How Much Information is in a Jet?

Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics has typically employed image recognition, natural language processing, or other algorithms that have been extensively developed in computer science. While these studies have demonstrated impressive discrimination power, often exceeding that of widely-used observables, they have been formulated in a non-constructive manner and it is not clear what additional information the machines are learning. In this paper, we study machine learning for jet physics constructively, expressing all of the information in a jet onto sets of observables that completely and minimally span N-body phase space. For concreteness, we study the application of machine learning for discrimination of boosted, hadronic decays of Z bosons from jets initiated by QCD processes. Our results demonstrate that the information in a jet that is useful for discrimination power of QCD jets from Z bosons is saturated by only considering observables that are sensitive to 4-body (8 dimensional) phase space.Comment: 14 pages + appendices, 10 figures; v2: JHEP version, updated neural network, included deeper network and boosted decision tree result

Automating the Construction of Jet Observables with Machine Learning

Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic final states. Techniques with simple analytic forms are particularly useful for establishing robustness and gaining physical insight. We introduce a procedure to automate the construction of a large class of observables that are chosen to completely specify $M$-body phase space. The procedure is validated on the task of distinguishing $H\rightarrow b\bar{b}$ from $g\rightarrow b\bar{b}$, where $M=3$ and previous brute-force approaches to construct an optimal product observable for the $M$-body phase space have established the baseline performance. We then use the new method to design tailored observables for the boosted $Z'$ search, where $M=4$ and brute-force methods are intractable. The new classifiers outperform standard $2$-prong tagging observables, illustrating the power of the new optimization method for improving searches and measurement at the LHC and beyond.Comment: 15 pages, 8 tables, 12 figure

Structural studies of novel bismuth containing piezoelectric ceramics

Perovskite-based materials are in the focus of research not only because of their excellent physical properties, but also because their relatively simple structure facilitates the understanding of structure-property relationships, which is crucial for developing novel materials with improved qualities. Recent research in the field of ferroelectric and piezoelectric materials is concerned with the development of eco-friendly lead-free materials. To achieve this goal, it is important to understand the fundamental correlation between the ‘Structure’ and the ‘Property’. In this work, the primary focus has been to elucidate the structural changes occurring as a function of doping in three different systems: (1) BiScO3-PbTiO3 (BS-PT), a recently developed system which has already attracted much interest because of its superior physical properties near the morphotropic phase boundary (MPB); (2) BiScO3-BaTiO3 (BS-BT), which can be considered as a lead-free analogue of the BS-PT family and lastly, (3) Na0.5Bi0.5TiO3-BaTiO3 (NBT-BT), which is a well-known lead-free material at the NBT-rich side of the phase diagram. Powder samples with a range of compositions for each system were prepared following the solid-state synthesis route and were investigated utilizing both neutron and x-ray powder diffraction and dielectric measurements. Detailed crystallographic information was obtained by Rietveld refinement against the neutron powder diffraction data. Structural phase transitions as a function of temperature were determined by nonambient x-ray powder diffraction and compared with the physical properties of the ceramics using high-temperature dielectric measurements. The significant outcomes are: 1. The best model to represent the so-called MPB of xBS-(1-x)PT system is found to be a mixture of a tetragonal and a monoclinic phases from the powder diffraction data. The structure beyond the MPB compositions is in better agreement for a single monoclinic model with the space group Cm than the accepted space group R3m. By contrast, single crystals with compositions around the MPB provide evidence for a model consisting of two primitive monoclinic cells. 2. The lead-free BS-BT system exhibits an extended phase boundary between tetragonal and pseudocubic phases, which can be modelled by a combination of tetragonal and rhombohedral phases. The incorporation of BS into BT also results in the suppression of the two low-temperature phase transitions of BT. 3. Samples with new compositions synthesized in the xNBT-(1-x)BT system demonstrate a rare enhancement in the tetragonality of the unit cell and an increase in the Curie temperature for compositions where x <= 0.40

LHC analysis-specific datasets with Generative Adversarial Networks

Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kind of generative model is analysis specific in the sense that it directly generates the high-level features used in the last stage of a given physics analyses, learning the N-dimensional distribution of relevant features in the context of a specific analysis selection. We apply this idea to the generation of muon four-momenta in $Z \to \mu\mu$ events at the LHC. We highlight how use-case specific issues emerge when the distributions of the considered quantities exhibit particular features. We show how substantial performance improvements and convergence speed-up can be obtained by including regression terms in the loss function of the generator. We develop an objective criterion to assess the geenrator performance in a quantitative way. With further development, a generalization of this approach could substantially reduce the needed amount of centrally produced fully simulated events in large particle physics experiments.Comment: 14 pages, 11 figure

Characterization of Mtg2p, a mitochondrial member of the Obg family of GTP -binding protein in <italic>Saccharomyces cerevisiae</italic>.

The Obg subfamily of GTPases is a novel family of GTP-binding proteins that are universally conserved and predicted to play a role in translation. Interestingly, all Obg proteins studied to date are involved in some aspect of ribosome function. In Chapters 2 and 3 of this study I demonstrate that the Saccharomyces cerevisiae GTPase, Mtg2p (Yhr168wp), is essential for mitochondrial ribosome function. Cells lacking MTG2 lose their mitochondrial DNA, giving rise to respiratory incompetent cells. In addition, cells expressing a temperature sensitive mgt2-1 allele are defective in mitochondrial protein synthesis and contain lower levels of mitochondrial ribosomal subunits. Significantly, elevated levels of Mtg2p partially suppress the thermosensitive loss of mitochondrial DNA in a 21S rRNA methyltransferase mutant, mrm2. Consistent with a role for Mtg2p in mitochondrial ribosome biogenesis, I show that Mtg2p is peripherally localized to the mitochondrial inner membrane and associates with the 54S large ribosomal subunit in a salt-dependent manner. The Obg subfamily of bacterial GTP-binding proteins are biochemically distinct from Ras-like proteins raising the possibility that they are not controlled by conventional guanine nucleotide exchange factors (GEFs) and/or guanine nucleotide activating proteins (GAPs). To test this hypothesis, in Chapter 4 we generated mutations in the Caulobacter crescentus obg gene, (cgtAC) which, in Ras-like proteins, would result in either activating or dominant negative phenotypes. I purified CgtACS173N (analogous to RasS17N) and CgtACP168V (analogous to RasG12V) protein and measured their guanine nucleotide affinities, exchange rates and half life of hydrolysis. In C. crescentus, a P168V mutant is not activating in vivo, although in vitro, the P168V protein showed a modest reduction in the affinity for GDP. Neither the S173N nor N280Y mutations resulted in a dominant negative phenotype. Further, the S173N was significantly impaired for GTP binding, consistent with a critical role of this residue in GTP binding. In general, conserved amino acids in the GTP-binding pocket were, however, important for function.Ph.D.Biological SciencesCellular biologyMicrobiologyMolecular biologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/125055/2/3186608.pd

Novel jet observables from machine learning

Abstract Previous studies have demonstrated the utility and applicability of machine learning techniques to jet physics. In this paper, we construct new observables for the discrimination of jets from different originating particles exclusively from information identified by the machine. The approach we propose is to first organize information in the jet by resolved phase space and determine the effective N -body phase space at which discrimination power saturates. This then allows for the construction of a discrimination observable from the N -body phase space coordinates. A general form of this observable can be expressed with numerous parameters that are chosen so that the observable maximizes the signal vs. background likelihood. Here, we illustrate this technique applied to discrimination of H → b b ¯ $$H\to b\overline{b}$$ decays from massive g → b b ¯ $$g\to b\overline{b}$$ splittings. We show that for a simple parametrization, we can construct an observable that has discrimination power comparable to, or better than, widely-used observables motivated from theory considerations. For the case of jets on which modified mass-drop tagger grooming is applied, the observable that the machine learns is essentially the angle of the dominant gluon emission off of the b b ¯ $$b\overline{b}$$ pair