Jet production at HERA I

This article reviews recent jet physics results from HERA

Constraining the Parameters of High-Dimensional Models with Active Learning

Constraining the parameters of physical models with $>5-10$ parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this parameter space often requires large amounts of computational resources. The commonly used solution of reducing the number of relevant physical parameters hampers the generality of the results. In this paper we show that this problem can be alleviated by the use of active learning. We illustrate this with examples from high energy physics, a field where simulations are often expensive and parameter spaces are high-dimensional. We show that the active learning techniques query-by-committee and query-by-dropout-committee allow for the identification of model points in interesting regions of high-dimensional parameter spaces (e.g. around decision boundaries). This makes it possible to constrain model parameters more efficiently than is currently done with the most common sampling algorithms and to train better performing machine learning models on the same amount of data. Code implementing the experiments in this paper can be found on GitHub

The case for 100 GeV bino dark matter: A dedicated LHC tri-lepton search

Global fit studies performed in the pMSSM and the photon excess signal originating from the Galactic Center seem to suggest compressed electroweak supersymmetric spectra with a $\sim$100 GeV bino-like dark matter particle. We find that these scenarios are not probed by traditional electroweak supersymmetry searches at the LHC. We propose to extend the ATLAS and CMS electroweak supersymmetry searches with an improved strategy for bino-like dark matter, focusing on chargino plus next-to-lightest neutralino production, with a subsequent decay into a tri-lepton final state. We explore the sensitivity for pMSSM scenarios with $\Delta m = m_{\rm NLSP} - m_{\rm LSP} \sim (5 - 50)$ GeV in the $\sqrt{s} = 14$ TeV run of the LHC. Counterintuitively, we find that the requirement of low missing transverse energy increases the sensitivity compared to the current ATLAS and CMS searches. With 300 fb$^{-1}$ of data we expect the LHC experiments to be able to discover these supersymmetric spectra with mass gaps down to $\Delta m \sim 9$ GeV for DM masses between 40 and 140 GeV. We stress the importance of a dedicated search strategy that targets precisely these favored pMSSM spectra.Comment: Published in JHE

Comparing Galactic Center MSSM dark matter solutions to the Reticulum II gamma-ray data

Observations with the Fermi Large Area Telescope (LAT) indicate a possible small photon signal originating from the dwarf galaxy Reticulum II that exceeds the expected background between 2 GeV and 10 GeV. We have investigated two specific scenarios for annihilating WIMP dark matter within the phenomenological Minimal Supersymmetric Standard Model (pMSSM) framework as a possible source for these photons. We find that the same parameter ranges in pMSSM as reported by an earlier paper to be consistent with the Galactic center excess, is also consistent with the excess observed in Reticulum II, resulting in a J-factor of $\log_{10}(J(\alpha_{int}=0.5 deg)) \simeq (20.3-20.5)^{+0.2}_{-0.3}$. This J-factor is consistent with $\log_{10}(J(\alpha_{int}=0.5 deg)) = 19.5^{+1.0}_{-0.6}$ GeV$^2$cm$^{-5}$, which is derived using an optimized spherical Jeans analysis of kinematic data obtained from the Michigan/Magellan Fiber System (M2FS).Comment: 4 pages, 2 figures, accepted in JCA

Analyzing {\gamma}-rays of the Galactic Center with Deep Learning

We present a new method to interpret the $\gamma$-ray data of our inner Galaxy as measured by the Fermi Large Area Telescope (Fermi LAT). We train and test convolutional neural networks with simulated Fermi-LAT images based on models tuned to real data. We use this method to investigate the origin of an excess emission of GeV $\gamma$-rays seen in previous studies. Interpretations of this excess include $\gamma$ rays created by the annihilation of dark matter particles and $\gamma$ rays originating from a collection of unresolved point sources, such as millisecond pulsars. Our new method allows precise measurements of the contribution and properties of an unresolved population of $\gamma$-ray point sources in the interstellar diffuse emission model.Comment: 24 pages, 11 figure

SPOT: Open Source framework for scientific data repository and interactive visualization

SPOT is an open source and free visual data analytics tool for multi-dimensional data-sets. Its web-based interface allows a quick analysis of complex data interactively. The operations on data such as aggregation and filtering are implemented. The generated charts are responsive and OpenGL supported. It follows FAIR principles to allow reuse and comparison of the published data-sets. The software also support PostgreSQL database for scalability

The BSM-AI project: SUSY-AI - Generalizing LHC limits on Supersymmetry with Machine Learning

A key research question at the Large Hadron Collider (LHC) is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: It requires the time consuming generation of scattering events, the simulation of the detector response, the event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiment. In the BSM-AI project we attack this challenge with a new approach. Machine learning tools are thought to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300,000 pMSSM model sets - each tested with 200 signal regions by ATLAS - have been used to train and validate SUSY-AI. The code is currently able to reproduce the ATLAS exclusion regions in 19 dimensions with an accuracy of at least 93 percent. It has been validated further within the constrained MSSM and a minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics. SUSY-AI can be downloaded at http://susyai.hepforge.org/. An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/