Organizations: Faculty of Science, Yamagata University, 1-4-12 Kojirakawa-machi, Yamagata, 990-8560 Japan · Department of Physics and Astronomy, University of Alabama, Tuscaloosa, Alabama, AL35487 USA
We construct the Yukawa couplings of the quark sector as overlap integrals of Gaussian wave functions in extra spatial dimensions. Assuming that the wave function of each chiral fermion is localised at a point in the extra dimensions, while that of the Higgs doublet is flat, the Yukawa matrix elements are determined by the relative positions of the three left-handed quark doublets, q1, q2, q3, and the six right-handed quark singlets, u1, u2, u3, d1, d2, d3. We employ numerical optimisation techniques from machine learning to identify field configurations that satisfy all current experimental constraints on the quark Yukawa sector. In this framework, the large hierarchy of quark masses arises naturally, since the overlap integrals of Gaussian wave functions are exponentially sensitive to O(1) separations in the extra dimensions.
Figures & tables
Sample
Optimised parameter values
x1
x2
x3
x4
x5
x6
x7
x8
x9
x10
x11
x12
#1
-0.74222
1.3478
5.4744
-4.4582
6.8517
-0.022080
4.4409
6.3030
-3.5100
-4.5038
3.9302
-4.1567
#2
-1.6859
1.6635
-0.17823
0.20985
0.056992
6.8938
3.5297
6.3481
-4.6365
-4.2183
3.9720
-4.1796
#3
-0.83353
1.4553
-1.0119
-4.6180
6.8937
0.056854
6.3481
3.5295
-4.6370
-4.2187
3.9719
-4.1800
#4
4.9318
-0.38551
-1.2008
4.2447
-6.9569
0.55474
-3.4003
-6.4104
-4.2314
5.2905
-3.9216
-3.9156
#5
0.74028
0.87891
0.85937
0.31770
-0.019286
-7.0584
-6.5114
3.4890
-4.4305
-3.9903
4.6432
-4.0458
Table 1: Optimised parameter values x1,x2,…,x12 for the 5 best fit samples for the one extra dimension model. The optimised loss function values of these 5 samples are 0.942, 1.34, 1.37, 2.20, and 2.54.
Standard Model parameters
yu/10−6
yd/10−5
ys/10−4
yc/10−3
yb/10−2
yt
θ12
θ23/10−2
θ13/10−3
δCP
J/10−5
Reference
6.15
1.35
2.68
3.11
1.401
0.8616
0.22704
4.275
3.77
1.139
3.23
±0.14
±0.02
±0.03
±0.05
±0.009
±0.0043
±0.00082
±0.042
±0.08
±0.023
±0.08
Sample
#1
6.1507
1.3491
2.6854
3.1097
1.4028
0.86196
0.22722
4.2634
3.7786
1.1328
3.1981
#2
6.1822
1.3507
2.6809
3.1105
1.4016
0.86165
0.22741
4.2737
3.7671
1.1389
3.2075
Table 2: Predictions for the Standard Model parameters obtained from the five best-fit samples listed in Table 1 . The reference experimental values are the MS quantities evaluated at 1 TeV [ 13 ] . The 1σ uncertainties are used to define the weights in the loss function.
Figure 1: Field configurations of the 10 best-fit samples in the one-extra-dimension model. The left-handed quark fields qi are shown in red, while the right-handed up- and down-type quark fields, ui and di , are shown in green and blue, respectively. The field q3 is fixed at the origin.
Sample
Optimised parameter values
(type)
x1
x2
x3
x4
x5
x6
x7
x8
x9
x10
x11
x12
#1 (d)
-1.4574
1.2402
4.3009
-2.1939
-0.043863
0.97873
2.7282
0.0056122
-1.9345
-3.8797
-2.8570
-1.2032
#2 (7)
2.1362
1.7137
2.6881
-2.2426
0.034977
0.99208
2.7684
-0.0086459
-1.9004
-3.8944
-2.8538
-1.1421
#3 (d)
-0.43696
2.4282
0.83581
-2.5946
-0.10209
-0.87904
3.0463
0.21507
-3.2692
4.3672
-2.7787
-1.6416
#4 (5)
1.1424
1.2041
1.2432
-2.0543
-0.0062153
0.94375
2.5559
0.24881
-2.0393
-3.9090
-2.8006
-1.1090
#5 (7)
-0.66948
2.3715
0.70856
-3.0679
0.15047
-0.83097
2.9951
-0.55299
-4.0532
-4.3160
2.9002
-1.8187
Table 3: Optimised parameter values x1,x2,…,x12 for the 5 best fit samples for the two extra dimension model. The optimised loss function values for these 5 samples are 0.457, 0.462, 0.482, 0.486, and 0.499.
Figure 2: Optimised field configuration of the best performing sample in the two extra dimension model. The left-handed quark fields qi are shown in red, while the right-handed up- and down-type quark fields, ui and di , are shown in green and blue, respectively. The field q1 is fixed at the origin and the field q2 is placed on the vertical axis.
Standard Model parameters
yu/10−6
yd/10−5
ys/10−4
yc/10−3
yb/10−2
yt
θ12
θ23/10−2
θ13/10−3
δCP
J/10−5
Reference
6.15
1.35
2.68
3.11
1.401
0.8616
0.22704
4.275
3.77
1.139
3.23
±0.14
±0.02
±0.03
±0.05
±0.009
±0.0043
±0.00082
±0.042
±0.08
±0.023
±0.08
Sample
#1
6.1634
1.3474
2.6793
3.1125
1.4014
0.86186
0.22679
4.2766
3.7698
1.1394
3.2045
#2
6.1785
1.3493
2.6805
3.1128
1.4008
0.86157
0.22701
4.2756
3.7717
1.1394
3.2083
Table 4: Predictions for the Standard Model parameters obtained from the five best-fit samples listed in Table 3 . The reference experimental values are the MS quantities evaluated at 1 TeV [ 13 ] . The 1σ uncertainties are used to define the weights in the loss function.
Figure 3: Planar visualisation of 12 dimension parameters {x1,x2,…,x12} of the two extra dimension model, using dimensionality reduction algorithm of t-SNE. The plot shows 502 best-fit samples with optimised loss values <12 . The colour represents the values of the optimised loss function: darker blue for smaller loss values, indicating better performance.
Figure 4: Optimised field configuration of the 48 best-fit samples in the two extra dimension model. The left-handed quark fields qi are shown in red, while the right-handed up- and down-type quark fields, ui and di , are shown in green and blue, respectively. The field q1 is fixed at the origin and the field q2 is constrained on the vertical axis.
We perform a global search for values of the Yukawa matrices and Majorana masses in the Type-I seesaw mechanism. Using flow matching, which is a generative artificial intelligence (generative AI) method, we generate a broad set of solutions reproducing the experimentally measured values of the neutrino mass-squared differences and the mixing angles. Then, a machine learning method known as an autoencoder is applied to uncover non-trivial correlations among physical quantities in the lepton sector. Our analysis reveals new non-linear relations involving neutrino masses and CP phases. These findings may contribute to elucidating the origins of the mass hierarchies and mixing patterns among generation structure.
Haruto Kitagawa, Satsuki Nishimura, Hajime Otsuka
Department of Physics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan · Department of Physics, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-ku, Kyoto 606-8502, Japan · Quantum and Spacetime Research Institute (QuaSR), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
The coupled ghost and gluon Dyson--Schwinger equations (DSEs) of four-dimensional Landau-gauge Yang--Mills (YM) theory are solved with a neural representation trained only from renormalized equation residuals. The neural and fixed-point solutions agree at the percent level and remain stable under changes of initialization, network size, integration grid, and infrared boundary condition. Variations of the three-gluon vertex model produce substantially larger effects than the neural error. The MiniMOM ultraviolet running and the sign change of the gluon Schwinger function are also reproduced within the limitations of the truncation.
Rodrigo Carmo Terin
King Juan Carlos University, Faculty of Experimental Sciences and Technology, Department of Applied Physics, Av. del Alcalde de Móstoles, 28933 Madrid, Spain
We demonstrate that modern machine-learning methods can autonomously reconstruct several flagship analytic structures in scattering amplitudes directly from numerical on-shell data. In particular, we show that the Kawai--Lewellen--Tye (KLT) relations can be rediscovered using symbolic regression applied to colour-ordered Yang--Mills amplitudes with Mandelstam invariants as input features. Using standard feature-selection techniques, specifically column-pivoted QR factorisation, we simultaneously recover the Kleiss--Kuijf and Bern--Carrasco--Johansson (BCJ) relations, identifying a minimal basis of partial amplitudes without any group-theoretic input. We obtain the tree-level KLT relations with high numerical accuracy up to five external legs, using only minimal theoretical priors, and we comment on the obstacles to generalising the method to higher multiplicity. Our results establish symbolic regression as a practical tool for exploring the analytic structure of the scattering-amplitude landscape, and suggests a general data-driven strategy for uncovering hidden relations in general theories. For comparison, we benchmark this general approach with a recently introduced neural-network based method.
Nathan Moynihan
Queen Mary, University of London, Mile End Road, London E1 4NS, UK