Variational Autoencoders for New Physics Mining at the Large Hadron Collider

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn't make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.Comment: 29 pages, 12 figures, 5 table

Final results from the Palo Verde Neutrino Oscillation Experiment

The analysis and results are presented from the complete data set recorded at Palo Verde between September 1998 and July 2000. In the experiment, the \nuebar interaction rate has been measured at a distance of 750 and 890 m from the reactors of the Palo Verde Nuclear Generating Station for a total of 350 days, including 108 days with one of the three reactors off for refueling. Backgrounds were determined by (a) the $swap$ technique based on the difference between signal and background under reversal of the positron and neutron parts of the correlated event and (b) making use of the conventional reactor-on and reactor-off cycles. There is no evidence for neutrino oscillation and the mode \nuebar\to\bar\nu_x was excluded at 90% CL for \dm>1.1\times10^{-3} eV$^2$ at full mixing, and \sinq>0.17 at large \dm.Comment: 11 pages, 8 figure

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure

RICE Limits on the Diffuse Ultra-High Energy Neutrino Flux

We present new limits on ultra-high energy neutrino fluxes above 100 PeV based on data collected by the Radio Ice Cherenkov Experiment (RICE) at the South Pole from 1999-2005. We discuss estimation of backgrounds, calibration and data analysis algorithms (both on-line and off-line), procedures used for the dedicated neutrino search, and refinements in our Monte Carlo (MC) simulation, including recent in situ measurements of the complex ice dielectric constant. An enlarged data set and a more detailed study of hadronic showers results in a sensitivity improvement of more than one order of magnitude compared to our previously published results. Examination of the full RICE data set yields zero acceptable neutrino candidates, resulting in 95% confidence-level model dependent limits on the flux (E_\nu)^2(d\phi/dE_\nu)<10^{-6} GeV/(cm^2s~sr}) in the energy range 10^{17}< E_\nu< 10^{20} eV. The new RICE results rule out the most intense flux model projections at 95% confidence level.Comment: Submitted to Astropart. Phy

Large-Scale Image Processing with the ROTSE Pipeline for Follow-Up of Gravitational Wave Events

Electromagnetic (EM) observations of gravitational-wave (GW) sources would bring unique insights into a source which are not available from either channel alone. However EM follow-up of GW events presents new challenges. GW events will have large sky error regions, on the order of 10-100 square degrees, which can be made up of many disjoint patches. When searching such large areas there is potential contamination by EM transients unrelated to the GW event. Furthermore, the characteristics of possible EM counterparts to GW events are also uncertain. It is therefore desirable to be able to assess the statistical significance of a candidate EM counterpart, which can only be done by performing background studies of large data sets. Current image processing pipelines such as that used by ROTSE are not usually optimised for large-scale processing. We have automated the ROTSE image analysis, and supplemented it with a post-processing unit for candidate validation and classification. We also propose a simple ad hoc statistic for ranking candidates as more likely to be associated with the GW trigger. We demonstrate the performance of the automated pipeline and ranking statistic using archival ROTSE data. EM candidates from a randomly selected set of images are compared to a background estimated from the analysis of 102 additional sets of archival images. The pipeline's detection efficiency is computed empirically by re-analysis of the images after adding simulated optical transients that follow typical light curves for gamma-ray burst afterglows and kilonovae. We show that the automated pipeline rejects most background events and is sensitive to simulated transients to limiting magnitudes consistent with the limiting magnitude of the images

Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark

We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. Anomaly detection based on ALAD matches performances reached by Variational Autoencoders, with a substantial improvement in some cases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, we show how a data-driven anomaly detection and characterization would work in real life, re-discovering the top quark by identifying the main features of the t-tbar experimental signature at the LHC.Comment: 16 pages, 9 figure