Search

One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be marginalized over when fitting the top-quark mass parameter. We compare three different methodologies for top-quark mass measurement: a classical histogram fitting procedure, similar to one commonly used in experiment optionally augmented with soft-drop jet grooming; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements., Comment: v1: 27 + 5 pages, 14 + 3 figures. v2: Matches version accepted to JHEP

Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. In this paper, we apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight velocities and associated uncertainties for ~92 million stars. The network, which takes as input a star's parallax, angular coordinates, and proper motions, is trained and validated on ~6.4 million stars in Gaia with complete phase-space information. The network's uncertainty on its velocity prediction is a key aspect of its design; by properly convolving these uncertainties with the inferred velocities, we obtain accurate stellar kinematic distributions. As a first science application, we use the new network-completed catalog to identify candidate stars that belong to the Milky Way's most recent major merger, Gaia-Sausage-Enceladus (GSE). We present the kinematic, energy, angular momentum, and spatial distributions of the ~450,000 GSE candidates in this sample, and also study the chemical abundances of those with cross matches to GALAH and APOGEE. The network's predictive power will only continue to improve with future Gaia data releases as the training set of stars with complete phase-space information grows. This work provides a first demonstration of how to use machine learning to exploit high-dimensional correlations on data to infer line-of-sight velocities, and offers a template for how to train, validate and apply such a neural network when complete observational data is not available., 18 pages, 11 figures

Anomaly detection with convolutional autoencoders is a popular method to search for new physics in a model-agnostic manner. These techniques are powerful, but they are still a "black box," since we do not know what high-level physical observables determine how anomalous an event is. To address this, we adapt a recently proposed technique by Faucett et al., which maps out the physical observables learned by a neural network classifier, to the case of anomaly detection. We propose two different strategies that use a small number of high-level observables to mimic the decisions made by the autoencoder on background events, one designed to directly learn the output of the autoencoder, and the other designed to learn the difference between the autoencoder's outputs on a pair of events. Despite the underlying differences in their approach, we find that both strategies have similar ordering performance as the autoencoder and independently use the same six high-level observables. From there, we compare the performance of these networks as anomaly detectors. We find that both strategies perform similarly to the autoencoder across a variety of signals, giving a nontrivial demonstration that learning to order background events transfers to ordering a variety of signal events., Comment: 17 pages, 8 figures; Corrections made for p_T, m calculations; analysis rerun with conclusions largely unchanged. Published version

We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to $10~\rm{fb}^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge., v1: 54 pages, 24 figures. v2: 56 pages, citations added, extend discussion of look-elsewhere-effect, results unchanged; v3. minor typos and updated references

Observations of structure at sub-galactic scales are crucial for probing the properties of dark matter, which is the dominant source of gravity in the universe. It will become increasingly important for future surveys to distinguish between line-of-sight halos and subhalos to avoid wrong inferences on the nature of dark matter. We reanalyze a sub-galactic structure (in lens JVAS B1938+666) that has been previously found using the gravitational imaging technique in galaxy-galaxy lensing systems. This structure has been assumed to be a satellite in the halo of the main lens galaxy. We fit the redshift of the perturber of the system as a free parameter, using the multi-plane thin-lens approximation, and find that the redshift of the perturber is $z_\mathrm{int} = 1.42\substack{+0.10 \\ -0.15}$ (with a main lens redshift of $z=0.881$). Our analysis indicates that this structure is more massive than the previous result by an order of magnitude. This constitutes the first dark perturber shown to be a line-of-sight halo with a gravitational lensing method., Comment: 15 pages, 2 tables, 15 figures. Code available at: https://github.com/acagansengul/interlopers_with_lenstronomy, v2: Modifications to analysis pipeline included, v3: Matches MNRAS published version

Massive dwarf galaxies that merge with the Milky Way on prograde orbits can be dragged into the disk plane before being completely disrupted. Such mergers can contribute to an accreted stellar disk and a dark matter disk. We present evidence for Nyx, a vast new stellar stream in the vicinity of the Sun, that may provide the first indication that such an event occurred in the Milky Way. We identify about 500 stars that have coherent radial and prograde motion in this stream using a catalog of accreted stars built by applying deep learning methods to the second Gaia data release. Nyx is concentrated within $\pm 2$ kpc of the Galactic midplane and spans the full radial range studied (6.5-9.5 kpc). The kinematics of Nyx stars are distinct from those of both the thin and thick disk. In particular, its rotational speed lags the disk by $\sim 80$ km/s and its stars follow more eccentric orbits. A small number of Nyx stars have chemical abundances or inferred ages; from these, we deduce that Nyx stars have a peak metallicity of [Fe/H] $\sim -0.5$ and ages $\sim $10-13 Gyr. Taken together with the kinematic observations, these results strongly favor the interpretation that Nyx is the remnant of a disrupted dwarf galaxy. To further justify this interpretation, we explicitly demonstrate that metal-rich, prograde streams like Nyx can be found in the disk plane of Milky Way-like galaxies using the FIRE hydrodynamic simulations. Future spectroscopic studies will be able to validate whether Nyx stars originate from a single progenitor., 8+5 pages, 4+7 figures. Nyx star sample updated with a modified probability calculation, conclusions unchanged. Stellar IDs can be found here https://linoush.github.io/nyx_stream/

Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine-learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent source magnitudes. The network is then able to resolve subhalos with masses $m\gtrsim 10^{8.5} M_{\odot}$. Training in this way allows the network to learn the gravitational lensing of light, and remarkably, it is then able to detect entire populations of substructure, even for locations further away from the Einstein ring than those used in training. Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over three mass bins from $10^9M_{\odot}$--$10^{10} M_{\odot}$ the SMF slope is recovered with an error of 36% for 50 images, and this improves to 10% for 1000 images with Hubble Space Telescope-like noise., v1:23 + 5 pages, 12 + 2 figures. v2: matches accepted version in ApJ. v3: matches version published in ApJ

The goal of this paper is to develop a machine learning model to analyze the main gravitational lens and 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 an independent test set with lenses with large ellipticities, quadrupole and octopole moments, and for source apparent magnitudes between 17-25, the area of the main lens is recovered accurately. On average, only 1.3% of the true area is missed and 1.2% of the true area is added to another part of the lens. In addition, subhalos as light as $10^{8.5}M_{\odot}$ can be detected if they lie in bright pixels along the Einstein ring. Furthermore, the model is able to generalize to new contexts it has not been trained on, such as locating multiple subhalos with varying masses or more than one large smooth lens., Comment: v1: 5 + 3 pages, 3 figures. v2: matches accepted version in A&A Letters. 5+2 pages; 3+1 figures

A new, strongly-coupled dark sector could be accessible to LHC searches now. These dark sectors consist of composites formed from constituents that are charged under the electroweak group and interact with the Higgs, but are neutral under Standard Model color. In these scenarios, the most promising target is the dark meson sector, consisting of dark vector-mesons as well as dark pions. In this paper we study dark meson production and decay at the LHC in theories that preserve a global SU(2) dark flavor symmetry. Dark pions can be pair-produced through resonant dark vector meson production, $p p\to\rho_D\to\pi_D\pi_D$, and decay in one of two distinct ways: gaugephobic, when $\pi_D\to f\bar{f}'$ generally dominates; or gaugephilic, when $\pi_D\to W+h,Z+h$ dominates once kinematically open. Unlike QCD, the decay $\pi^0_D\to\gamma\gamma$ is virtually absent due to the dark flavor symmetry. We recast a vast set of LHC searches to determine the current constraints on dark meson production and decay. When $m_{\rho_D}$ is slightly heavier than $2 m_{\pi_D}$ and $\rho_D^{\pm,0}$ kinetically mixes with the weak gauge bosons, the 8 TeV same-sign lepton search strategy sets the best bound, $m_{\pi_D}>500$ GeV. Yet, when only the $\rho^0_D$ kinetically mixes with hypercharge, we find the strongest LHC bound is $m_{\pi_D}>130$ GeV, that is only slightly better than what LEP II achieved. We find the relative insensitivity of LHC searches, especially at 13 TeV, can be blamed mainly on their penchant for high mass objects or large MET. Dedicated searches would undoubtedly yield substantially improved sensitivity. We provide a GitHub page to speed the implementation of these searches in future LHC analyses. Our findings provide a strong motivation for model-independent searches of the form $pp\to A\to B+C\to SM\, SM+SM\, SM$ where the theoretical prejudice is for SM to be a t,b,$\tau$ or W,Z,h., Comment: 45 pages, 12 figures; typos fixed, refs updated, GitHub page: https://github.com/bostdiek/HeavyDarkMesons

The discovery of the stop - the Supersymmetric partner of the top quark - is a key goal of the physics program enabled by the Large Hadron Collider. Although much of the accessible parameter space has already been probed, all current searches assume the top mass is known. This is relevant for the "stealth stop" regime, which is characterized by decay kinematics that force the final state top quark off its mass shell; such decays would contaminate the top mass measurements. We investigate the resulting bias imparted to the template method based ATLAS approach. A careful recasting of these results shows that effect can be as large as 2.0 GeV, comparable to the current quoted uncertainty on the top mass. Thus, a robust exploration of the stealth stop splinter requires the simultaneous consideration of the impact on the top mass. Additionally, we explore the robustness of the template technique, and point out a simple strategy for improving the methodology implemented for the semi-leptonic channel., 27 pages plus appendices, 20 figures; v2 improved discussion of semi-leptonic channel; v3 expanded discussion of previous related phenomenological work, matches published version

Searching for new physics in large data sets needs a balance between two competing effects---signal identification vs background distortion. In this work, we perform a systematic study of both single variable and multivariate jet tagging methods that aim for this balance. The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself. Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background. This is the first study to show that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NN and uBoost, but are both easier to implement and computationally cheaper., 25 pages, 15 figures, plus appendices. Prepared for submission to SciPost Physics

In previous work, we developed a deep neural network classifier that only relies on phase-space information to obtain a catalog of accreted stars based on the second data release of Gaia (DR2). In this paper, we apply two clustering algorithms to identify velocity substructure within this catalog. We focus on the subset of stars with line-of-sight velocity measurements that fall in the range of Galactocentric radii $r \in [6.5, 9.5]$ kpc and vertical distances $|z| < 3$ kpc. Known structures such as Gaia Enceladus and the Helmi stream are identified. The largest previously-unknown structure, Nyx, is a vast stream consisting of at least 90 stars in the region of interest. This study displays the power of the machine learning approach by not only successfully identifying known features, but also discovering new kinematic structures that may shed light on the merger history of the Milky Way., 15 + 13 pages, 9 + 15 figures. v2: Minor changes in calculating probabilities to belong to a kinematic structure. Conclusions unchanged

The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion of Gaia DR2. A method known as "transfer learning" is shown to be effective through extensive testing on a set of mock Gaia catalogs that are based on the FIRE cosmological zoom-in hydrodynamic simulations of Milky Way-mass galaxies. The machine is first trained on simulated data using only 5D kinematics as inputs and is then further trained on a cross-matched Gaia/RAVE data set, which improves sensitivity to properties of the real Milky Way. The result is a catalog that identifies around 767,000 accreted stars within Gaia DR2. This catalog can yield empirical insights into the merger history of the Milky Way and could be used to infer properties of the dark matter distribution., Comment: v1: 24 pages and 13 Figures + 4 appendices, v2: Journal version, fixed bug resulting in incorrect final catalog scores, updated plots, conclusions unchanged

Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables -- aided by physical intuition -- that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus non-linear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn., Comment: 6 pages, 3 figures. Version published in PRD, discussion added

Motivated by the recent excess in the diphoton invariant mass near 750 GeV, we explore a supersymmetric extension of the Standard Model that includes the minimal set of superpartners as well as additional Dirac partner chiral superfields in the adjoint representation for each gauge group. The bino partner pseudoscalar is identified as the 750 GeV resonance, while superpotential interactions between it and the gluino (wino) partners yield production via gluon fusion (decay to photon pairs) at one-loop. The gauginos and these additional adjoint superpartners are married by a Dirac mass and must also have Majorana masses. While a large wino partner Majorana mass is necessary to explain the excess, the gluino can be approximately Dirac-like, providing benefits consistent with being both "supersoft" (loop corrections to the scalar masses from Dirac gauginos are free of logarithmic enhancements) and "supersafe" (the experimental limits on the squark/gluino masses can be relaxed due to the reduced production rate). Consistency with the measured Standard Model-like Higgs boson mass is imposed, and a numerical exploration of the parameter space is provided. Models that can account for the diphoton excess are additionally characterized by having couplings that can remain perturbative up to very high scales, while remaining consistent with experimental constraints, the Higgs boson mass, and an electroweak scale which is not excessively fine tuned., Comment: v2: Published version. Minor updates and added references. v1: 34 pages, 5 figures

We present a complete phenomenological prospectus for thermal relic neutralinos. Including Sommerfeld enhancements to relic abundance and halo annihilation calculations, we obtain direct, indirect, and collider discovery prospects for all neutralinos with mass parameters $M_1,M_2,|��| < 4$ TeV, that freeze out to the observed dark matter abundance, with scalar superpartners decoupled. Much of the relic neutralino sector will be uncovered by the direct detection experiments Xenon1T and LZ, as well as indirect detection with CTA. We emphasize that thermal relic higgsinos will be found by next-generation direct detection experiments, so long as $M_{1,2} < 4$ TeV. Charged tracks at a 100 TeV hadron collider complement indirect searches for relic winos. Thermal relic bino-winos still evade all planned experiments, including disappearing charged-track searches. However, they can be discovered by compressed electroweakino searches at a 100 TeV collider, completing the full coverage of the relic neutralino surface., 23 pages, 8 figures

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 version

Adding a fermion to the standard model particle content which is a fiveplet under $SU(2)_L$ gives a dark matter candidate. The lightest state in the multiplet is neutral and automatically stable. The charged component of the multiplet is only slightly heavier and can travel a finite distance in the LHC detectors before decaying, leaving a charged track which disappears before the edge of the detector. We use the recent ATLAS and CMS searches to exclude a Majorana fiveplet with a mass up to 267 GeV. We estimate that with 3 ab$^{-1}$ of $\sqrt{s}=14$ TeV data this could be pushed to a mass 520 GeV. These exclusions are $\sim 10\%$ greater than what is achieved for wino-like dark matter. We also discuss how the doubly charged states could be used to distinguish a disappearing track signal from that given by a triplet such as the pure wino., Comment: 8 pages, 4 figures

In this paper we explore extensions of the Minimal Supersymmetric Standard Model involving two $SU(2)_L$ triplet chiral superfields that share a superpotential Dirac mass yet only one of which couples to the Higgs fields. This choice is motivated by recent work using two singlet superfields with the same superpotential requirements. We find that, as in the singlet case, the Higgs mass in the triplet extension can easily be raised to $125\,\text{GeV}$ without introducing large fine-tuning. For triplets that carry hypercharge, the regions of least fine tuning are characterized by small contributions to the $\mathcal T$ parameter, and light stop squarks, $m_{\tilde t_1} \sim 300-450\,\text{GeV}$; the latter is a result of the $\tan\beta$ dependence of the triplet contribution to the Higgs mass. Despite such light stop masses, these models are viable provided the stop-electroweakino spectrum is sufficiently compressed., Comment: 26 pages, 4 figures

We map the parameter space for MSSM neutralino dark matter which freezes out to the observed relic abundance, in the limit that all superpartners except the neutralinos and charginos are decoupled. In this space of relic neutralinos, we show the dominant dark matter annihilation modes, the mass splittings among the electroweakinos, direct detection rates, and collider cross-sections. The mass difference between the dark matter and the next-to-lightest neutral and charged states is typically much less than electroweak gauge boson masses. With these small mass differences, the relic neutralino surface is accessible to a future 100 TeV hadron collider, which can discover inter-neutralino mass splittings down to 1 GeV and thermal relic dark matter neutralino masses up to 1.5 TeV with a few inverse attobarns of luminosity. This coverage is a direct consequence of the increased collider energy: the Standard Model events with missing transverse momentum in the TeV range have mostly hard electroweak radiation, distinct from the soft radiation shed in compressed electroweakino decays. We exploit this kinematic feature in final states including photons and leptons, tailored to the 100 TeV collider environment., 20 pages, 11 figures, published version. Animated surfaces can be found at http://www3.nd.edu/~bostdiek/research_welltmp.html

Extending the Standard Model by adding a scalar field transforming as a septet under $SU(2)_L$ preserves the $\rho$ parameter at tree level and can satisfy experimental constraints on the electroweak parameters $S$ and $T$. This work presents the first fully general phenomenological study of such an extension. We examine constraints on the septet model couplings based on electroweak and Higgs observables, and use LHC searches for new physics to bound the mass of the septet to be above $\sim 400$ GeV at a $95\%$ CL., Comment: pdfLateX, 17 pages, 6 figures, reference added. Version published in JHEP

We study an extension of the minimal supersymmetric standard model with a zero hypercharge triplet, and the effect that such a particle has on stop decays. This model has the capability of predicting a 125.5 GeV Higgs even in the presence of light stops and it can modify the diphoton rate by means of the extra charged fermion triplet coupled to the Higgs. Working in the limit where the scalar triplet decouples, and with small values of mA, we find that the fermion triplet can greatly affect the branching ratios of the stops, even in the absence of a direct stop-triplet coupling. We compare the triplet extension with the MSSM and discuss how the additional fields affect the search for stop pair production., Comment: pdfLateX, 16 pages, 7 figures, 2 tables, Typos, minor changes. Version published in JHEP

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 tables

We study the implications at the LHC for the minimal (least) version of the supersymmetric standard model. In this model supersymmetry is broken by gravity and extra gauge interactions effects, providing a spectrum similar in several aspects to that in natural supersymmetric scenarios. Having the first two generations of sparticles partially decoupled means that any significant signal can only involve gauginos and the third family of sfermions. In practice, the signals are dominated by gluino production with subsequent decays into the stop sector. As we show, for gluino masses below 2300 GeV, a discovery at the LHC is possible at \sqrt{s}=14 TeV, but will require large integrated luminosities., Comment: pdfLatex, 12 pages, 3 figures. Typos, minor changes. Version published in Phys. Rev. D

We study the implications at the LHC for a recent class of non-custodial warped extra-dimensional models where the AdS_5 metric is modified near the infrared brane. Such models allow for TeV Kaluza-Klein excitations without conflict with electroweak precision tests. We discuss both the production of electroweak and strong Kaluza-Klein gauge bosons. As we will show, only signals involving the third generation of quarks seem to be feasible in order to probe this scenario., Comment: pdfLateX, 22 pages, 17 figures