config(self):
184 """ Choose the configuration of non-perturbed coupling orders to be 185 retained for all diagrams. This is used when the user did not specify 186 any order. """ 187 chosen_order_config = {} 188 min_wgt = self['born_diagrams'].get_min_order('WEIGHTED') 189 # Scan the born diagrams of minimum weight to chose a configuration 190 # of non-perturbed orders. 191 min_non_pert_order_wgt = -1 192 for diag in [d for d in self['born_diagrams'] if \ 193 d.get_order('WEIGHTED')==min_wgt]: 194 non_pert_order_wgt = min_wgt - sum([diag.get_order(order)*\ 195 self['process']['model']['order_hierarchy'][order] for order in \ 196 self['process']['perturbation_couplings']]) 197 if min_non_pert_order_wgt == -1 or \ 198 non_pert_order_wgt<min_non_pert_order_wgt: 199 chosen_order_config = self.get_non_pert_order_config(diag) 200 logger.info("Chosen coupling orders configuration: (%s)"\ 201 %self.print_config(chosen_order_config)) 202 return chosen_order_config
203
205 """If squared orders (other than WEIGHTED) are defined, then they can be 206 used for determining what is the expected upper bound for the order 207 restricting loop diagram generation.""" 208 for order, value in self['process']['squared_orders'].items(): 209 if order.upper()!='WEIGHTED' and order not in self['process']['orders']: 210 # If the bound is of type '>' we cannot say anything 211 if self['process'].get('sqorders_types')[order]=='>': 212 continue 213 # If there is no born, the min order will simply be 0 as it should. 214 bornminorder=self['born_diagrams'].get_min_order(order) 215 if value>=0: 216 self['process']['orders'][order]=value-bornminorder 217 elif self['process']['has_born']: 218 # This means the user want the leading if order=-1 or N^n 219 # Leading term if order=-n. If there is a born diag, we can 220 # infer the necessary maximum order in the loop: 221 # bornminorder+2*(n-1). 222 # If there is no born diag, then we cannot say anything. 223 self['process']['orders'][order]=bornminorder+2*(-value-1)
224
225 - def guess_loop_orders(self, user_orders):
226 """Guess the upper bound for the orders for loop diagram generation 227 based on either no squared orders or simply 'Weighted'""" 228 229 hierarchy = self['process']['model']['order_hierarchy'] 230 231 # Maximum of the hierarchy weigtht among all perturbed order 232 max_pert_wgt = max([hierarchy[order] for order in \ 233 self['process']['perturbation_couplings']]) 234 235 # In order to be sure to catch the corrections to all born diagrams that 236 # the user explicitly asked for with the amplitude orders, we take here 237 # the minimum weighted order as being the maximum between the min weighted 238 # order detected in the Born diagrams and the weight computed from the 239 # user input amplitude orders. 240 user_min_wgt = 0 241 242 # One can chose between the two behaviors below. It is debatable which 243 # one is best. The first one tries to only consider the loop which are 244 # dominant, even when the user selects the amplitude orders and the 245 # second chosen here makes sure that the user gets a correction of the 246 # desired type for all the born diagrams generated with its amplitude 247 # order specification. 248 # min_born_wgt=self['born_diagrams'].get_min_order('WEIGHTED') 249 min_born_wgt=max(self['born_diagrams'].get_min_order('WEIGHTED'), 250 sum([hierarchy[order]*val for order, val in user_orders.items() \ 251 if order!='WEIGHTED'])) 252 253 if 'WEIGHTED' not in [key.upper() for key in \ 254 self['process']['squared_orders'].keys()]: 255 # Then we guess it from the born 256 self['process']['squared_orders']['WEIGHTED']= 2*(min_born_wgt+\ 257 max_pert_wgt) 258 259 # Now we know that the remaining weighted orders which can fit in 260 # the loop diagram is (self['target_weighted_order']- 261 # min_born_weighted_order) so for each perturbed order we just have to 262 # take that number divided by its hierarchy weight to have the maximum 263 # allowed order for the loop diagram generation. Of course, 264 # we don't overwrite any order already defined by the user. 265 if self['process']['squared_orders']['WEIGHTED']>=0: 266 trgt_wgt=self['process']['squared_orders']['WEIGHTED']-min_born_wgt 267 else: 268 trgt_wgt=min_born_wgt+(-self['process']['squared_orders']['WEIGHTED']+1)*2 269 # We also need the minimum number of vertices in the born. 270 min_nvert=min([len([1 for vert in diag['vertices'] if vert['id']!=0]) \ 271 for diag in self['born_diagrams']]) 272 # And the minimum weight for the ordered declared as perturbed 273 min_pert=min([hierarchy[order] for order in \ 274 self['process']['perturbation_couplings']]) 275 276 for order, value in hierarchy.items(): 277 if order not in self['process']['orders']: 278 # The four cases below come from a study of the maximal order 279 # needed in the loop for the weighted order needed and the 280 # number of vertices available. 281 if order in self['process']['perturbation_couplings']: 282 if value!=1: 283 self['process']['orders'][order]=\ 284 int((trgt_wgt-min_nvert-2)/(value-1)) 285 else: 286 self['process']['orders'][order]=int(trgt_wgt) 287 else: 288 if value!=1: 289 self['process']['orders'][order]=\ 290 int((trgt_wgt-min_nvert-2*min_pert)/(value-1)) 291 else: 292 self['process']['orders'][order]=\ 293 int(trgt_wgt-2*min_pert) 294 # Now for the remaining orders for which the user has not set squared 295 # orders neither amplitude orders, we use the max order encountered in 296 # the born (and add 2 if this is a perturbed order). 297 # It might be that this upper bound is better than the one guessed 298 # from the hierarchy. 299 for order in self['process']['model']['coupling_orders']: 300 neworder=self['born_diagrams'].get_max_order(order) 301 if order in self['process']['perturbation_couplings']: 302 neworder+=2 303 if order not in self['process']['orders'].keys() or \ 304 neworder<self['process']['orders'][order]: 305 self['process']['orders'][order]=neworder
306
307 - def filter_from_order_config(self, diags, config, discarded_configurations):
308 """ Filter diags to select only the diagram with the non perturbed orders 309 configuration config and update discarded_configurations.Diags is the 310 name of the key attribute of this class containing the diagrams to 311 filter.""" 312 newdiagselection = base_objects.DiagramList() 313 for diag in self[diags]: 314 diag_config = self.get_non_pert_order_config(diag) 315 if diag_config == config: 316 newdiagselection.append(diag) 317 elif diag_config not in discarded_configurations: 318 discarded_configurations.append(diag_config) 319 self[diags] = newdiagselection
320
321 - def remove_Furry_loops(self, model, structs):
322 """ Remove the loops which are zero because of Furry theorem. So as to 323 limit any possible mistake in case of BSM model, I limit myself here to 324 removing SM-quark loops with external legs with an odd number of photons, 325 possibly including exactly two gluons.""" 326 327 new_diag_selection = base_objects.DiagramList() 328 329 n_discarded = 0 330 for diag in self['loop_diagrams']: 331 if diag.get('tag')==[]: 332 raise MadGraph5Error, "The loop diagrams should have been tagged"+\ 333 " before going through the Furry filter." 334 335 loop_line_pdgs = diag.get_loop_lines_pdgs() 336 attached_pdgs = diag.get_pdgs_attached_to_loop(structs) 337 if (attached_pdgs.count(22)%2==1) and \ 338 (attached_pdgs.count(21) in [0,2]) and \ 339 (all(pdg in [22,21] for pdg in attached_pdgs)) and \ 340 (abs(loop_line_pdgs[0]) in list(range(1,7))) and \ 341 (all(abs(pdg)==abs(loop_line_pdgs[0]) for pdg in loop_line_pdgs)): 342 n_discarded += 1 343 else: 344 new_diag_selection.append(diag) 345 346 self['loop_diagrams'] = new_diag_selection 347 348 if n_discarded > 0: 349 logger.debug(("MadLoop discarded %i diagram%s because they appeared"+\ 350 " to be zero because of Furry theorem.")%(n_discarded,'' if \ 351 n_discarded<=1 else 's'))
352 353 @staticmethod
354 - def get_loop_filter(filterdef):
355 """ Returns a function which applies the filter corresponding to the 356 conditional expression encoded in filterdef.""" 357 358 def filter(diag, structs, model, id): 359 """ The filter function generated '%s'."""%filterdef 360 361 loop_pdgs = diag.get_loop_lines_pdgs() 362 struct_pdgs = diag.get_pdgs_attached_to_loop(structs) 363 loop_masses = [model.get_particle(pdg).get('mass') for pdg in loop_pdgs] 364 struct_masses = [model.get_particle(pdg).get('mass') for pdg in struct_pdgs] 365 if not eval(filterdef.lower(),{'n':len(loop_pdgs), 366 'loop_pdgs':loop_pdgs, 367 'struct_pdgs':struct_pdgs, 368 'loop_masses':loop_masses, 369 'struct_masses':struct_masses, 370 'id':id}): 371 return False 372 else: 373 return True
374 375 return filter
376
377 - def user_filter(self, model, structs, filter=None):
378 """ User-defined user-filter. By default it is not called, but the expert 379 user can turn it on and code here is own filter. Some default examples 380 are provided here. 381 The tagging of the loop diagrams must be performed before using this 382 user loop filter""" 383 384 # By default the user filter does nothing if filter is not set, 385 # if you want to turn it on and edit it by hand, then set the 386 # variable edit_filter_manually to True 387 edit_filter_manually = False 388 if not edit_filter_manually and filter in [None,'None']: 389 return 390 if isinstance(filter,str) and filter.lower() == 'true': 391 edit_filter_manually = True 392 filter=None 393 394 395 if filter not in [None,'None']: 396 filter_func = LoopAmplitude.get_loop_filter(filter) 397 else: 398 filter_func = None 399 400 new_diag_selection = base_objects.DiagramList() 401 discarded_diags = base_objects.DiagramList() 402 i=0 403 for diag in self['loop_diagrams']: 404 if diag.get('tag')==[]: 405 raise MadGraph5Error, "Before using the user_filter, please "+\ 406 "make sure that the loop diagrams have been tagged first." 407 valid_diag = True 408 i=i+1 409 410 # Apply the custom filter specified if any 411 if filter_func: 412 try: 413 valid_diag = filter_func(diag, structs, model, i) 414 except Exception as e: 415 raise InvalidCmd("The user-defined filter '%s' did not"%filter+ 416 " returned the following error:\n > %s"%str(e)) 417 418 # if any([abs(pdg) not in range(1,7) for pdg in diag.get_loop_lines_pdgs()]): 419 # valid_diag = False 420 421 # if any([abs(i)!=1000021 for i in diag.get_loop_lines_pdgs()]): 422 # valid_diag=False 423 # if len(diag.get_loop_lines_pdgs())<4: 424 # valid_diag = False 425 426 # connected_id = diag.get_pdgs_attached_to_loop(structs) 427 # if connected_id.count(22)!=2 or not all(abs(pdg) in range(7) for pdg in diag.get_loop_lines_pdgs()): 428 # valid_diag=False 429 430 # Ex. 0: Chose a specific diagram number, here the 8th one for ex. 431 # if i not in [31]: 432 # valid_diag = False 433 434 # Ex. 0: Keeps only the top quark loops. 435 # if any([pdg not in [6,-6] for pdg in diag.get_loop_lines_pdgs()]): 436 # valid_diag = False 437 438 # Ex. 1: Chose the topology, i.e. number of loop line. 439 # Notice that here particles and antiparticles are not 440 # differentiated and always the particle PDG is returned. 441 # In this example, only boxes are selected. 442 # if len(diag.get_loop_lines_pdgs())>2 and \ 443 # any([i in diag.get_loop_lines_pdgs() for i in[24,-24,23]]): 444 # valid_diag=False 445 446 # Ex. 2: Use the pdgs of the particles directly attached to the loop. 447 # In this example, we forbid the Z to branch off the loop. 448 # connected_id = diag.get_pdgs_attached_to_loop(structs) 449 # if 22 not in connected_id: 450 # valid_diag=False 451 452 # Ex. 3: Filter based on the mass of the particles running in the 453 # loop. It shows how to access the particles properties from 454 # the PDG. 455 # In this example, only massive parts. are allowed in the loop. 456 # if 'ZERO' in [model.get_particle(pdg).get('mass') for pdg in \ 457 # diag.get_loop_lines_pdgs()]: 458 # valid_diag=False 459 460 # Ex. 4: Complicated filter which gets rid of all bubble diagrams made 461 # of two vertices being the four gluon vertex and the effective 462 # glu-glu-Higgs vertex. 463 # if len(diag.get_loop_lines_pdgs())==2: 464 # bubble_lines_pdgs=[abs(diag.get('canonical_tag')[0][0]), 465 # abs(diag.get('canonical_tag')[0][0])] 466 # first_vertex_pdgs=bubble_lines_pdgs+\ 467 # [abs(structs.get_struct(struct_ID).get('binding_leg').get('id')) \ 468 # for struct_ID in diag.get('canonical_tag')[0][1]] 469 # second_vertex_pdgs=bubble_lines_pdgs+\ 470 # [abs(structs.get_struct(struct_ID).get('binding_leg').get('id')) \ 471 # for struct_ID in diag.get('canonical_tag')[1][1]] 472 # first_vertex_pdgs.sort() 473 # second_vertex_pdgs.sort() 474 # bubble_vertices=[first_vertex_pdgs,second_vertex_pdgs] 475 # bubble_vertices.sort() 476 # if bubble_vertices==[[21,21,21,21],[21,21,25]]: 477 # valid_diag=False 478 479 # If you need any more advanced function for your filter and cannot 480 # figure out how to implement them. Just contact the authors. 481 482 if valid_diag: 483 new_diag_selection.append(diag) 484 else: 485 discarded_diags.append(diag) 486 487 self['loop_diagrams'] = new_diag_selection 488 if filter in [None,'None']: 489 warn_msg = """ 490 The user-defined loop diagrams filter is turned on and discarded %d loops."""\ 491 %len(discarded_diags) 492 else: 493 warn_msg = """ 494 The loop diagrams filter '%s' is turned on and discarded %d loops."""\ 495 %(filter,len(discarded_diags)) 496 logger.warning(warn_msg)
497
498 - def filter_loop_for_perturbative_orders(self):
499 """ Filter the loop diagrams to make sure they belong to the class 500 of coupling orders perturbed. """ 501 502 # First define what are the set of particles allowed to run in the loop. 503 allowedpart=[] 504 for part in self['process']['model']['particles']: 505 for order in self['process']['perturbation_couplings']: 506 if part.is_perturbating(order,self['process']['model']): 507 allowedpart.append(part.get_pdg_code()) 508 break 509 510 newloopselection=base_objects.DiagramList() 511 warned=False 512 warning_msg = ("Some loop diagrams contributing to this process"+\ 513 " are discarded because they are not pure (%s)-perturbation.\nMake sure"+\ 514 " you did not want to include them.")%\ 515 ('+'.join(self['process']['perturbation_couplings'])) 516 for i,diag in enumerate(self['loop_diagrams']): 517 # Now collect what are the coupling orders building the loop which 518 # are also perturbed order. 519 loop_orders=diag.get_loop_orders(self['process']['model']) 520 pert_loop_order=set(loop_orders.keys()).intersection(\ 521 set(self['process']['perturbation_couplings'])) 522 # Then make sure that the particle running in the loop for all 523 # diagrams belong to the set above. Also make sure that there is at 524 # least one coupling order building the loop which is in the list 525 # of the perturbed order. 526 valid_diag=True 527 if (diag.get_loop_line_types()-set(allowedpart))!=set() or \ 528 pert_loop_order==set([]): 529 valid_diag=False 530 if not warned: 531 logger.warning(warning_msg) 532 warned=True 533 if len([col for col in [ 534 self['process'].get('model').get_particle(pdg).get('color') \ 535 for pdg in diag.get_pdgs_attached_to_loop(\ 536 self['structure_repository'])] if col!=1])==1: 537 valid_diag=False 538 539 if valid_diag: 540 newloopselection.append(diag) 541 self['loop_diagrams']=newloopselection
542 # To monitor what are the diagrams filtered, simply comment the line 543 # directly above and uncomment the two directly below. 544 # self['loop_diagrams'] = base_objects.DiagramList( 545 # [diag for diag in self['loop_diagrams'] if diag not in newloopselection]) 546
547 - def check_factorization(self,user_orders):
548 """ Makes sure that all non perturbed orders factorize the born diagrams 549 """ 550 warning_msg = "All Born diagrams do not factorize the same sum of power(s) "+\ 551 "of the the perturbed order(s) %s.\nThis is potentially dangerous"+\ 552 " as the real-emission diagrams from aMC@NLO will not be consistent"+\ 553 " with these virtual contributions." 554 if self['process']['has_born']: 555 trgt_summed_order = sum([self['born_diagrams'][0].get_order(order) 556 for order in self['process']['perturbation_couplings']]) 557 for diag in self['born_diagrams'][1:]: 558 if sum([diag.get_order(order) for order in self['process'] 559 ['perturbation_couplings']])!=trgt_summed_order: 560 logger.warning(warning_msg%' '.join(self['process'] 561 ['perturbation_couplings'])) 562 break 563 564 warning_msg = "All born diagrams do not factorize the same power of "+\ 565 "the order %s which is not perturbed and for which you have not"+\ 566 "specified any amplitude order. \nThis is potentially dangerous"+\ 567 " as the real-emission diagrams from aMC@NLO will not be consistent"+\ 568 " with these virtual contributions." 569 if self['process']['has_born']: 570 for order in self['process']['model']['coupling_orders']: 571 if order not in self['process']['perturbation_couplings'] and \ 572 order not in user_orders.keys(): 573 order_power=self['born_diagrams'][0].get_order(order) 574 for diag in self['born_diagrams'][1:]: 575 if diag.get_order(order)!=order_power: 576 logger.warning(warning_msg%order) 577 break
578 579 # Helper function
580 - def get_non_pert_order_config(self, diagram):
581 """ Return a dictionary of all the coupling orders of this diagram which 582 are not the perturbed ones.""" 583 return dict([(order, diagram.get_order(order)) for \ 584 order in self['process']['model']['coupling_orders'] if \ 585 not order in self['process']['perturbation_couplings'] ])
586
587 - def print_config(self,config):
588 """Return a string describing the coupling order configuration""" 589 res = [] 590 for order in self['process']['model']['coupling_orders']: 591 try: 592 res.append('%s=%d'%(order,config[order])) 593 except KeyError: 594 res.append('%s=*'%order) 595 return ','.join(res)
596
597 - def generate_diagrams(self, loop_filter=None, diagram_filter=None):
598 """ Generates all diagrams relevant to this Loop Process """ 599 600 # Description of the algorithm to guess the leading contribution. 601 # The summed weighted order of each diagram will be compared to 602 # 'target_weighted_order' which acts as a threshold to decide which 603 # diagram to keep. Here is an example on how MG5 sets the 604 # 'target_weighted_order'. 605 # 606 # In the sm process uu~ > dd~ [QCD, QED] with hierarchy QCD=1, QED=2 we 607 # would have at leading order contribution like 608 # (QED=4) , (QED=2, QCD=2) , (QCD=4) 609 # leading to a summed weighted order of respectively 610 # (4*2=8) , (2*2+2*1=6) , (4*1=4) 611 # at NLO in QCD and QED we would have the following possible contributions 612 # (QED=6), (QED=4,QCD=2), (QED=2,QCD=4) and (QCD=6) 613 # which translate into the following weighted orders, respectively 614 # 12, 10, 8 and 6 615 # So, now we take the largest weighted order at born level, 4, and add two 616 # times the largest weight in the hierarchy among the order for which we 617 # consider loop perturbation, in this case 2*2 wich gives us a 618 # target_weighted_order of 8. based on this we will now keep all born 619 # contributions and exclude the NLO contributions (QED=6) and (QED=4,QCD=2) 620 621 # Use the globally defined loop_filter if the locally defined one is empty 622 if (not self.loop_filter is None) and (loop_filter is None): 623 loop_filter = self.loop_filter 624 625 logger.debug("Generating %s "\ 626 %self['process'].nice_string().replace('Process', 'process')) 627 628 # Hierarchy and model shorthands 629 model = self['process']['model'] 630 hierarchy = model['order_hierarchy'] 631 632 # Later, we will specify the orders for the loop amplitude. 633 # It is a temporary change that will be reverted after loop diagram 634 # generation. We then back up here its value prior modification. 635 user_orders=copy.copy(self['process']['orders']) 636 # First generate the born diagram if the user asked for it 637 if self['process']['has_born']: 638 bornsuccessful = self.generate_born_diagrams() 639 ldg_debug_info("# born diagrams after first generation",\ 640 len(self['born_diagrams'])) 641 else: 642 self['born_diagrams'] = base_objects.DiagramList() 643 bornsuccessful = True 644 logger.debug("Born diagrams generation skipped by user request.") 645 646 # Make sure that all orders specified belong to the model: 647 for order in self['process']['orders'].keys()+\ 648 self['process']['squared_orders'].keys(): 649 if not order in model.get('coupling_orders') and \ 650 order != 'WEIGHTED': 651 if self['process']['orders'][order] == 0: 652 del self['process']['orders'][order] 653 else: 654 raise InvalidCmd("Coupling order %s not found"%order +\ 655 " in any interaction of the current model %s."%model['name']) 656 657 # The decision of whether the virtual must be squared against the born or the 658 # virtual is made based on whether there are Born or not unless the user 659 # already asked for the loop squared. 660 if self['process']['has_born']: 661 self['process']['has_born'] = self['born_diagrams']!=[] 662 self['has_born'] = self['process']['has_born'] 663 664 ldg_debug_info("User input born orders",self['process']['orders']) 665 ldg_debug_info("User input squared orders", 666 self['process']['squared_orders']) 667 ldg_debug_info("User input perturbation",\ 668 self['process']['perturbation_couplings']) 669 670 # Now, we can further specify the orders for the loop amplitude. 671 # Those specified by the user of course remain the same, increased by 672 # two if they are perturbed. It is a temporary change that will be 673 # reverted after loop diagram generation. 674 user_orders=copy.copy(self['process']['orders']) 675 user_squared_orders=copy.copy(self['process']['squared_orders']) 676 677 # If the user did not specify any order, we can expect him not to be an 678 # expert. So we must make sure the born all factorize the same powers of 679 # coupling orders which are not perturbed. If not we chose a configuration 680 # of non-perturbed order which has the smallest total weight and inform 681 # the user about this. It is then stored below for later filtering of 682 # the loop diagrams. 683 chosen_order_config={} 684 if self['process']['squared_orders']=={} and \ 685 self['process']['orders']=={} and self['process']['has_born']: 686 chosen_order_config = self.choose_order_config() 687 688 discarded_configurations = [] 689 # The born diagrams are now filtered according to the chose configuration 690 if chosen_order_config != {}: 691 self.filter_from_order_config('born_diagrams', \ 692 chosen_order_config,discarded_configurations) 693 694 # Before proceeding with the loop contributions, we must make sure that 695 # the born diagram generated factorize the same sum of power of the 696 # perturbed couplings. If this is not true, then it is very 697 # cumbersome to get the real radiation contribution correct and consistent 698 # with the computations of the virtuals (for now). 699 # Also, when MadLoop5 guesses the a loop amplitude order on its own, it 700 # might decide not to include some subleading loop which might be not 701 # be consistently neglected for now in the MadFKS5 so that its best to 702 # warn the user that he should enforce that target born amplitude order 703 # to any value of his choice. 704 self.check_factorization(user_orders) 705 706 # Now find an upper bound for the loop diagram generation. 707 self.guess_loop_orders_from_squared() 708 709 # If the user had not specified any fixed squared order other than 710 # WEIGHTED, we will use the guessed weighted order to assign a bound to 711 # the loop diagram order. Later we will check if the order deduced from 712 # the max order appearing in the born diagrams is a better upper bound. 713 # It will set 'WEIGHTED' to the desired value if it was not already set 714 # by the user. This is why you see the process defined with 'WEIGHTED' 715 # in the squared orders no matter the user input. Leave it like this. 716 if [k.upper() for k in self['process']['squared_orders'].keys()] in \ 717 [[],['WEIGHTED']] and self['process']['has_born']: 718 self.guess_loop_orders(user_orders) 719 720 # Finally we enforce the use of the orders specified for the born 721 # (augmented by two if perturbed) by the user, no matter what was 722 # the best guess performed above. 723 for order in user_orders.keys(): 724 if order in self['process']['perturbation_couplings']: 725 self['process']['orders'][order]=user_orders[order]+2 726 else: 727 self['process']['orders'][order]=user_orders[order] 728 if 'WEIGHTED' in user_orders.keys(): 729 self['process']['orders']['WEIGHTED']=user_orders['WEIGHTED']+\ 730 2*min([hierarchy[order] for order in \ 731 self['process']['perturbation_couplings']]) 732 733 ldg_debug_info("Orders used for loop generation",\ 734 self['process']['orders']) 735 736 # Make sure to warn the user if we already possibly excluded mixed order 737 # loops by smartly setting up the orders 738 warning_msg = ("Some loop diagrams contributing to this process might "+\ 739 "be discarded because they are not pure (%s)-perturbation.\nMake sure"+\ 740 " there are none or that you did not want to include them.")%(\ 741 ','.join(self['process']['perturbation_couplings'])) 742 743 if self['process']['has_born']: 744 for order in model['coupling_orders']: 745 if order not in self['process']['perturbation_couplings']: 746 try: 747 if self['process']['orders'][order]< \ 748 self['born_diagrams'].get_max_order(order): 749 logger.warning(warning_msg) 750 break 751 except KeyError: 752 pass 753 754 # Now we can generate the loop diagrams. 755 totloopsuccessful=self.generate_loop_diagrams() 756 757 # If there is no born neither loop diagrams, return now. 758 if not self['process']['has_born'] and not self['loop_diagrams']: 759 self['process']['orders'].clear() 760 self['process']['orders'].update(user_orders) 761 return False 762 763 # We add here the UV renormalization contribution built in 764 # LoopUVCTDiagram. It is done before the squared order selection because 765 # it is possible that some UV-renorm. diagrams are removed as well. 766 if self['process']['has_born']: 767 self.set_Born_CT() 768 769 ldg_debug_info("#UVCTDiags generated",len(self['loop_UVCT_diagrams'])) 770 771 # Reset the orders to their original specification by the user 772 self['process']['orders'].clear() 773 self['process']['orders'].update(user_orders) 774 775 # If there was no born, we will guess the WEIGHT squared order only now, 776 # based on the minimum weighted order of the loop contributions, if it 777 # was not specified by the user. 778 if not self['process']['has_born'] and not \ 779 self['process']['squared_orders'] and not\ 780 self['process']['orders'] and hierarchy: 781 pert_order_weights=[hierarchy[order] for order in \ 782 self['process']['perturbation_couplings']] 783 self['process']['squared_orders']['WEIGHTED']=2*(\ 784 self['loop_diagrams'].get_min_order('WEIGHTED')+\ 785 max(pert_order_weights)-min(pert_order_weights)) 786 787 ldg_debug_info("Squared orders after treatment",\ 788 self['process']['squared_orders']) 789 ldg_debug_info("#Diags after diagram generation",\ 790 len(self['loop_diagrams'])) 791 792 793 # If a special non perturbed order configuration was chosen at the 794 # beginning because of the absence of order settings by the user, 795 # the corresponding filter is applied now to loop diagrams. 796 # List of discarded configurations 797 if chosen_order_config != {}: 798 self.filter_from_order_config('loop_diagrams', \ 799 chosen_order_config,discarded_configurations) 800 # # Warn about discarded configurations. 801 if discarded_configurations!=[]: 802 msg = ("The contribution%s of th%s coupling orders "+\ 803 "configuration%s %s discarded :%s")%(('s','ese','s','are','\n')\ 804 if len(discarded_configurations)>1 else ('','is','','is',' ')) 805 msg = msg + '\n'.join(['(%s)'%self.print_config(conf) for conf \ 806 in discarded_configurations]) 807 msg = msg + "\nManually set the coupling orders to "+\ 808 "generate %sthe contribution%s above."%(('any of ','s') if \ 809 len(discarded_configurations)>1 else ('','')) 810 logger.info(msg) 811 812 # The minimum of the different orders used for the selections can 813 # possibly increase, after some loop diagrams are selected out. 814 # So this check must be iterated until the number of diagrams 815 # remaining is stable. 816 # We first apply the selection rules without the negative constraint. 817 # (i.e. QCD=1 for LO contributions only) 818 regular_constraints = dict([(key,val) for (key,val) in 819 self['process']['squared_orders'].items() if val>=0]) 820 negative_constraints = dict([(key,val) for (key,val) in 821 self['process']['squared_orders'].items() if val<0]) 822 while True: 823 ndiag_remaining=len(self['loop_diagrams']+self['born_diagrams']) 824 self.check_squared_orders(regular_constraints) 825 if len(self['loop_diagrams']+self['born_diagrams'])==ndiag_remaining: 826 break 827 # And then only the negative ones 828 if negative_constraints!={}: 829 # It would be meaningless here to iterate because <order>=-X would 830 # have a different meaning every time. 831 # notice that this function will change the negative values of 832 # self['process']['squared_orders'] to their corresponding positive 833 # constraint for the present process. 834 # For example, u u~ > d d~ QCD^2=-2 becomes u u~ > d d~ QCD=2 835 # because the LO QCD contribution has QED=4, QCD=0 and the NLO one 836 # selected with -2 is QED=2, QCD=2. 837 self.check_squared_orders(negative_constraints,user_squared_orders) 838 839 ldg_debug_info("#Diags after constraints",len(self['loop_diagrams'])) 840 ldg_debug_info("#Born diagrams after constraints",len(self['born_diagrams'])) 841 ldg_debug_info("#UVCTDiags after constraints",len(self['loop_UVCT_diagrams'])) 842 843 # Now the loop diagrams are tagged and filtered for redundancy. 844 tag_selected=[] 845 loop_basis=base_objects.DiagramList() 846 for diag in self['loop_diagrams']: 847 diag.tag(self['structure_repository'],model) 848 # Make sure not to consider wave-function renormalization, vanishing tadpoles, 849 # or redundant diagrams 850 if not diag.is_wf_correction(self['structure_repository'], \ 851 model) and not diag.is_vanishing_tadpole(model) and \ 852 diag['canonical_tag'] not in tag_selected: 853 loop_basis.append(diag) 854 tag_selected.append(diag['canonical_tag']) 855 856 self['loop_diagrams']=loop_basis 857 858 # Now select only the loops corresponding to the perturbative orders 859 # asked for. 860 self.filter_loop_for_perturbative_orders() 861 862 if len(self['loop_diagrams'])==0 and len(self['born_diagrams'])!=0: 863 raise InvalidCmd('All loop diagrams discarded by user selection.\n'+\ 864 'Consider using a tree-level generation or relaxing the coupling'+\ 865 ' order constraints.') 866 # If there is no born neither loop diagrams after filtering, return now. 867 if not self['process']['has_born'] and not self['loop_diagrams']: 868 self['process']['squared_orders'].clear() 869 self['process']['squared_orders'].update(user_squared_orders) 870 return False 871 872 873 # Discard diagrams which are zero because of Furry theorem 874 self.remove_Furry_loops(model,self['structure_repository']) 875 876 # Apply here some user-defined filter. 877 # For expert only, you can edit your own filter by modifying the 878 # user_filter() function which by default does nothing but in which you 879 # will find examples of common filters. 880 self.user_filter(model,self['structure_repository'], filter=loop_filter) 881 882 # Set the necessary UV/R2 CounterTerms for each loop diagram generated 883 self.set_LoopCT_vertices() 884 885 # Now revert the squared order. This function typically adds to the 886 # squared order list the target WEIGHTED order which has been detected. 887 # This is typically not desired because if the user types in directly 888 # what it sees on the screen, it does not get back the same process. 889 # for example, u u~ > d d~ [virt=QCD] becomes 890 # u u~ > d d~ [virt=QCD] WEIGHTED=6 891 # but of course the photon-gluon s-channel Born interference is not 892 # counted in. 893 # However, if you type it in generate again with WEIGHTED=6, you will 894 # get it. 895 self['process']['squared_orders'].clear() 896 self['process']['squared_orders'].update(user_squared_orders) 897 898 # The computation below is just to report what split order are computed 899 # and which one are considered (i.e. kept using the order specifications) 900 self.print_split_order_infos() 901 902 # Give some info about the run 903 nLoopDiag = 0 904 nCT={'UV':0,'R2':0} 905 for ldiag in self['loop_UVCT_diagrams']: 906 nCT[ldiag['type'][:2]]+=len(ldiag['UVCT_couplings']) 907 for ldiag in self['loop_diagrams']: 908 nLoopDiag+=1 909 nCT['UV']+=len(ldiag.get_CT(model,'UV')) 910 nCT['R2']+=len(ldiag.get_CT(model,'R2')) 911 912 # The identification of numerically equivalent diagrams is done here. 913 # Simply comment the line above to remove it for testing purposes 914 # (i.e. to make sure it does not alter the result). 915 nLoopsIdentified = self.identify_loop_diagrams() 916 if nLoopsIdentified > 0: 917 logger.debug("A total of %d loop diagrams "%nLoopsIdentified+\ 918 "were identified with equivalent ones.") 919 logger.info("Contributing diagrams generated: "+\ 920 "%d Born, %d%s loops, %d R2, %d UV"%(len(self['born_diagrams']), 921 len(self['loop_diagrams']),'(+%d)'%nLoopsIdentified \ 922 if nLoopsIdentified>0 else '' ,nCT['R2'],nCT['UV'])) 923 924 ldg_debug_info("#Diags after filtering",len(self['loop_diagrams'])) 925 ldg_debug_info("# of different structures identified",\ 926 len(self['structure_repository'])) 927 928 return (bornsuccessful or totloopsuccessful)
929
930 - def identify_loop_diagrams(self):
931 """ Uses a loop_tag characterizing the loop with only physical 932 information about it (mass, coupling, width, color, etc...) so as to 933 recognize numerically equivalent diagrams and group them together, 934 such as massless quark loops in pure QCD gluon loop amplitudes.""" 935 936 # This dictionary contains key-value pairs of the form 937 # (loop_tag, DiagramList) where the loop_tag key unambiguously 938 # characterizes a class of equivalent diagrams and the DiagramList value 939 # lists all the diagrams belonging to this class. 940 # In the end, the first diagram of this DiagramList will be used as 941 # the reference included in the numerical code for the loop matrix 942 # element computations and all the others will be omitted, being 943 # included via a simple multiplicative factor applied to the first one. 944 diagram_identification = {} 945 946 for i, loop_diag in enumerate(self['loop_diagrams']): 947 loop_tag = loop_diag.build_loop_tag_for_diagram_identification( 948 self['process']['model'], self.get('structure_repository'), 949 use_FDStructure_ID_for_tag = True) 950 # We store the loop diagrams in a 2-tuple that keeps track of 'i' 951 # so that we don't lose their original order. It is just for 952 # convenience, and not strictly necessary. 953 try: 954 diagram_identification[loop_tag].append((i+1,loop_diag)) 955 except KeyError: 956 diagram_identification[loop_tag] = [(i+1,loop_diag)] 957 958 # Now sort the loop_tag keys according to their order of appearance 959 sorted_loop_tag_keys = sorted(diagram_identification.keys(), 960 key=lambda k:diagram_identification[k][0][0]) 961 962 new_loop_diagram_base = base_objects.DiagramList([]) 963 n_loops_identified = 0 964 for loop_tag in sorted_loop_tag_keys: 965 n_diag_in_class = len(diagram_identification[loop_tag]) 966 n_loops_identified += n_diag_in_class-1 967 new_loop_diagram_base.append(diagram_identification[loop_tag][0][1]) 968 # We must add the counterterms of all the identified loop diagrams 969 # to the reference one. 970 new_loop_diagram_base[-1]['multiplier'] = n_diag_in_class 971 for ldiag in diagram_identification[loop_tag][1:]: 972 new_loop_diagram_base[-1].get('CT_vertices').extend( 973 copy.copy(ldiag[1].get('CT_vertices'))) 974 if n_diag_in_class > 1: 975 ldg_debug_info("# Diagram equivalence class detected","#(%s) -> #%d"\ 976 %(','.join('%d'%diag[0] for diag in diagram_identification[loop_tag][1:])+ 977 (',' if n_diag_in_class==2 else ''),diagram_identification[loop_tag][0][0])) 978 979 980 self.set('loop_diagrams',new_loop_diagram_base) 981 return n_loops_identified
982
983 - def print_split_order_infos(self):
984 """This function is solely for monitoring purposes. It reports what are 985 the coupling order combination which are obtained with the diagram 986 genarated and among those which ones correspond to those selected by 987 the process definition and which ones are the extra combinations which 988 comes as a byproduct of the computation of the desired one. The typical 989 example is that if you ask for d d~ > u u~ QCD^2==2 [virt=QCD, QED], 990 you will not only get (QCD,QED)=(2,2);(2,4) which are the desired ones 991 but the code output will in principle also be able to return 992 (QCD,QED)=(4,0);(4,2);(0,4);(0,6) because they involve the same amplitudes 993 """ 994 995 hierarchy = self['process']['model']['order_hierarchy'] 996 997 sqorders_types=copy.copy(self['process'].get('sqorders_types')) 998 # The WEIGHTED order might have been automatically assigned to the 999 # squared order constraints, so we must assign it a type if not specified 1000 if 'WEIGHTED' not in sqorders_types: 1001 sqorders_types['WEIGHTED']='<=' 1002 1003 sorted_hierarchy = [order[0] for order in \ 1004 sorted(hierarchy.items(), key=lambda el: el[1])] 1005 1006 loop_SOs = set(tuple([d.get_order(order) for order in sorted_hierarchy]) 1007 for d in self['loop_diagrams']+self['loop_UVCT_diagrams']) 1008 1009 if self['process']['has_born']: 1010 born_SOs = set(tuple([d.get_order(order) for order in \ 1011 sorted_hierarchy]) for d in self['born_diagrams']) 1012 else: 1013 born_SOs = set([]) 1014 1015 born_sqSOs = set(tuple([x + y for x, y in zip(b1_SO, b2_SO)]) for b1_SO 1016 in born_SOs for b2_SO in born_SOs) 1017 if self['process']['has_born']: 1018 ref_amps = born_SOs 1019 else: 1020 ref_amps = loop_SOs 1021 loop_sqSOs = set(tuple([x + y for x, y in zip(b_SO, l_SO)]) for b_SO in 1022 ref_amps for l_SO in loop_SOs) 1023 1024 # Append the corresponding WEIGHT of each contribution 1025 sorted_hierarchy.append('WEIGHTED') 1026 born_sqSOs = sorted([b_sqso+(sum([b*hierarchy[sorted_hierarchy[i]] for 1027 i, b in enumerate(b_sqso)]),) for b_sqso in born_sqSOs], 1028 key=lambda el: el[1]) 1029 loop_sqSOs = sorted([l_sqso+(sum([l*hierarchy[sorted_hierarchy[i]] for 1030 i, l in enumerate(l_sqso)]),) for l_sqso in loop_sqSOs], 1031 key=lambda el: el[1]) 1032 1033 1034 logger.debug("Coupling order combinations considered:"+\ 1035 " (%s)"%','.join(sorted_hierarchy)) 1036 1037 # Now check what is left 1038 born_considered = [] 1039 loop_considered = [] 1040 for i, sqSOList in enumerate([born_sqSOs,loop_sqSOs]): 1041 considered = [] 1042 extra = [] 1043 for sqSO in sqSOList: 1044 for sqo, constraint in self['process']['squared_orders'].items(): 1045 sqo_index = sorted_hierarchy.index(sqo) 1046 # Notice that I assume here that the negative coupling order 1047 # constraint should have been replaced here (by its 1048 # corresponding positive value). 1049 if (sqorders_types[sqo]=='==' and 1050 sqSO[sqo_index]!=constraint ) or \ 1051 (sqorders_types[sqo] in ['=','<='] and 1052 sqSO[sqo_index]>constraint) or \ 1053 (sqorders_types[sqo] in ['>'] and 1054 sqSO[sqo_index]<=constraint): 1055 extra.append(sqSO) 1056 break; 1057 1058 # Set the ones considered to be the complement of the omitted ones 1059 considered = [sqSO for sqSO in sqSOList if sqSO not in extra] 1060 1061 if i==0: 1062 born_considered = considered 1063 name = "Born" 1064 if not self['process']['has_born']: 1065 logger.debug(" > No Born contributions for this process.") 1066 continue 1067 elif i==1: 1068 loop_considered = considered 1069 name = "loop" 1070 1071 if len(considered)==0: 1072 logger.debug(" > %s : None"%name) 1073 else: 1074 logger.debug(" > %s : %s"%(name,' '.join(['(%s,W%d)'%( 1075 ','.join(list('%d'%s for s in c[:-1])),c[-1]) 1076 for c in considered]))) 1077 1078 if len(extra)!=0: 1079 logger.debug(" > %s (not selected but available): %s"%(name,' '. 1080 join(['(%s,W%d)'%(','.join(list('%d'%s for s in e[:-1])), 1081 e[-1]) for e in extra]))) 1082 1083 # In case it is needed, the considered orders are returned 1084 # (it is used by some of the unit tests) 1085 return (born_considered, 1086 [sqSO for sqSO in born_sqSOs if sqSO not in born_considered], 1087 loop_considered, 1088 [sqSO for sqSO in loop_sqSOs if sqSO not in loop_considered])
1089 1090
1091 - def generate_born_diagrams(self):
1092 """ Generates all born diagrams relevant to this NLO Process """ 1093 1094 bornsuccessful, self['born_diagrams'] = \ 1095 diagram_generation.Amplitude.generate_diagrams(self,True) 1096 1097 return bornsuccessful
1098
1099 - def generate_loop_diagrams(self):
1100 """ Generates all loop diagrams relevant to this NLO Process """ 1101 1102 # Reinitialize the loop diagram container 1103 self['loop_diagrams']=base_objects.DiagramList() 1104 totloopsuccessful=False 1105 1106 # Make sure to start with an empty l-cut particle list. 1107 self.lcutpartemployed=[] 1108 1109 for order in self['process']['perturbation_couplings']: 1110 ldg_debug_info("Perturbation coupling generated now ",order) 1111 lcutPart=[particle for particle in \ 1112 self['process']['model']['particles'] if \ 1113 (particle.is_perturbating(order, self['process']['model']) and \ 1114 particle.get_pdg_code() not in \ 1115 self['process']['forbidden_particles'])] 1116 # lcutPart = [lp for lp in lcutPart if abs(lp.get('pdg_code'))==6] 1117 # misc.sprint("lcutPart=",[part.get('name') for part in lcutPart]) 1118 for part in lcutPart: 1119 if part.get_pdg_code() not in self.lcutpartemployed: 1120 # First create the two L-cut particles to add to the process. 1121 # Remember that in the model only the particles should be 1122 # tagged as contributing to the a perturbation. Never the 1123 # anti-particle. We chose here a specific orientation for 1124 # the loop momentum flow, say going IN lcutone and OUT 1125 # lcuttwo. We also define here the 'positive' loop fermion 1126 # flow by always setting lcutone to be a particle and 1127 # lcuttwo the corresponding anti-particle. 1128 ldg_debug_info("Generating loop diagram with L-cut type",\ 1129 part.get_name()) 1130 lcutone=base_objects.Leg({'id': part.get_pdg_code(), 1131 'state': True, 1132 'loop_line': True}) 1133 lcuttwo=base_objects.Leg({'id': part.get_anti_pdg_code(), 1134 'state': True, 1135 'loop_line': True}) 1136 self['process'].get('legs').extend([lcutone,lcuttwo]) 1137 # WARNING, it is important for the tagging to notice here 1138 # that lcuttwo is the last leg in the process list of legs 1139 # and will therefore carry the highest 'number' attribute as 1140 # required to insure that it will never be 'propagated' to 1141 # any output leg. 1142 1143 # We generate the diagrams now 1144 loopsuccessful, lcutdiaglist = \ 1145 super(LoopAmplitude, self).generate_diagrams(True) 1146 1147 # Now get rid of all the previously defined l-cut particles. 1148 leg_to_remove=[leg for leg in self['process']['legs'] \ 1149 if leg['loop_line']] 1150 for leg in leg_to_remove: 1151 self['process']['legs'].remove(leg) 1152 1153 # The correct L-cut type is specified 1154 for diag in lcutdiaglist: 1155 diag.set('type',part.get_pdg_code()) 1156 self['loop_diagrams']+=lcutdiaglist 1157 1158 # Update the list of already employed L-cut particles such 1159 # that we never use them again in loop particles 1160 self.lcutpartemployed.append(part.get_pdg_code()) 1161 self.lcutpartemployed.append(part.get_anti_pdg_code()) 1162 1163 ldg_debug_info("#Diags generated w/ this L-cut particle",\ 1164 len(lcutdiaglist)) 1165 # Accordingly update the totloopsuccessful tag 1166 if loopsuccessful: 1167 totloopsuccessful=True 1168 1169 # Reset the l-cut particle list 1170 self.lcutpartemployed=[] 1171 1172 return totloopsuccessful
1173 1174
1175 - def set_Born_CT(self):
1176 """ Scan all born diagrams and add for each all the corresponding UV 1177 counterterms. It creates one LoopUVCTDiagram per born diagram and set 1178 of possible coupling_order (so that QCD and QED wavefunction corrections 1179 are not in the same LoopUVCTDiagram for example). Notice that this takes 1180 care only of the UV counterterm which factorize with the born and the 1181 other contributions like the UV mass renormalization are added in the 1182 function setLoopCTVertices""" 1183 1184 # return True 1185 # ============================================ 1186 # Including the UVtree contributions 1187 # ============================================ 1188 1189 # The following lists the UV interactions potentially giving UV counterterms 1190 # (The UVmass interactions is accounted for like the R2s) 1191 UVCTvertex_interactions = base_objects.InteractionList() 1192 for inter in self['process']['model']['interactions'].get_UV(): 1193 if inter.is_UVtree() and len(inter['particles'])>1 and \ 1194 inter.is_perturbating(self['process']['perturbation_couplings']) \ 1195 and (set(inter['orders'].keys()).intersection(\ 1196 set(self['process']['perturbation_couplings'])))!=set([]) and \ 1197 (any([set(loop_parts).intersection(set(self['process']\ 1198 ['forbidden_particles']))==set([]) for loop_parts in \ 1199 inter.get('loop_particles')]) or \ 1200 inter.get('loop_particles')==[[]]): 1201 UVCTvertex_interactions.append(inter) 1202 1203 # Temporarly give the tagging order 'UVCT_SPECIAL' to those interactions 1204 self['process']['model'].get('order_hierarchy')['UVCT_SPECIAL']=0 1205 self['process']['model'].get('coupling_orders').add('UVCT_SPECIAL') 1206 for inter in UVCTvertex_interactions: 1207 neworders=copy.copy(inter.get('orders')) 1208 neworders['UVCT_SPECIAL']=1 1209 inter.set('orders',neworders) 1210 # Refresh the model interaction dictionary while including those special 1211 # interactions 1212 self['process']['model'].actualize_dictionaries(useUVCT=True) 1213 1214 # Generate the UVCTdiagrams (born diagrams with 'UVCT_SPECIAL'=0 order 1215 # will be generated along) 1216 self['process']['orders']['UVCT_SPECIAL']=1 1217 1218 UVCTsuccessful, UVCTdiagrams = \ 1219 super(LoopAmplitude, self).generate_diagrams(True) 1220 1221 for UVCTdiag in UVCTdiagrams: 1222 if UVCTdiag.get_order('UVCT_SPECIAL')==1: 1223 newUVCTDiag = loop_base_objects.LoopUVCTDiagram({\ 1224 'vertices':copy.deepcopy(UVCTdiag['vertices'])}) 1225 UVCTinter = newUVCTDiag.get_UVCTinteraction(self['process']['model']) 1226 newUVCTDiag.set('type',UVCTinter.get('type')) 1227 # This interaction counter-term must be accounted for as many times 1228 # as they are list of loop_particles defined and allowed for by 1229 # the process. 1230 newUVCTDiag.get('UVCT_couplings').append((len([1 for loop_parts \ 1231 in UVCTinter.get('loop_particles') if set(loop_parts).intersection(\ 1232 set(self['process']['forbidden_particles']))==set([])])) if 1233 loop_parts!=[[]] else 1) 1234 self['loop_UVCT_diagrams'].append(newUVCTDiag) 1235 1236 # Remove the additional order requirement in the born orders for this 1237 # process 1238 del self['process']['orders']['UVCT_SPECIAL'] 1239 # Remove the fake order added to the selected UVCT interactions 1240 del self['process']['model'].get('order_hierarchy')['UVCT_SPECIAL'] 1241 self['process']['model'].get('coupling_orders').remove('UVCT_SPECIAL') 1242 for inter in UVCTvertex_interactions: 1243 del inter.get('orders')['UVCT_SPECIAL'] 1244 # Revert the model interaction dictionaries to default 1245 self['process']['model'].actualize_dictionaries(useUVCT=False) 1246 1247 # Set the correct orders to the loop_UVCT_diagrams 1248 for UVCTdiag in self['loop_UVCT_diagrams']: 1249 UVCTdiag.calculate_orders(self['process']['model']) 1250 1251 # ============================================ 1252 # Wavefunction renormalization 1253 # ============================================ 1254 1255 if not self['process']['has_born']: 1256 return UVCTsuccessful 1257 1258 # We now scan each born diagram, adding the necessary wavefunction 1259 # renormalizations 1260 for bornDiag in self['born_diagrams']: 1261 # This dictionary takes for keys the tuple 1262 # (('OrderName1',power1),...,('OrderNameN',powerN) representing 1263 # the power brought by the counterterm and the value is the 1264 # corresponding LoopUVCTDiagram. 1265 # The last entry is of the form ('EpsilonOrder', value) to put the 1266 # contribution of each different EpsilonOrder to different 1267 # LoopUVCTDiagrams. 1268 LoopUVCTDiagramsAdded={} 1269 for leg in self['process']['legs']: 1270 counterterm=self['process']['model'].get_particle(abs(leg['id'])).\ 1271 get('counterterm') 1272 for key, value in counterterm.items(): 1273 if key[0] in self['process']['perturbation_couplings']: 1274 for laurentOrder, CTCoupling in value.items(): 1275 # Create the order key of the UV counterterm 1276 orderKey=[(key[0],2),] 1277 orderKey.sort() 1278 orderKey.append(('EpsilonOrder',-laurentOrder)) 1279 CTCouplings=[CTCoupling for loop_parts in key[1] if 1280 set(loop_parts).intersection(set(self['process']\ 1281 ['forbidden_particles']))==set([])] 1282 if CTCouplings!=[]: 1283 try: 1284 LoopUVCTDiagramsAdded[tuple(orderKey)].get(\ 1285 'UVCT_couplings').extend(CTCouplings) 1286 except KeyError: 1287 LoopUVCTDiagramsAdded[tuple(orderKey)]=\ 1288 loop_base_objects.LoopUVCTDiagram({\ 1289 'vertices':copy.deepcopy(bornDiag['vertices']), 1290 'type':'UV'+('' if laurentOrder==0 else 1291 str(-laurentOrder)+'eps'), 1292 'UVCT_orders':{key[0]:2}, 1293 'UVCT_couplings':CTCouplings}) 1294 1295 for LoopUVCTDiagram in LoopUVCTDiagramsAdded.values(): 1296 LoopUVCTDiagram.calculate_orders(self['process']['model']) 1297 self['loop_UVCT_diagrams'].append(LoopUVCTDiagram) 1298 1299 return UVCTsuccessful
1300
1301 - def set_LoopCT_vertices(self):
1302 """ Scan each loop diagram and recognizes what are the R2/UVmass 1303 CounterTerms associated to them """ 1304 #return # debug 1305 # We first create a base dictionary with as a key (tupleA,tupleB). For 1306 # each R2/UV interaction, tuple B is the ordered tuple of the loop 1307 # particles (not anti-particles, so that the PDG is always positive!) 1308 # listed in its loop_particles attribute. Tuple A is the ordered tuple 1309 # of external particles PDGs. making up this interaction. The values of 1310 # the dictionary are a list of the interaction ID having the same key 1311 # above. 1312 CT_interactions = {} 1313 for inter in self['process']['model']['interactions']: 1314 if inter.is_UVmass() or inter.is_UVloop() or inter.is_R2() and \ 1315 len(inter['particles'])>1 and inter.is_perturbating(\ 1316 self['process']['perturbation_couplings']): 1317 # This interaction might have several possible loop particles 1318 # yielding the same CT. So we add this interaction ID 1319 # for each entry in the list loop_particles. 1320 for i, lparts in enumerate(inter['loop_particles']): 1321 keya=copy.copy(lparts) 1322 keya.sort() 1323 if inter.is_UVloop(): 1324 # If it is a CT of type UVloop, then do not specify the 1325 # keya (leave it empty) but make sure the particles 1326 # specified as loop particles are not forbidden before 1327 # adding this CT to CT_interactions 1328 if (set(self['process']['forbidden_particles']) & \ 1329 set(lparts)) != set([]): 1330 continue 1331 else: 1332 keya=[] 1333 keyb=[part.get_pdg_code() for part in inter['particles']] 1334 keyb.sort() 1335 key=(tuple(keyb),tuple(keya)) 1336 # We keep track of 'i' (i.e. the position of the 1337 # loop_particle list in the inter['loop_particles']) so 1338 # that each coupling in a vertex of type 'UVloop' is 1339 # correctly accounted for since the keya is always replaced 1340 # by an empty list since the constraint on the loop particles 1341 # is simply that there is not corresponding forbidden 1342 # particles in the process definition and not that the 1343 # actual particle content of the loop generate matches. 1344 # 1345 # This can also happen with the type 'UVmass' or 'R2' 1346 # CTvertex ex1( 1347 # type='UVmass' 1348 # [...] 1349 # loop_particles=[[[d,g],[d,g]]]) 1350 # Which is a bit silly but can happen and would mean that 1351 # we must account twice for the coupling associated to each 1352 # of these loop_particles. 1353 # One might imagine someone doing it with 1354 # loop_particles=[[[],[]]], for example, because he wanted 1355 # to get rid of the loop particle constraint for some reason. 1356 try: 1357 CT_interactions[key].append((inter['id'],i)) 1358 except KeyError: 1359 CT_interactions[key]=[(inter['id'],i),] 1360 1361 # The dictionary CTmass_added keeps track of what are the CounterTerms of 1362 # type UVmass or R2 already added and prevents us from adding them again. 1363 # For instance, the fermion boxes with four external gluons exists in 6 copies 1364 # (with different crossings of the external legs each time) and the 1365 # corresponding R2 must be added only once. The key of this dictionary 1366 # characterizing the loop is (tupleA,tupleB). Tuple A is made from the 1367 # list of the ID of the external structures attached to this loop and 1368 # tuple B from list of the pdg of the particles building this loop. 1369 1370 # Notice that when a CT of type UVmass is specified with an empty 1371 # loop_particles attribute, then it means it must be added once for each 1372 # particle with a matching topology, irrespectively of the loop content. 1373 # Whenever added, such a CT is put in the dictionary CT_added with a key 1374 # having an empty tupleB. 1375 # Finally, because CT interactions of type UVloop do specify a 1376 # loop_particles attribute, but which serves only to be filtered against 1377 # particles forbidden in the process definition, they will also be added 1378 # with an empty tupleB. 1379 CT_added = {} 1380 1381 for diag in self['loop_diagrams']: 1382 # First build the key from this loop for the CT_interaction dictionary 1383 # (Searching Key) and the key for the CT_added dictionary (tracking Key) 1384 searchingKeyA=[] 1385 # Notice that searchingKeyB below also serves as trackingKeyB 1386 searchingKeyB=[] 1387 trackingKeyA=[] 1388 for tagElement in diag['canonical_tag']: 1389 for structID in tagElement[1]: 1390 trackingKeyA.append(structID) 1391 searchingKeyA.append(self['process']['model'].get_particle(\ 1392 self['structure_repository'][structID]['binding_leg']['id']).\ 1393 get_pdg_code()) 1394 searchingKeyB.append(self['process']['model'].get_particle(\ 1395 tagElement[0]).get('pdg_code')) 1396 searchingKeyA.sort() 1397 # We do not repeat particles present many times in the loop 1398 searchingKeyB=list(set(searchingKeyB)) 1399 searchingKeyB.sort() 1400 trackingKeyA.sort() 1401 # I repeat, they are two kinds of keys: 1402 # searchingKey: 1403 # This serves to scan the CT interactions defined and then find 1404 # which ones match a given loop topology and particle. 1405 # trackingKey: 1406 # Once some CT vertices are identified to be a match for a loop, 1407 # the trackingKey is used in conjunction with the dictionary 1408 # CT_added to make sure that this CT has not already been included. 1409 1410 # Each of these two keys above, has the format 1411 # (tupleA, tupleB) 1412 # with tupleB being the loop_content and either contains the set of 1413 # loop particles PDGs of the interaction (for the searchingKey) 1414 # or of the loops already scanned (trackingKey). It can also be 1415 # empty when considering interactions of type UVmass or R2 which 1416 # have an empty loop_particle attribute or those of type UVloop. 1417 # TupleA is the set of external particle PDG (for the searchingKey) 1418 # and the unordered list of structID attached to the loop (for the 1419 # trackingKey) 1420 searchingKeySimple=(tuple(searchingKeyA),()) 1421 searchingKeyLoopPart=(tuple(searchingKeyA),tuple(searchingKeyB)) 1422 trackingKeySimple=(tuple(trackingKeyA),()) 1423 trackingKeyLoopPart=(tuple(trackingKeyA),tuple(searchingKeyB)) 1424 # Now we look for a CT which might correspond to this loop by looking 1425 # for its searchingKey in CT_interactions 1426 1427 # misc.sprint("I have the following CT_interactions=",CT_interactions) 1428 try: 1429 CTIDs=copy.copy(CT_interactions[searchingKeySimple]) 1430 except KeyError: 1431 CTIDs=[] 1432 try: 1433 CTIDs.extend(copy.copy(CT_interactions[searchingKeyLoopPart])) 1434 except KeyError: 1435 pass 1436 if not CTIDs: 1437 continue 1438 # We have found some CT interactions corresponding to this loop 1439 # so we must make sure we have not included them already 1440 try: 1441 usedIDs=copy.copy(CT_added[trackingKeySimple]) 1442 except KeyError: 1443 usedIDs=[] 1444 try: 1445 usedIDs.extend(copy.copy(CT_added[trackingKeyLoopPart])) 1446 except KeyError: 1447 pass 1448 1449 for CTID in CTIDs: 1450 # Make sure it has not been considered yet and that the loop 1451 # orders match 1452 if CTID not in usedIDs and diag.get_loop_orders(\ 1453 self['process']['model'])==\ 1454 self['process']['model']['interaction_dict'][CTID[0]]['orders']: 1455 # Create the amplitude vertex corresponding to this CT 1456 # and add it to the LoopDiagram treated. 1457 CTleglist = base_objects.LegList() 1458 for tagElement in diag['canonical_tag']: 1459 for structID in tagElement[1]: 1460 CTleglist.append(\ 1461 self['structure_repository'][structID]['binding_leg']) 1462 CTVertex = base_objects.Vertex({'id':CTID[0], \ 1463 'legs':CTleglist}) 1464 diag['CT_vertices'].append(CTVertex) 1465 # Now add this CT vertex to the CT_added dictionary so that 1466 # we are sure it will not be double counted 1467 if self['process']['model']['interaction_dict'][CTID[0]]\ 1468 ['loop_particles'][CTID[1]]==[] or \ 1469 self['process']['model']['interaction_dict'][CTID[0]].\ 1470 is_UVloop(): 1471 try: 1472 CT_added[trackingKeySimple].append(CTID) 1473 except KeyError: 1474 CT_added[trackingKeySimple] = [CTID, ] 1475 else: 1476 try: 1477 CT_added[trackingKeyLoopPart].append(CTID) 1478 except KeyError: 1479 CT_added[trackingKeyLoopPart] = [CTID, ]
1480
1481 - def create_diagram(self, vertexlist):
1482 """ Return a LoopDiagram created.""" 1483 return loop_base_objects.LoopDiagram({'vertices':vertexlist})
1484
1485 - def copy_leglist(self, leglist):
1486 """ Returns a DGLoopLeg list instead of the default copy_leglist 1487 defined in base_objects.Amplitude """ 1488 1489 dgloopleglist=base_objects.LegList() 1490 for leg in leglist: 1491 dgloopleglist.append(loop_base_objects.DGLoopLeg(leg)) 1492 1493 return dgloopleglist
1494
1495 - def convert_dgleg_to_leg(self, vertexdoublelist):
1496 """ Overloaded here to convert back all DGLoopLegs into Legs. """ 1497 for vertexlist in vertexdoublelist: 1498 for vertex in vertexlist: 1499 if not isinstance(vertex['legs'][0],loop_base_objects.DGLoopLeg): 1500 continue 1501 vertex['legs'][:]=[leg.convert_to_leg() for leg in \ 1502 vertex['legs']] 1503 return True
1504
1505 - def get_combined_legs(self, legs, leg_vert_ids, number, state):
1506 """Create a set of new legs from the info given.""" 1507 1508 looplegs=[leg for leg in legs if leg['loop_line']] 1509 1510 # Get rid of all vanishing tadpoles 1511 #Ease the access to the model 1512 model=self['process']['model'] 1513 exlegs=[leg for leg in looplegs if leg['depth']==0] 1514 if(len(exlegs)==2): 1515 if(any([part['mass'].lower()=='zero' for pdg,part in model.get('particle_dict').items() if pdg==abs(exlegs[0]['id'])])): 1516 return [] 1517 1518 # Correctly propagate the loopflow 1519 loopline=(len(looplegs)==1) 1520 mylegs = [] 1521 for i, (leg_id, vert_id) in enumerate(leg_vert_ids): 1522 # We can now create the set of possible merged legs. 1523 # However, we make sure that its PDG is not in the list of 1524 # L-cut particles we already explored. If it is, we simply reject 1525 # the diagram. 1526 if not loopline or not (leg_id in self.lcutpartemployed): 1527 # Reminder: The only purpose of the "depth" flag is to get rid 1528 # of (some, not all) of the wave-function renormalization 1529 # already during diagram generation. We reckognize a wf 1530 # renormalization diagram as follows: 1531 if len(legs)==2 and len(looplegs)==2: 1532 # We have candidate 1533 depths=(looplegs[0]['depth'],looplegs[1]['depth']) 1534 if (0 in depths) and (-1 not in depths) and depths!=(0,0): 1535 # Check that the PDG of the outter particle in the 1536 # wavefunction renormalization bubble is equal to the 1537 # one of the inner particle. 1538 continue 1539 1540 # If depth is not 0 because of being an external leg and not 1541 # the propagated PDG, then we set it to -1 so that from that 1542 # point we are sure the diagram will not be reckognized as a 1543 # wave-function renormalization. 1544 depth=-1 1545 # When creating a loop leg from exactly two external legs, we 1546 # set the depth to the PDG of the external non-loop line. 1547 if len(legs)==2 and loopline and (legs[0]['depth'],\ 1548 legs[1]['depth'])==(0,0): 1549 if not legs[0]['loop_line']: 1550 depth=legs[0]['id'] 1551 else: 1552 depth=legs[1]['id'] 1553 # In case of two point interactions among two same particle 1554 # we propagate the existing depth 1555 if len(legs)==1 and legs[0]['id']==leg_id: 1556 depth=legs[0]['depth'] 1557 # In all other cases we set the depth to -1 since no 1558 # wave-function renormalization diagram can arise from this 1559 # side of the diagram construction. 1560 1561 mylegs.append((loop_base_objects.DGLoopLeg({'id':leg_id, 1562 'number':number, 1563 'state':state, 1564 'from_group':True, 1565 'depth': depth, 1566 'loop_line': loopline}), 1567 vert_id)) 1568 return mylegs
1569
1570 - def get_combined_vertices(self, legs, vert_ids):
1571 """Allow for selection of vertex ids.""" 1572 1573 looplegs=[leg for leg in legs if leg['loop_line']] 1574 nonlooplegs=[leg for leg in legs if not leg['loop_line']] 1575 1576 # Get rid of all vanishing tadpoles 1577 model=self['process']['model'] 1578 exlegs=[leg for leg in looplegs if leg['depth']==0] 1579 if(len(exlegs)==2): 1580 if(any([part['mass'].lower()=='zero' for pdg,part in \ 1581 model.get('particle_dict').items() if pdg==abs(exlegs[0]['id'])])): 1582 return [] 1583 1584 1585 # Get rid of some wave-function renormalization diagrams already during 1586 # diagram generation already.In a similar manner as in get_combined_legs. 1587 if(len(legs)==3 and len(looplegs)==2): 1588 depths=(looplegs[0]['depth'],looplegs[1]['depth']) 1589 if (0 in depths) and (-1 not in depths) and depths!=(0,0): 1590 return [] 1591 1592 return vert_ids
1593 1594 # Helper function 1595
1596 - def check_squared_orders(self, sq_order_constrains, user_squared_orders=None):
1597 """ Filters the diagrams according to the constraints on the squared 1598 orders in argument and wether the process has a born or not. """ 1599 1600 diagRef=base_objects.DiagramList() 1601 AllLoopDiagrams=base_objects.DiagramList(self['loop_diagrams']+\ 1602 self['loop_UVCT_diagrams']) 1603 1604 AllBornDiagrams=base_objects.DiagramList(self['born_diagrams']) 1605 if self['process']['has_born']: 1606 diagRef=AllBornDiagrams 1607 else: 1608 diagRef=AllLoopDiagrams 1609 1610 sqorders_types=copy.copy(self['process'].get('sqorders_types')) 1611 1612 # The WEIGHTED order might have been automatically assigned to the 1613 # squared order constraints, so we must assign it a type if not specified 1614 if 'WEIGHTED' not in sqorders_types: 1615 sqorders_types['WEIGHTED']='<=' 1616 1617 if len(diagRef)==0: 1618 # If no born contributes but they were supposed to ( in the 1619 # case of self['process']['has_born']=True) then it means that 1620 # the loop cannot be squared against anything and none should 1621 # contribute either. The squared order constraints are just too 1622 # tight for anything to contribute. 1623 AllLoopDiagrams = base_objects.DiagramList() 1624 1625 1626 # Start by filtering the loop diagrams 1627 AllLoopDiagrams = AllLoopDiagrams.apply_positive_sq_orders(diagRef, 1628 sq_order_constrains, sqorders_types) 1629 # And now the Born ones if there are any 1630 if self['process']['has_born']: 1631 # We consider both the Born*Born and Born*Loop squared terms here 1632 AllBornDiagrams = AllBornDiagrams.apply_positive_sq_orders( 1633 AllLoopDiagrams+AllBornDiagrams, sq_order_constrains, sqorders_types) 1634 1635 # Now treat the negative squared order constraint (at most one) 1636 neg_orders = [(order, value) for order, value in \ 1637 sq_order_constrains.items() if value<0] 1638 if len(neg_orders)==1: 1639 neg_order, neg_value = neg_orders[0] 1640 # If there is a Born contribution, then the target order will 1641 # be computed over all Born*Born and Born*loop contributions 1642 if self['process']['has_born']: 1643 AllBornDiagrams, target_order =\ 1644 AllBornDiagrams.apply_negative_sq_order( 1645 base_objects.DiagramList(AllLoopDiagrams+AllBornDiagrams), 1646 neg_order,neg_value,sqorders_types[neg_order]) 1647 # Now we must filter the loop diagrams using to the target_order 1648 # computed above from the LO and NLO contributions 1649 AllLoopDiagrams = AllLoopDiagrams.apply_positive_sq_orders( 1650 diagRef,{neg_order:target_order}, 1651 {neg_order:sqorders_types[neg_order]}) 1652 1653 # If there is no Born, then the situation is completely analoguous 1654 # to the tree level case since it is simply Loop*Loop 1655 else: 1656 AllLoopDiagrams, target_order = \ 1657 AllLoopDiagrams.apply_negative_sq_order( 1658 diagRef,neg_order,neg_value,sqorders_types[neg_order]) 1659 1660 # Substitute the negative value to this positive one 1661 # (also in the backed up values in user_squared_orders so that 1662 # this change is permanent and we will still have access to 1663 # it at the output stage) 1664 self['process']['squared_orders'][neg_order]=target_order 1665 user_squared_orders[neg_order]=target_order 1666 1667 elif len(neg_orders)>1: 1668 raise MadGraph5Error('At most one negative squared order constraint'+\ 1669 ' can be specified, not %s.'%str(neg_orders)) 1670 1671 if self['process']['has_born']: 1672 self['born_diagrams'] = AllBornDiagrams 1673 self['loop_diagrams']=[diag for diag in AllLoopDiagrams if not \ 1674 isinstance(diag,loop_base_objects.LoopUVCTDiagram)] 1675 self['loop_UVCT_diagrams']=[diag for diag in AllLoopDiagrams if \ 1676 isinstance(diag,loop_base_objects.LoopUVCTDiagram)]
1677
1678 - def order_diagram_set(self, diag_set, split_orders):
1679 """ This is a helper function for order_diagrams_according_to_split_orders 1680 and intended to be used from LoopHelasAmplitude only""" 1681 1682 # The dictionary below has keys being the tuple (split_order<i>_values) 1683 # and values being diagram lists sharing the same split orders. 1684 diag_by_so = {} 1685 1686 for diag in diag_set: 1687 so_key = tuple([diag.get_order(order) for order in split_orders]) 1688 try: 1689 diag_by_so[so_key].append(diag) 1690 except KeyError: 1691 diag_by_so[so_key]=base_objects.DiagramList([diag,]) 1692 1693 so_keys = diag_by_so.keys() 1694 # Complete the order hierarchy by possibly missing defined order for 1695 # which we set the weight to zero by default (so that they are ignored). 1696 order_hierarchy = self.get('process').get('model').get('order_hierarchy') 1697 order_weights = copy.copy(order_hierarchy) 1698 for so in split_orders: 1699 if so not in order_hierarchy.keys(): 1700 order_weights[so]=0 1701 1702 # Now order the keys of diag_by_so by the WEIGHT of the split_orders 1703 # (and only those, the orders not included in the split_orders do not 1704 # count for this ordering as they could be mixed in any given group). 1705 so_keys = sorted(so_keys, key = lambda elem: (sum([power*order_weights[\ 1706 split_orders[i]] for i,power in enumerate(elem)]))) 1707 1708 # Now put the diagram back, ordered this time, in diag_set 1709 diag_set[:] = [] 1710 for so_key in so_keys: 1711 diag_set.extend(diag_by_so[so_key])
1712 1713
1714 - def order_diagrams_according_to_split_orders(self, split_orders):
1715 """ Reorder the loop and Born diagrams (if any) in group of diagrams 1716 sharing the same coupling orders are put together and these groups are 1717 order in decreasing WEIGHTED orders. 1718 Notice that this function is only called for now by the 1719 LoopHelasMatrixElement instances at the output stage. 1720 """ 1721 1722 # If no split order is present (unlikely since the 'corrected order' 1723 # normally is a split_order by default, then do nothing 1724 if len(split_orders)==0: 1725 return 1726 1727 self.order_diagram_set(self['born_diagrams'], split_orders) 1728 self.order_diagram_set(self['loop_diagrams'], split_orders) 1729 self.order_diagram_set(self['loop_UVCT_diagrams'], split_orders)
1730
1731 #=============================================================================== 1732 # LoopMultiProcess 1733 #=============================================================================== 1734 -class LoopMultiProcess(diagram_generation.MultiProcess):
1735 """LoopMultiProcess: MultiProcess with loop features. 1736 """ 1737 1738 @classmethod
1739 - def get_amplitude_from_proc(cls, proc, **opts):
1740 """ Return the correct amplitude type according to the characteristics 1741 of the process proc """ 1742 return LoopAmplitude({"process": proc},**opts)
1743
1744 #=============================================================================== 1745 # LoopInducedMultiProcess 1746 #=============================================================================== 1747 -class LoopInducedMultiProcess(diagram_generation.MultiProcess):
1748 """Special mode for the LoopInduced.""" 1749 1750 @classmethod
1751 - def get_amplitude_from_proc(cls,proc,**opts):
1752 """ Return the correct amplitude type according to the characteristics of 1753 the process proc """ 1754 return LoopAmplitude({"process": proc, 'has_born':False},**opts)
1755