Charting the flavor landscape of MSSM-like Abelian heterotic orbifolds

Abstract

Discovering a selection principle and the origin of flavor symmetries from an ultraviolet completion of particle physics is an interesting open task. As a step in this direction, we classify all possible flavor symmetries of 4D massless spectra emerging from supersymmetric Abelian orbifold compactifications, including roto-translations and non-factorizable tori, for generic moduli values. Although these symmetries are valid in all string theories, we focus on the E 8 ×E 8 heterotic string. We perform the widest known search of E 8 ×E 8 Abelian orbifold compactifications, yielding over 121,000 models with MSSM-like features. About 75.4% of these models exhibit flavor symmetries containing D 4 factors and only nearly 1.2% have ∆(54) factors. The remaining models are furnished with purely Abelian flavor symmetries. Our findings suggest that, should particle phenomenology arise from such a heterotic orbifold, it could accommodate only one of these flavor symmetries.

Contrast data mining for the MSSM from strings

“…Compared to the hidden E 8 dataset with 136 inequivalent MSSM-like Z 6 -II models we now have in total 415 MSSM-like models. This is already more than in any existing Z 6 -II search [17][18][19][20]. Hence, we were able to significantly improve the search for inequivalent MSSM-like models in the Z 6 -II as can be seen in table 3.…”

“…Finally, combining all datasets yields in total 468 inequivalent MSSM-like Z 6 -II models. 6 To summarize, we were able to significantly exceed all previous searches for MSSM-like Z 6 -II models [17][18][19][20] by excluding those regions in the Z 6 -II orbifold landscape where most likely no MSSM-like model exists. It is tempting to speculate that some of our contrast patterns might even be necessary conditions for all MSSM-like models.…”

“…sr of unbroken roots from the hidden E 8 factor for MSSM-like orbifold models from refs. [17,18] for various Z N orbifold geometries O (where "all" refers to the various lattices for a given Z N orbifold geometry, as given in the first column of table 8. ).…”

“…To compute these bounds, we use the traditional searches of refs. [17,18] as a background search and split the combined dataset into datasets D (O from [17,18]) corresponding to the different Z N orbifold geometries O. Then, the conventional search in refs.…”

“…[5][6][7][8][9][10][11][12][13][14][15] for other schemes. Then, the conventional approach to search for MSSM-like string models from heterotic orbifolds is given by a random scan in the landscape [16][17][18] or in fertile islands that can be identified by certain patterns, like local GUTs [19][20][21]. In this paper, we show that the approach of defining fertile islands can be generalized by applying machine learning techniques to the string landscape [22][23][24][25], see also [26][27][28][29][30][31][32][33].…”