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SUMMARY:Differentiable reconstruction of the AdS/QCD Warp Factor
DTSTART;VALUE=DATE-TIME:20261029T071500Z
DTEND;VALUE=DATE-TIME:20261029T073000Z
DTSTAMP;VALUE=DATE-TIME:20261023T070900Z
UID:indico-contribution-6200@cern.ch
DESCRIPTION:Speakers: Anna Komleva (MIPT (NRU))\nHolographic QCD models de
 scribe strongly interacting matter through a five-dimensional gravitationa
 l background. In Einstein–Maxwell–Dilaton models\, the warp factor A(z
 ) deforms the AdS metric and affects QCD thermodynamics\, the phase struct
 ure\, and heavy-meson spectra. Such models are commonly calibrated using l
 attice-QCD data [1\, 2] in contrast with employment on the experimental da
 ta.\n\nIn this work\, we develop a differentiable-programming framework fo
 r the inverse reconstruction of the warp factor in AdS/QCD using PyTorch [
 3]. We consider parameterized families of phenomenological ansatz\, in par
 ticular combinations of logarithms of polynomials. Their parameters are op
 timized with gradient-based methods\, in particular Adam [4] and L-BFGS-B 
 [5]\, while automatic differentiation provides gradients through the numer
 ical pipeline without finite-difference approximations.\n\nFor each parame
 ter set\, we construct the holographic background and calculate the phase-
 transition curve in the temperature–baryon-chemical-potential coordinate
 s. Its deviation from the chemical freeze-out line defines the thermodynam
 ic loss\; the method is tested using both an analytic freeze-out parameter
 ization and experimental data from the relativistic nucleus-nucleus collis
 ions [6]. Heavy-meson masses are calculated following [7] using the aforem
 entioned ansatz'. The constraints on the meson masses are included through
  gaussian process emulator differentiable surrogate [8] that approximate t
 he dependence of meson masses on the background parameters. The total loss
  combines information on the thermodynamics and meson spectroscopy along w
 ith regularization on the critical endpoint (CEP) position to achieve smoo
 th and physically admissible warp factors. The procedure reconstructs fami
 lies of warp factors compatible with chemical-freeze-out and spectroscopic
  constraints\, allowing us to assess the stability and non-uniqueness of t
 he inverse problem.\n\n## References\n\n[1] X. Chen and M. Huang\, “Flav
 or dependent critical endpoint from holographic QCD through machine learni
 ng\,” arXiv:2405.06179 [hep-ph] (2024).\n\n[2] M. T. Arun and R. Pal\, 
 “Learning holographic QCD with unflavored meson spectra\,” J. High Ene
 rgy Phys. 06 (2026) 226\n\n[3] A. Paszke et al.\, “Automatic differentia
 tion in PyTorch\,” J. Mach. Learn. Res. 18 (2018) 1\, arXiv:1701.06757 [
 cs.LG].\n\n[4] D. P. Kingma and J. Ba\, “Adam: A Method for Stochastic O
 ptimization\,” in 3rd International Conference on Learning Representatio
 ns (ICLR 2015)\, San Diego\, CA\, USA\, May 7-9\, 2015\, Conference Track 
 Proceedings\, 2015.\n\n[5] D. C. Liu and J. Nocedal\, “On the Limited Me
 mory BFGS Method for Large Scale Optimization\,” in Mathematical Program
 ming\, vol. 45\, pp. 503–528\, 1989.\n\n[6] X. Luo and N. Xu\, “Search
  for the QCD critical point with fluctuations of conserved quantities in r
 elativistic heavy-ion collisions at RHIC: An overview\,” Nucl. Sci. Tech
 . 28 (2017) 112\n\n[7] B. Toniato\, D. Dudal\, S. Mahapatra\, R. da Rocha\
 , and S. S. Jena\, “Holographic QCD model for heavy and exotic mesons at
  finite density: A self-consistent dynamical approach\,” Phys. Rev. D 11
 1\, 126021 (2025)\n\n[8] M. C. Kennedy and A. O'Hagan\, “Bayesian calibr
 ation of computer models\,” J. R. Stat. Soc. Ser. B Stat. Methodol. 63 (
 2001)\, no. 3\, 425–464.\n\nhttps://indico.particle.mephi.ru/event/626/c
 ontributions/6200/
LOCATION:Sevastopol Hotel
URL:https://indico.particle.mephi.ru/event/626/contributions/6200/
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