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