(Machine) Learning to Do More with Less

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail -- both analytically and numerically -- with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC.Comment: 32 pages, 12 figures. Example code is provided at https://github.com/bostdiek/PublicWeaklySupervised . v3: Version published in JHEP, discussion adde

Dark Matter from the Supersymmetric Custodial Triplet Model

The Supersymmetric Custodial Triplet Model (SCTM) adds to the particle content of the MSSM three $SU(2)_L$ triplet chiral superfields with hypercharge $Y=(0,\pm1)$. At the superpotential level the model respects a global $SU(2)_L \otimes SU(2)_R$ symmetry only broken by the Yukawa interactions. The pattern of vacuum expectation values of the neutral doublet and triplet scalar fields depends on the symmetry pattern of the Higgs soft breaking masses. We study the cases where this symmetry is maintained in the Higgs sector, and when it is broken only by the two doublets attaining different vacuum expectation values. In the former case, the symmetry is spontaneously broken down to the vectorial subgroup $SU(2)_V$ and the $\rho$ parameter is protected by the custodial symmetry. However in both situations the $\rho$ parameter is protected at tree level, allowing for light triplet scalars with large vacuum expectation values. We find that over a large range of parameter space, a light neutralino can supply the correct relic abundance of dark matter either through resonant s-channel triplet scalar funnels or well tempering of the Bino with the triplet fermions. Direct detection experiments have trouble probing these model points because the custodial symmetry suppresses the coupling of the neutralino and the $Z$ and a small Higgsino component of the neutralino suppresses the coupling with the Higgs. Likewise the annihilation cross sections for indirect detection lie below the Fermi-LAT upper bounds for the different channels.Comment: 26 pages, 8 figures; v2 revised comments on classification method and indirect detection section. Results unchanged, matches PRD published versio

Catching sparks from well-forged neutralinos

In this paper we present a new search technique for electroweakinos, the superpartners of electroweak gauge and Higgs bosons, based on final states with missing transverse energy, a photon, and a dilepton pair, $\ell^+\,\ell^- + \gamma + \displaystyle{\not} E_T$. Unlike traditional electroweakino searches, which perform best when $m_{\widetilde{\chi}^0_{2,3}} - m_{\widetilde{\chi}^0_1}, m_{\widetilde{\chi}^{\pm}} - m_{\widetilde{\chi}^0_1} > m_Z$, our search favors nearly degenerate spectra; degenerate electroweakinos typically have a larger branching ratio to photons, and the cut $m_{\ell\ell} \ll m_Z$ effectively removes on-shell Z boson backgrounds while retaining the signal. This feature makes our technique optimal for `well-tempered' scenarios, where the dark matter relic abundance is achieved with inter-electroweakino splittings of $\sim 20 - 70\,\text{GeV}$. Additionally, our strategy applies to a wider range of scenarios where the lightest neutralinos are almost degenerate, but only make up a subdominant component of the dark matter -- a spectrum we dub `well-forged'. Focusing on bino-Higgsino admixtures, we present optimal cuts and expected efficiencies for several benchmark scenarios. We find bino-Higgsino mixtures with $m_{\widetilde{\chi}^0_{2,3}} \lesssim 190\,\text{GeV}$ and $m_{\widetilde{\chi}^0_{2,3}} - m_{\widetilde{\chi}^0_1} \cong 30\,\text{GeV}$ can be uncovered after roughly $600\,\text{fb}^{-1}$ of luminosity at the 14 TeV LHC. Scenarios with lighter states require less data for discovery, while scenarios with heavier states or larger mass splittings are harder to discriminate from the background and require more data. Unlike many searches for supersymmetry, electroweakino searches are one area where the high luminosity of the next LHC run, rather than the increased energy, is crucial for discovery.Comment: Updated to published version. Reference adde, discussion of other models expanded, and typos fixed. revtex4-1, 29 pages, 9 figures, and 3 table

Detecting Subhalos in Strong Gravitational Lens Images with Image Segmentation

We develop a machine learning model to detect dark substructure (subhalos) within simulated images of strongly lensed galaxies. Using the technique of image segmentation, we turn the task of identifying subhalos into a classification problem where we label each pixel in an image as coming from the main lens, a subhalo within a binned mass range, or neither. Our network is only trained on images with a single smooth lens and either zero or one subhalo near the Einstein ring. On a test set of noiseless simulated images with a single subhalo, the network is able to locate subhalos with a mass of $10^{8} M_{\odot}$ and place them in the correct or adjacent mass bin, effectively detecting them 97% of the time. For this test set, the network detects subhalos down to masses of $10^{6} M_{\odot}$ at 61% accuracy. However, noise limits the sensitivity to light subhalo masses. With 1% noise (with this level of noise, the distribution of signal-to-noise in the image pixels approximates that of images from the Hubble Space Telescope for sources with magnitude $< 20$), a subhalo with mass $10^{8.5}M_{\odot}$ is detected 86% of the time, while subhalos with masses of $10^{8}M_{\odot}$ are only detected 38% of the time. Furthermore, the model is able to generalize to new contexts it has not been trained on, such as locating multiple subhalos with varying masses, subhalos far from the Einstein ring, or more than one large smooth lens.Comment: 5 + 3 pages, 3 figure