Orateur
Description
Searching for new physics often requires testing many different signal hypotheses across an extensive parameter space, such as signal mass or width. Traditional approaches typically involves the training of one classifier per hypothesis, which quickly becomes impractical when scanning over a broad range of parameters. At higher masses, where event yields are low, limited training data leads to unstable classifiers and poor generalization.
A possible solution is to use Parameterized Neural Networks (pNN) [[1],[2]], in which the signal parameters are provided as an additional input alongside the input features. The network learns from multiple hypotheses at once, making it robust to low statistics regime, and capable of smoothly interpolating between trained mass points and generalize to unseen ones. We present a practical use of pNNs for signal-background discrimination in a search for high-mass diphotons resonances with the CMS run-3 dataset. Different strategies for assigning background events are explored, and we show that our model preserves the background mass distribution without introducing artificial shaping.
References
[[1]] P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, D. Whiteson, Parameterized Machine Learning for High-Energy Physics
[[2]] L. Anzalone, T. Diotalevi, D. Bonacorsi, Improving Parametric Neural Networks for High-Energy Physics (and Beyond)
