模型不适当。对模型进行细微调整会产生粗略拟合：

import numpy as np
from matplotlib import pyplot as plt
from scipy.optimize import curve_fit

xdata = np.array((
    2834.486, 2834.968, 2835.45 , 2835.932, 2836.414, 2836.896, 2837.378, 2837.861,
    2838.343, 2838.825, 2839.307, 2839.789, 2840.271, 2840.753, 2841.235, 2841.718,
    2842.2  , 2842.682, 2843.164, 2843.646, 2844.128, 2844.61 , 2845.093, 2845.575,
    2846.057, 2846.539, 2847.021, 2847.503, 2847.985, 2848.468, 2848.95 , 2849.432,
    2849.914, 2850.396, 2850.878, 2851.36 , 2851.843, 2852.325, 2852.807, 2853.289,
    2853.771, 2854.253, 2854.735, 2855.218, 2855.699, 2856.182, 2856.664, 2857.146,
    2857.628, 2858.11 , 2858.592, 2859.074, 2859.557, 2860.039, 2860.521, 2861.003,
    2861.485, 2861.967, 2862.449, 2862.932, 2863.414, 2863.896, 2864.378, 2864.86 ,
    2865.342, 2865.824, 2866.307, 2866.789, 2867.271, 2867.753, 2868.235, 2868.717,
    2869.199, 2869.682, 2870.164, 2870.646, 2871.128, 2871.61 , 2872.092, 2872.574,
    2873.056, 2873.539, 2874.021, 2874.503, 2874.985, 2875.467, 2875.949, 2876.431,
    2876.914, 2877.396, 2877.878, 2878.36 , 2878.842, 2879.324, 2879.806, 2880.289,
    2880.771, 2881.253, 2881.735, 2882.217,
))
ydata = np.array((
    0.5027119, 0.5155925, 0.5296563, 0.5450429, 0.5619112, 0.5804411, 0.6008373,
    0.6233361, 0.6482099, 0.67577  , 0.7063611, 0.7403504, 0.7781109, 0.8200049,
    0.8663718, 0.9175249, 0.9737514, 1.035319 , 1.102472 , 1.175419 , 1.254304 ,
    1.339163 , 1.429889 , 1.526202 , 1.627649 , 1.733603 , 1.84322  , 1.955248 ,
    2.067605 , 2.176702 , 2.276757 , 2.359875 , 2.417753 , 2.445059 , 2.441798 ,
    2.41245  , 2.362954 , 2.298523 , 2.223243 , 2.14052  , 2.05336  , 1.964326 ,
    1.87539  , 1.787885 , 1.702644 , 1.620191 , 1.540921 , 1.465193 , 1.393333 ,
    1.325607 , 1.262171 , 1.203057 , 1.148185 , 1.097403 , 1.050529 , 1.007382 ,
    0.9678   , 0.9316369, 0.8987471, 0.8689752, 0.8421496, 0.8180863, 0.7965991,
    0.7775094, 0.76065  , 0.7458642, 0.732995 , 0.7218768, 0.7123291, 0.7041584,
    0.6971676, 0.6911709, 0.6860058, 0.6815417, 0.6776828, 0.674363 , 0.6715436,
    0.6692089, 0.6673671, 0.6660498, 0.6653103, 0.6652156, 0.6658351, 0.6672268,
    0.6694273, 0.6724483, 0.676279 , 0.6808962, 0.686272 , 0.6923797, 0.699192 ,
    0.7066767, 0.7147906, 0.7234787, 0.7326793, 0.7423348, 0.7524015, 0.7628553,
    0.7736901, 0.7849081,
))

def gauss(x: np.ndarray, *args: float) -> np.ndarray:
    a, b, c, d = args
    return a*np.exp(-b*(x - c)**2) + d

popt, _ = curve_fit(
    gauss, xdata, ydata,
    p0=(1.7, 0.02, 2851, 0.7),
    maxfev=100_000,
)
print(popt)
fit_y = gauss(xdata, *popt)

plt.scatter(xdata, ydata, label='data', s=5)
plt.plot(xdata, fit_y, '-', label='fit')
plt.legend()
plt.show()

[1.68927347e+00 2.10977276e-02 2.85117456e+03 6.81806648e-01]

要做得更好，您的模型需要进行更多更改。