Physics of String Flux Compactifications

Figure 1: Schematic of a gravitational-wave effect on a Michelson interferometer with mirrors as free test masses.

Figure 2: Schematic of the first-generation Laser Interferometer Gravitational-Wave Observatory detector. Abbreviation: RF, radio frequency. Modified with permission from Reference 7. Copyright IOP Pu...

Figure 3: Sensitivity of the first-generation Laser Interferometer Gravitational-Wave Observatory detector, along with the different noise sources. The black curve shows the measured strain noise, the...

Figure 4: (a) Expected strain sensitivities of second- and third-generation detectors. (b) Comparison between sensitivities of various experiments with different theoretical models of SGWB. Shown are ...

Figure 5: (a) Newtonian noise (NN) contributions estimated for the Laser Interferometer Gravitational-Wave Observatory (LIGO) Hanford site. The “noise model” curve corresponds to a potential third-gen...

Figure 1: The observed abundances of a typical low-metallicity star (HD 108317) unveil a clear r-process (and not an s-process) pattern, exactly as found in the Solar System (SS), at least for element...

Figure 2: Ratios of [Mg/Fe] (blue uncertainty range, indicating 95% of observations) and [Eu/Fe] (individual stellar observations shown as red error bars) as a function of metallicity [Fe/H] for stars...

Figure 3: (a) Abundances of neutrons (), He ( particles; ), and so-called seed nuclei () in the mass range 50 100, resulting from the charged-particle freeze-out of explosive burning, as a function o...

Figure 4: The line of stability (black circles) and the r-process path, resulting from a neutron star merger environment (30). The position of the path follows from a chemical equilibrium between neut...

Figure 5: Time derivatives (color-coded) of nuclear abundances during an r-process simulation (30), due to (a) the destruction via neutron-induced and -delayed fission (127) and (b) the production of ...

Figure 6: (a) r-Process abundances, compared with solar values (gray dots), resulting from neutron star merger simulations (30), using decay half-lives (134) together with a relatively old set (purpl...

Figure 7: The neutrino wind contribution to neutron star merger ejecta, which depends on the delay time (in milliseconds) between the merger and black hole formation (31). The dynamic ejecta from Refe...

Figure 8: r-Process abundances, compared with solar values (gray dots), resulting from black hole accretion disk simulations (51), using a black hole mass of 3 M, a disk mass of 0.03 M, an initial of...

Figure 9: Influence of the coalescence timescale and neutron star merger probability on Eu abundances in galactic chemical evolution. The magenta stars represent observations. The red dots correspond ...

Figure 1: Guido Altarelli.

Figure 2: (a) One-particle irreducible diagrams contributing to the anomalous dimension of the nonleptonic Hamiltonian. Dots indicate all other diagrams with one gluon exchanged among the external qua...

Figure 3: In a semileptonic decay, the charged lepton takes the maximum energy (endpoint) for collinear decay configurations. (a) In c decay, the endpoint corresponds to helicity 3/2. Therefore, the r...

Figure 4: Guido Altarelli making remarks during a presentation.

Figure 1: (a) Schematic hadronic (solid curves) and pure strange quark matter (dashed curves) equations of state. (b) The corresponding M−R relations. Arrows connect specific central energy density an...

Figure 2: Representative hadronic and strange quark matter equations of state (EOSs). The mean exponent γ=dlnp/dlnn≃2 holds for hadronic EOSs in the vicinity of ns=0.16 fm−3. The range of pressures at...

Figure 3: Typical M−R curves for hadronic equations of state (EOSs) (black curves) and strange quark matter (SQM) EOSs (green curves). The EOS names are given in Reference 13, and their P−n relations ...

Figure 4: The relation between the maximum mass and the central density predicted by the maximally compact equation of state (EOS) (Equation 10) is shown by the curve s = 1. Results for different clas...

Figure 5: Mass-radius contours with various values of Mmax for the maximally compact equation of state (EOS) p=ε−ε0 are shown as solid lines. Contours for the EOS p=(ε−ε0)/3 are shown as dashed lines....

Figure 6: Shapiro delay for various values of eccentricity e and periastron longitude ω, assuming an orbital inclination of i=0.495π radians.

Figure 7: Measured neutron star masses with 1-σ errors. References in parentheses following source names are identified in Table 1.

Figure 8: Histograms of neutron star masses for four groups: X-ray binaries, double–neutron star binaries, white dwarf–pulsar binaries, and white dwarf–pulsar binaries in globular clusters (GCs). Bins...

Figure 9: The density of neutron star masses, namely the number of stars per mass interval, is shown for the four groups of neutron stars displayed in Figure 8. The weighted average mass is indicated ...

Figure 10: M−R probability densities of neutron stars from (a) four quiescent low-mass X-ray binaries in globular clusters and (b) four photospheric radius expansion bursts (incorporating the possibil...

Figure 11: (a) Probability distributions for pressure as a function of energy density using the M−R probability distributions from Figure 10. (b) Probability distributions for the M−R curve. The diago...

Figure 12: Summary of constraints on symmetry energy parameters. The filled ellipsoid indicates joint Sv−L constraints from nuclear masses (114). The finite-range droplet model fit (120) is indicated ...

Figure 13: Representative hadronic and strange quark matter (SQM) equations of state, as in Figure 2 but with astrophysical results (100), neutron matter calculations (134, 135), and heavy-ion studies...

Figure 1: Schematic showing (a) feed-forward, (b) recurrent, and (c) recursive neural network architectures. Inputs and outputs are represented by diamonds, and processing units are represented by cir...

Figure 2: (a) A comparison between the performance of deep networks (DNs) in signal–background classification and that of shallow networks (NNs) with a variety of low- and high-level features demonstr...

Figure 3: Example jet image inputs from the jet substructure classification problem described in Reference 46. (a) The background jets are characterized by a large central core of deposited energy fro...

Figure 4: (a) A comparison (54) of the jet flavor–tagging performance of deep networks with varying levels of feature engineering. The lowest-level data (labeled Tracks) contain all of the information...

Figure 5: The hierarchical composition of a deep learning model following the outline of the traditional high-energy physics data pipeline. The lowest-level detector inputs are represented by xi (diam...