New Physics Searches with Photons in CDF

A brief review of searches for physics beyond the Standard Model with photons using the CDF detector at the Tevatron is given here. These include searches for supersymmetry, extra dimensions, excited electrons and W/Z$+\gamma$ production, as well as anomalous photon production. Recent results from CDF Run II experiment is presented, but some results from Run I are also reviewed.Comment: To appear in the Proceedings of SUSY 2003, held at the University of Arizona, Tucson, AZ, 5-10 June 200

Searches for Physics Beyond the Standard Model at CMS

Recent results on searches for physics beyond the Standard Model at Large Hadron Collider are presented, based on early LHC data in proton-proton collisions at $\sqrt{s} = 7$ TeV collected by the CMS experiment. Prospects of early SUSY searches at CMS are also outlined.Comment: 6 pages, Proceedings of Hadron Collider Physics Symposium, HCP 2010, Toront

Monte Carlo Bayesian Reinforcement Learning

Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in a model and represents uncertainty in model parameters by maintaining a probability distribution over them. This paper presents Monte Carlo BRL (MC-BRL), a simple and general approach to BRL. MC-BRL samples a priori a finite set of hypotheses for the model parameter values and forms a discrete partially observable Markov decision process (POMDP) whose state space is a cross product of the state space for the reinforcement learning task and the sampled model parameter space. The POMDP does not require conjugate distributions for belief representation, as earlier works do, and can be solved relatively easily with point-based approximation algorithms. MC-BRL naturally handles both fully and partially observable worlds. Theoretical and experimental results show that the discrete POMDP approximates the underlying BRL task well with guaranteed performance.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012