Paper:
Searching for Higgs Boson Decay Modes with Deep Learning
Particle colliders enable us to probe the fundamental nature of matter by observing exotic particles produced by high-energy collisions. Because the experimental measurements from these collisions are necessarily incomplete and imprecise, machine learning algorithms play a major role in the analysis of experimental data. The high-energy physics community typically relies on standardized machine learning software packages for this analysis, and devotes substantial effort towards improving statistical power by hand-crafting high-level features derived from the raw collider measurements. In this paper, we train artificial neural networks to detect the decay of the Higgs boson to tau leptons on a dataset of 82 million simulated collision events. We demonstrate that deep neural network architectures are particularly well-suited for this task with the ability to automatically discover high-level features from the data and increase discovery significance.

Review:
The paper describes an application of deep neural networks to offline high energy physics particle collider event detection. The data used for training and testing were generated by simulating collisions. The paper describes experiments using a small number of features that correspond to the actual detector measurements as well as engineered features previously proposed for this task.The paper describes a reasonable machine learning approach to an important problem.As an application paper, in order to add substantial value to the conference, the paper needs to provide a practically important improvement over previous approaches to the problem. Furthermore, the choices made when applying the model to the problem need to be clearly explained, justified, and ideally supported empirically. How are the discovery significance numbers in table 3 calculated and what evidence is there that the improvement over the baseline will be exciting to the physicists who would make use of the classifier? Any citation or argument understandable to people outside HEP that supports the usefulness of the improvement would strengthen the paper dramatically.The caption for figure 2 and section 2.1 need clarification. By my count, there are 6 momenta inputs for the charged leptons and 1 transverse momentum imbalance input. However, it isn't completely clear how the jets are dealt with. Are there 3 more jet momenta inputs that hold the sum of the momenta of all jets? And then one more input holding the number of jets? Presumably the momentum angular information mentioned in the caption for figure 2 is not used. How exactly are the inputs encoded?The first sentence of section 2.2 would be improved by some sort of citation to an example of something in the physics literature that deals with some of these high level features.If at all possible, the exact dataset used should be released publicly.The empirical investigation of different neural net architectures and metaparameter settings feels somewhat preliminary and unfinished, especially since the largest networks got the best results and there is no obvious reason why larger networks couldn't be tried relatively quickly with a couple of the machines used. Were any other momentum schedules tried other than ramping up to 0.99? Or a learning rate annealing schedule based on the training progress? How about rectified linear units? The text mentions that there is no overfitting, but to be maximally clear it should mention somewhere that there is no gap between training set and validation set error or provide a training curve. A training curve with training set and validation set progress as a function of epoch number would help the reader judge whether stopping after 50 epochs was reasonable.How large is the test set? Adding a count scale to the ordinate of the plots in figure 4 would have partially answered this question, but it would probably be better just to say how large the test set is.Figure 5 is not particularly valuable and could be eliminated if more space is needed.In general, in an application paper that uses a deep neural net classifier, there should be a strenuous effort to squeeze the most performance possible out of the neural net, possibly by using Bayesian optimization to set the metaparameters.