We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter $\fnl$. Our sample comprises over 12 million targets, covering 14,000 square degrees of the sky, with redshifts in the range $0.2< z < 1.35$. We identify Galactic extinction, survey depth, and astronomical seeing as the primary sources of systematic error, and employ linear regression and artificial neural networks to alleviate non-cosmological excess clustering on large scales. Our methods are tested against simulations with and without $\fnl$ and systematics, showing superior performance of the neural network treatment. The neural network with a set of nine imaging property maps passes our systematic null test criteria, and is chosen as the fiducial treatment. Assuming the universality relation, we find $\fnl = 34^{+24(+50)}_{-44(-73)}$ at 68\%(95\%) confidence. We apply a series of robustness tests (e.g., cuts on imaging, declination, or scales used) that show consistency in the obtained constraints. We study how the regression method biases the measured angular power-spectrum and degrades the $\fnl$ constraining power. The use of the nine maps more than doubles the uncertainty compared to using only the three primary maps in the regression. Our results thus motivate the development of more efficient methods that avoid over-correction, protect large-scale clustering information, and preserve constraining power. Additionally, our results encourage further studies of $\fnl$ with DESI spectroscopic samples, where the inclusion of 3D clustering modes should help separate imaging systematics and lessen the degradation in the $\fnl$ uncertainty.

翻译：我们利用暗能量光谱仪（DESI）成像巡天中发光红星系的角聚类统计来约束局域原初非高斯参数$\fnl$。样本包含超过1200万个目标源，覆盖14,000平方度的天区，红移范围在$0.2< z < 1.35$之间。我们确认银河系消光、巡天深度和天文视宁度是系统误差的主要来源，并采用线性回归和人工神经网络方法来抑制大尺度上的非宇宙学超额成团效应。我们的方法在包含与不包含$\fnl$及系统误差的模拟数据中进行了测试，结果显示神经网络处理方法具有更优性能。采用九幅成像属性图的神经网络通过了系统零检验标准，因此被选为基准处理方法。在假设普适性关系的条件下，我们得到$\fnl = 34^{+24(+50)}_{-44(-73)}$（68\%(95\%)置信度）。我们实施了一系列稳健性检验（如对成像质量、赤纬或使用尺度进行截断），结果表明所得约束具有一致性。我们研究了回归方法如何使测量的角功率谱产生偏差并降低$\fnl$的约束能力。使用九幅图相比仅使用三幅基础图的回归方法会使不确定性增加超过两倍。因此，我们的结果推动着发展更高效的方法，以避免过度修正、保护大尺度成团信息并保持约束能力。此外，我们的研究鼓励利用DESI光谱样本进一步开展$\fnl$研究，其中三维成团模式的引入应有助于分离成像系统误差并减轻$\fnl$不确定性的恶化。