-
Notifications
You must be signed in to change notification settings - Fork 0
/
references.bib
1282 lines (1032 loc) · 41.5 KB
/
references.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
@book{mayo, place={Cambridge}, title={Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars}, DOI={10.1017/9781107286184}, publisher={Cambridge University Press}, author={Mayo, Deborah G.}, year={2018}}
@misc{jaxshower,
doi = {10.48550/ARXIV.2208.02274},
url = {https://arxiv.org/abs/2208.02274},
author = {Nachman, Benjamin and Prestel, Stefan},
keywords = {High Energy Physics - Phenomenology (hep-ph), High Energy Physics - Experiment (hep-ex), FOS: Physical sciences, FOS: Physical sciences},
title = {Morphing parton showers with event derivatives},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@misc{atlaspublic,
url = {https://atlaspo.cern.ch/public/summary_plots/},
author = "{ATLAS Collaboration}",
title = {ATLAS summary physics plots},
year = {2022}
}
@software{lukasautodiff,
author = {Lukas Heinrich},
title = {lukasheinrich/pyhep2020-autodiff-tutorial 0.0.2},
month = oct,
year = 2020,
publisher = {Zenodo},
version = {0.0.2},
doi = {10.5281/zenodo.4067099},
url = {https://doi.org/10.5281/zenodo.4067099}
}
@misc{ox,
author = {{Oxford University}},
howpublished = {website},
title = {Why two Higgs are better than one},
year = {2021},
url = {https://www.physics.ox.ac.uk/news/why-two-higgs-are-better-one}
}
@article{evt,
author = "Aad, Georges and others",
collaboration = "ATLAS",
title = "{Search for Higgs boson pair production in the two bottom quarks plus two photons final state in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector}",
eprint = "2112.11876",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
reportNumber = "CERN-EP-2021-180",
doi = "10.1103/PhysRevD.106.052001",
journal = "Phys. Rev. D",
volume = "106",
number = "5",
pages = "052001",
year = "2022"
}
@misc{m4l,
doi = {10.48550/ARXIV.2207.00320},
url = {https://arxiv.org/abs/2207.00320},
author = {{ATLAS Collaboration}
},
keywords = {High Energy Physics - Experiment (hep-ex), FOS: Physical sciences, FOS: Physical sciences},
title = {Measurement of the Higgs boson mass in the $H \rightarrow ZZ^* \rightarrow 4\ell$ decay channel using 139 fb$^{-1}$ of $\sqrt{s}=13$ TeV $pp$ collisions recorded by the ATLAS detector at the LHC},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
@misc{madjax,
doi = {10.48550/ARXIV.2203.00057},
url = {https://arxiv.org/abs/2203.00057},
author = {Heinrich, Lukas and Kagan, Michael},
keywords = {High Energy Physics - Phenomenology (hep-ph), Machine Learning (cs.LG), Computational Physics (physics.comp-ph), Data Analysis, Statistics and Probability (physics.data-an), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Differentiable Matrix Elements with MadJax},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
@misc{ratios,
doi = {10.23731/CYRM-2017-002},
url = {https://e-publishing.cern.ch/index.php/CYRM/issue/view/32},
author = {{CERN}},
language = {en},
title = {CERN Yellow Reports: Monographs, Vol 2 (2017): Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector},
publisher = {CERN},
year = {2017},
chapter = {4},
copyright = {This work is licensed under a Creative Commons Attribution 4.0 International License.}
}
@article{freq,
author = {Senn, Stephen},
year = {2011},
month = {01},
pages = {},
title = {You May Believe You Are a Bayesian But You Are Probably Wrong},
volume = {2},
journal = {Rationality, Markets and Morals}
}
@software{examples,
author = {Simpson, Nathan},
doi = {10.5281/zenodo.7129990},
month = {9},
title = {{phinate/differentiable-analysis-examples}},
version = {PyHEP2022 (0.1.3)},
year = {2022}
}
@article{ns,
author = {John Skilling},
title = {{Nested sampling for general Bayesian computation}},
volume = {1},
journal = {Bayesian Analysis},
number = {4},
publisher = {International Society for Bayesian Analysis},
pages = {833 -- 859},
keywords = {algorithm, annealing, Bayesian computation, evidence, marginal likelihood, Model selection, nest, phase change},
year = {2006},
doi = {10.1214/06-BA127},
URL = {https://doi.org/10.1214/06-BA127}
}
@book{buckley,
author = {Buckley, Andy and White, Christopher and White, Martin},
title = {Practical Collider Physics},
publisher = {IOP Publishing},
year = {2021},
series = {2053-2563},
isbn = {978-0-7503-2444-1},
url = {https://dx.doi.org/10.1088/978-0-7503-2444-1},
doi = {10.1088/978-0-7503-2444-1}
}
@article{wald,
ISSN = {00029947},
URL = {http://www.jstor.org/stable/1990256},
author = {Abraham Wald},
journal = {Transactions of the American Mathematical Society},
number = {3},
pages = {426--482},
publisher = {American Mathematical Society},
title = {Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large},
urldate = {2022-10-13},
volume = {54},
year = {1943}
}
@article{adadelta,
author = {Matthew D. Zeiler},
title = {{ADADELTA:} An Adaptive Learning Rate Method},
journal = {CoRR},
volume = {abs/1212.5701},
year = {2012},
url = {http://arxiv.org/abs/1212.5701},
eprinttype = {arXiv},
eprint = {1212.5701},
timestamp = {Mon, 13 Aug 2018 16:45:57 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1212-5701.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{neos,
doi = {10.48550/ARXIV.2203.05570},
url = {https://arxiv.org/abs/2203.05570},
author = {Simpson, Nathan and Heinrich, Lukas},
keywords = {Data Analysis, Statistics and Probability (physics.data-an), Machine Learning (cs.LG), High Energy Physics - Experiment (hep-ex), High Energy Physics - Phenomenology (hep-ph), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {neos: End-to-End-Optimised Summary Statistics for High Energy Physics},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
@article{FC,
doi = {10.1103/physrevd.57.3873},
url = {https://doi.org/10.1103%2Fphysrevd.57.3873},
year = 1998,
month = {apr},
publisher = {American Physical Society ({APS})},
volume = {57},
number = {7},
pages = {3873--3889},
author = {Gary J. Feldman and Robert D. Cousins},
title = {Unified approach to the classical statistical analysis of small signals},
journal = {Physical Review D}
}
@misc{keras,
title={Keras},
author={Chollet, Fran\c{c}ois and others},
year={2015},
howpublished={\url{https://keras.io}},
}
@inproceedings{sklearn,
author = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and
Fabian Pedregosa and Andreas Mueller and Olivier Grisel and
Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort
and Jaques Grobler and Robert Layton and Jake VanderPlas and
Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux},
title = {{API} design for machine learning software: experiences from the scikit-learn
project},
booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning},
year = {2013},
pages = {108--122},
}
@misc{bob,
doi = {10.48550/ARXIV.1807.05996},
url = {https://arxiv.org/abs/1807.05996},
author = {Cousins, Robert D.},
keywords = {Data Analysis, Statistics and Probability (physics.data-an), FOS: Physical sciences, FOS: Physical sciences},
title = {Lectures on Statistics in Theory: Prelude to Statistics in Practice},
publisher = {arXiv},
year = {2018},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@misc{bayesrant,
url = {http://www.stat.columbia.edu/~gelman/stuff_for_blog/rant2.pdf},
author = {Skilling, John},
title = {This Physicist's view of Gelman Bayes},
year = {2008}
}
@book{sivia,
title={Data Analysis: A Bayesian Tutorial},
author={Sivia, D. and Skilling, J.},
isbn={9780198568315},
lccn={2006284782},
series={Oxford science publications},
url={https://books.google.ch/books?id=lYMSDAAAQBAJ},
year={2006},
publisher={OUP Oxford}
}
@article{kolmogorov-thing,
doi = {10.1214/088342305000000467},
url = {https://doi.org/10.1214%2F088342305000000467},
year = 2006,
month = {feb},
publisher = {Institute of Mathematical Statistics},
volume = {21},
number = {1},
author = {Glenn Shafer and Vladimir Vovk},
title = {The Sources of Kolmogorov's Grundbegriffe},
journal = {Statistical Science}
}
@article{deepsetsjets,
doi = {10.1007/jhep01(2019)121},
url = {https://doi.org/10.1007%2Fjhep01%282019%29121},
year = 2019,
month = {jan},
publisher = {Springer Science and Business Media {LLC}
},
volume = {2019},
number = {1},
author = {Patrick T. Komiske and Eric M. Metodiev and Jesse Thaler},
title = {Energy flow networks: deep sets for particle jets},
journal = {Journal of High Energy Physics}
}
@article{flows,
doi = {10.48550/ARXIV.1912.02762},
url = {https://arxiv.org/abs/1912.02762},
author = {Papamakarios, George and Nalisnick, Eric and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji},
keywords = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Normalizing Flows for Probabilistic Modeling and Inference},
publisher = {arXiv},
year = {2019},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@ARTICLE{gnn,
author={Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele},
journal={IEEE Transactions on Neural Networks},
title={The Graph Neural Network Model},
year={2009},
volume={20},
number={1},
pages={61-80},
doi={10.1109/TNN.2008.2005605}}
@article{vit,
author = {Alexey Dosovitskiy and
Lucas Beyer and
Alexander Kolesnikov and
Dirk Weissenborn and
Xiaohua Zhai and
Thomas Unterthiner and
Mostafa Dehghani and
Matthias Minderer and
Georg Heigold and
Sylvain Gelly and
Jakob Uszkoreit and
Neil Houlsby},
title = {An Image is Worth 16x16 Words: Transformers for Image Recognition
at Scale},
journal = {CoRR},
volume = {abs/2010.11929},
year = {2020},
url = {https://arxiv.org/abs/2010.11929},
eprinttype = {arXiv},
eprint = {2010.11929},
timestamp = {Fri, 20 Nov 2020 14:04:05 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2010-11929.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{geodl,
author = {Michael M. Bronstein and
Joan Bruna and
Taco Cohen and
Petar Velickovic},
title = {Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges},
journal = {CoRR},
volume = {abs/2104.13478},
year = {2021},
url = {https://arxiv.org/abs/2104.13478},
eprinttype = {arXiv},
eprint = {2104.13478},
timestamp = {Tue, 04 May 2021 15:12:43 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-13478.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@misc{deepsets,
doi = {10.48550/ARXIV.1703.06114},
url = {https://arxiv.org/abs/1703.06114},
author = {Zaheer, Manzil and Kottur, Satwik and Ravanbakhsh, Siamak and Poczos, Barnabas and Salakhutdinov, Ruslan and Smola, Alexander},
keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Deep Sets},
publisher = {arXiv},
year = {2017},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@inproceedings{cnn,
author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
booktitle = {Advances in Neural Information Processing Systems},
editor = {F. Pereira and C.J. Burges and L. Bottou and K.Q. Weinberger},
pages = {},
publisher = {Curran Associates, Inc.},
title = {ImageNet Classification with Deep Convolutional Neural Networks},
url = {https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf},
volume = {25},
year = {2012}
}
@article{transformers,
author = {Ashish Vaswani and
Noam Shazeer and
Niki Parmar and
Jakob Uszkoreit and
Llion Jones and
Aidan N. Gomez and
Lukasz Kaiser and
Illia Polosukhin},
title = {Attention Is All You Need},
journal = {CoRR},
volume = {abs/1706.03762},
year = {2017},
url = {http://arxiv.org/abs/1706.03762},
eprinttype = {arXiv},
eprint = {1706.03762},
timestamp = {Sat, 23 Jan 2021 01:20:40 +0100},
biburl = {https://dblp.org/rec/journals/corr/VaswaniSPUJGKP17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{realnvp,
author = {Laurent Dinh and
Jascha Sohl{-}Dickstein and
Samy Bengio},
title = {Density estimation using Real {NVP}},
journal = {CoRR},
volume = {abs/1605.08803},
year = {2016},
url = {http://arxiv.org/abs/1605.08803},
eprinttype = {arXiv},
eprint = {1605.08803},
timestamp = {Mon, 13 Aug 2018 16:47:21 +0200},
biburl = {https://dblp.org/rec/journals/corr/DinhSB16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@software{nflows,
author = {Conor Durkan and
Artur Bekasov and
Iain Murray and
George Papamakarios},
title = {{nflows}: normalizing flows in {PyTorch}},
month = nov,
year = 2020,
publisher = {Zenodo},
version = {v0.14},
doi = {10.5281/zenodo.4296287},
url = {https://doi.org/10.5281/zenodo.4296287}
}
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
@article{torch,
author = {Adam Paszke and
Sam Gross and
Francisco Massa and
Adam Lerer and
James Bradbury and
Gregory Chanan and
Trevor Killeen and
Zeming Lin and
Natalia Gimelshein and
Luca Antiga and
Alban Desmaison and
Andreas K{\"{o}}pf and
Edward Z. Yang and
Zach DeVito and
Martin Raison and
Alykhan Tejani and
Sasank Chilamkurthy and
Benoit Steiner and
Lu Fang and
Junjie Bai and
Soumith Chintala},
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
journal = {CoRR},
volume = {abs/1912.01703},
year = {2019},
url = {http://arxiv.org/abs/1912.01703},
eprinttype = {arXiv},
eprint = {1912.01703},
timestamp = {Tue, 02 Nov 2021 15:18:32 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1912-01703.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{ensembles,
author = {Mudasir A. Ganaie and
Minghui Hu and
Mohammad Tanveer and
Ponnuthurai N. Suganthan},
title = {Ensemble deep learning: {A} review},
journal = {CoRR},
volume = {abs/2104.02395},
year = {2021},
url = {https://arxiv.org/abs/2104.02395},
eprinttype = {arXiv},
eprint = {2104.02395},
timestamp = {Mon, 21 Jun 2021 12:14:53 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-02395.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{made,
author = {Mathieu Germain and
Karol Gregor and
Iain Murray and
Hugo Larochelle},
title = {{MADE:} Masked Autoencoder for Distribution Estimation},
journal = {CoRR},
volume = {abs/1502.03509},
year = {2015},
url = {http://arxiv.org/abs/1502.03509},
eprinttype = {arXiv},
eprint = {1502.03509},
timestamp = {Mon, 13 Aug 2018 16:46:39 +0200},
biburl = {https://dblp.org/rec/journals/corr/GermainGML15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{pnn,
doi = {10.1140/epjc/s10052-016-4099-4},
url = {https://doi.org/10.1140%2Fepjc%2Fs10052-016-4099-4},
year = 2016,
month = {apr},
publisher = {Springer Science and Business Media {LLC}
},
volume = {76},
number = {5},
author = {Pierre Baldi and Kyle Cranmer and Taylor Faucett and Peter Sadowski and Daniel Whiteson},
title = {Parameterized neural networks for high-energy physics},
journal = {The European Physical Journal C}
}
@article{pythia,
doi = {10.1016/j.cpc.2015.01.024},
url = {https://doi.org/10.1016%2Fj.cpc.2015.01.024},
year = 2015,
month = {jun},
publisher = {Elsevier {BV}
},
volume = {191},
pages = {159--177},
author = {Torbjörn Sjöstrand and Stefan Ask and Jesper R. Christiansen and Richard Corke and Nishita Desai and Philip Ilten and Stephen Mrenna and Stefan Prestel and Christine O. Rasmussen and Peter Z. Skands},
title = {An introduction to {PYTHIA} 8.2},
journal = {Computer Physics Communications}
}
@article{nnpdf,
author = "Ball, Richard D. and others",
collaboration = "NNPDF",
title = "{Parton distributions for the LHC Run II}",
eprint = "1410.8849",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
reportNumber = "EDINBURGH-2014-15, IFUM-1034-FT, CERN-PH-TH-2013-253, OUTP-14-11P, CAVENDISH-HEP-14-11",
doi = "10.1007/JHEP04(2015)040",
journal = "JHEP",
volume = "04",
pages = "040",
year = "2015"
}
@article{evtgen,
author = "Ryd, Anders and Lange, David and Kuznetsova, Natalia and Versille, Sophie and Rotondo, Marcello and Kirkby, David P. and Wuerthwein, Frank K. and Ishikawa, Akimasa",
title = "{EvtGen: A Monte Carlo Generator for B-Physics}",
reportNumber = "EVTGEN-V00-11-07",
month = "5",
year = "2005"
}
@article{sherpa,
doi = {10.21468/scipostphys.7.3.034},
url = {https://doi.org/10.21468%2Fscipostphys.7.3.034},
year = 2019,
month = {sep},
publisher = {Stichting {SciPost}
},
volume = {7},
number = {3},
author = {Enrico Bothmann and Gurpreet Singh Chahal and Stefan Höche and Johannes Krause and Frank Krauss and Silvan Kuttimalai and Sebastian Liebschner and Davide Napoletano and Marek Schönherr and Holger Schulz and Steffen Schumann and Frank Siegert},
title = {Event generation with Sherpa 2.2},
journal = {{SciPost} Physics}
}
@article{gps,
author = {Görtler, Jochen and Kehlbeck, Rebecca and Deussen, Oliver},
title = {A Visual Exploration of Gaussian Processes},
journal = {Distill},
year = {2019},
note = {https://distill.pub/2019/visual-exploration-gaussian-processes},
doi = {10.23915/distill.00017}
}
@article{sh-sens,
author = "{Sebastian Baum, Nausheen R. Shah}",
title = "{Benchmark Suggestions for Resonant Double Higgs Production at the LHC for Extended Higgs Sectors}",
year = "2010",
eprint = "1904.10810",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
reportNumber = "NORDITA-2019-037",
}
@misc{meatball,
title={The MEAL Collaboration (MEat ball AcceLerator)},
url={https://docs.google.com/presentation/d/1gsmlsq8gu11pZV4X-yBy_g1DLFZXASfZgYrS-vn17SU/edit?usp=sharing},
journal={Stupid Hackathon Sweden},
year={2019},
author={Doglioni, Caterina and Suarez, Rebeca, and Simpson, Nathan and Mullier, Geoffrey and Cox, David and Kalderon, William}}
@misc{rmsprop,
author = {Geoffrey Hinton},
url = {https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf},
title = {Neural Networks for Machine Learning Lecture 6a: Overview of mini-batch gradient descent},
year = {2018}
}
@book{sm,
author = "Oerter, R.",
title = "{The theory of almost everything: The standard model, the unsung triumph of modern physics}",
year = "2006"
}
@Article{ATLAS,
author = "{ATLAS Collaboration}",
title = "{The ATLAS Experiment at the CERN Large Hadron Collider}",
journal = "JINST",
volume = "3",
year = "2008",
pages = "S08003",
doi = "10.1088/1748-0221/3/08/S08003",
primaryClass = "hep-ex",
}
% N2HDM orignal theory paper - https://journals.aps.org/prd/abstract/10.1103/PhysRevD.79.023521
@Article{N2HDM,
author = "He, Xiao-Gang and Li, Tong and Li, Xue-Qian and Tandean, Jusak and Tsai, Ho-Chin",
title = "Constraints on scalar dark matter from direct experimental searches",
journal = "Phys. Rev. D",
volume = "79",
issue = "2",
year = "2009",
pages = "0235212",
doi = "10.1103/PhysRevD.79.023521",
url = "https://link.aps.org/doi/10.1103/PhysRevD.79.023521",
eprint = "0811.0658",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% NMSSM https://arxiv.org/abs/0910.1785
@article{NMSSM,
title = {The Next-to-Minimal Supersymmetric Standard Model},
journal = {Physics Reports},
volume = {496},
number = {1},
pages = {1-77},
year = {2010},
issn = {0370-1573},
doi = {https://doi.org/10.1016/j.physrep.2010.07.001},
url = {https://www.sciencedirect.com/science/article/pii/S0370157310001614},
author = {Ulrich Ellwanger and Cyril Hugonie and Ana M. Teixeira},
eprint = "0910.1785",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% complex 2HDM model https://arxiv.org/abs/hep-ph/0211371 it is unpublished :( there is also the following
% published paper: https://arxiv.org/abs/hep-ph/0506227
@article{C2HDM,
author = "{Ilya F. Ginzburg, Maria Krawczyk and Per Osland }",
title = "{Two-Higgs-Doublet Models with CP violation}",
year = "2002",
eprint = "0211371",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
reportNumber = "CERN-TH/2002-330",
}
% TRSM https://arxiv.org/abs/1908.08554
@article{TRSM,
title = {Two-real-scalar-singlet extension of the SM: LHC phenomenology and benchmark scenarios},
journal = {The European Physical Journal C},
volume = {80},
number = {151},
%pages = {1-77},
year = {2020},
doi = {https://doi.org/10.1140/epjc/s10052-020-7655-x},
url = {https://link.springer.com/article/10.1140/epjc/s10052-020-7655-x},
author = {Tania Robens, Tim Stefaniak and Jonas Wittbrodt},
eprint = "1908.08554",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
@misc{lrt,
doi = {10.48550/ARXIV.1506.02169},
url = {https://arxiv.org/abs/1506.02169},
author = {Cranmer, Kyle and Pavez, Juan and Louppe, Gilles},
keywords = {Applications (stat.AP), Data Analysis, Statistics and Probability (physics.data-an), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences, 62P35, 62F99, 62H30},
title = {Approximating Likelihood Ratios with Calibrated Discriminative Classifiers},
publisher = {arXiv},
year = {2015},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@misc{adam,
doi = {10.48550/ARXIV.1412.6980},
url = {https://arxiv.org/abs/1412.6980},
author = {Kingma, Diederik P. and Ba, Jimmy},
keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Adam: A Method for Stochastic Optimization},
publisher = {arXiv},
year = {2014},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@article{cls,
doi = {10.1088/0954-3899/28/10/313},
url = {https://dx.doi.org/10.1088/0954-3899/28/10/313},
year = {2002},
month = {sep},
publisher = {},
volume = {28},
number = {10},
pages = {2693},
author = {A L Read},
title = {Presentation of search results: the CLs technique},
journal = {Journal of Physics G: Nuclear and Particle Physics}
}
@article{momentum,
title = {On the momentum term in gradient descent learning algorithms},
journal = {Neural Networks},
volume = {12},
number = {1},
pages = {145-151},
year = {1999},
issn = {0893-6080},
doi = {https://doi.org/10.1016/S0893-6080(98)00116-6},
url = {https://www.sciencedirect.com/science/article/pii/S0893608098001166},
author = {Ning Qian},
keywords = {Momentum, Gradient descent learning algorithm, Damped harmonic oscillator, Critical damping, Learning rate, Speed of convergence},
abstract = {A momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer simulations. In particular, I derive the bounds for convergence on learning-rate and momentum parameters, and demonstrate that the momentum term can increase the range of learning rate over which the system converges. The optimal condition for convergence is also analyzed.}
}
% BAU
% Baryogenesis in the Two-Higgs Doublet Model https://arxiv.org/abs/hep-ph/0605242
%Lars Fromme, Stephan J. Huber, Michael Seniuch
%https://iopscience.iop.org/article/10.1088/1126-6708/2006/11/038
%https://doi.org/10.1088/1126-6708/2006/11/038
@article{asym1,
doi = {10.1088/1126-6708/2006/11/038},
url = {https://doi.org/10.1088/1126-6708/2006/11/038},
year = 2006,
month = {nov},
publisher = {Springer Science and Business Media {LLC}},
volume = {2006},
number = {11},
pages = {038--038},
author = {Lars Fromme and Stephan J Huber and Michael Seniuch},
title = {Baryogenesis in the two-Higgs doublet model},
journal = {Journal of High Energy Physics},
eprint = "0605242",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
@inproceedings{kylenotes,
author = "Cranmer, Kyle",
title = "{Practical Statistics for the LHC}",
booktitle = "{2011 European School of High-Energy Physics}",
eprint = "1503.07622",
archivePrefix = "arXiv",
primaryClass = "physics.data-an",
doi = "10.5170/CERN-2014-003.267",
pages = "267--308",
year = "2014"
}
%https://arxiv.org/abs/0909.0520
@article{dm1,
title = {Gauge singlet scalar as inflaton and thermal relic dark matter},
author = {Lerner, Rose N. and McDonald, John},
journal = {Phys. Rev. D},
volume = {80},
issue = {12},
pages = {123507},
numpages = {14},
year = {2009},
month = {Dec},
publisher = {American Physical Society},
doi = {10.1103/PhysRevD.80.123507},
url = {https://link.aps.org/doi/10.1103/PhysRevD.80.123507},
eprint = "0909.0520",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
@misc{dtong,
author = {David Tong},
howpublished = {Website},
title = {David Tong: Lectures on Particle Physics},
year = {2022},
url = {https://www.damtp.cam.ac.uk/user/tong/particle.html}
}
% relevance of the models for dark matter
@article{dm2,
title = {Complex scalar singlet dark matter: Vacuum stability and phenomenology},
author = {Gonderinger, Matthew and Lim, Hyungjun and Ramsey-Musolf, Michael J.},
journal = {Phys. Rev. D},
volume = {86},
issue = {4},
pages = {043511},
numpages = {20},
year = {2012},
month = {Aug},
publisher = {American Physical Society},
doi = {10.1103/PhysRevD.86.043511},
url = {https://link.aps.org/doi/10.1103/PhysRevD.86.043511},
eprint = "1202.1316",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% relevance to DM and BAU
@article{asym2,
title = {Impact of a complex singlet: Electroweak baryogenesis and dark matter},
author = {Jiang, Minyuan and Bian, Ligong and Huang, Weicong and Shu, Jing},
journal = {Phys. Rev. D},
volume = {93},
issue = {6},
pages = {065032},
numpages = {15},
year = {2016},
month = {Mar},
publisher = {American Physical Society},
doi = {10.1103/PhysRevD.93.065032},
url = {https://link.aps.org/doi/10.1103/PhysRevD.93.065032},
eprint = "1502.07574",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
@Article{matplotlib,
Author = {Hunter, J. D.},
Title = {Matplotlib: A 2D graphics environment},
Journal = {Computing in Science \& Engineering},
Volume = {9},
Number = {3},
Pages = {90--95},
abstract = {Matplotlib is a 2D graphics package used for Python for
application development, interactive scripting, and publication-quality
image generation across user interfaces and operating systems.},
publisher = {IEEE COMPUTER SOC},
doi = {10.1109/MCSE.2007.55},
year = 2007
}
@article{ml-essay,
ISSN = {00027162},
URL = {http://www.jstor.org/stable/1033694},
abstract = {Artificial intelligence is neither a myth nor a threat to man. It relates to a serious attempt to develop machine methods for dealing with some of the perplexing problems that should, in all justice, be delegated to machines but which now seem to require the exercise of human intelligence. Two fundamentally different approaches to the problem are being explored, the one aimed at a complete understanding of the intellectual processes involved and the other aimed at duplicating the assumed specific behavior of the brain. The first approach concerns itself with such matters as search, pattern recognition, learning, planning, and induction; the second approach involves a study of the behavior of random nets. It is fair to conclude that artificial intelligence promises to reduce rather than to augment technological unemployment.},
author = {Arthur L. Samuel},
journal = {The Annals of the American Academy of Political and Social Science},
pages = {10--20},
publisher = {[Sage Publications, Inc., American Academy of Political and Social Science]},
title = {Artificial Intelligence: A Frontier of Automation},
urldate = {2022-10-06},
volume = {340},
year = {1962}
}
@misc{maf,
doi = {10.48550/ARXIV.1705.07057},
url = {https://arxiv.org/abs/1705.07057},
author = {Papamakarios, George and Pavlakou, Theo and Murray, Iain},
keywords = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Masked Autoregressive Flow for Density Estimation},
publisher = {arXiv},
year = {2017},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@misc{covariance,
author = {Vincent Spruyt},
howpublished = {website},
title = {A geometric interpretation of the covariance matrix},
year = {2014}
}
@article{NNLOPS,
doi = {10.1007/jhep10(2013)222},
url = {https://doi.org/10.1007%2Fjhep10%282013%29222},
year = 2013,
month = {oct},
publisher = {Springer Science and Business Media {LLC}
},
volume = {2013},