5 research outputs found
Fast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniques
A pressing problem for supersymmetry (SUSY) phenomenologists is how to
incorporate Large Hadron Collider search results into parameter fits designed
to measure or constrain the SUSY parameters. Owing to the computational expense
of fully simulating lots of points in a generic SUSY space to aid the
calculation of the likelihoods, the limits published by experimental
collaborations are frequently interpreted in slices of reduced parameter
spaces. For example, both ATLAS and CMS have presented results in the
Constrained Minimal Supersymmetric Model (CMSSM) by fixing two of four
parameters, and generating a coarse grid in the remaining two. We demonstrate
that by generating a grid in the full space of the CMSSM, one can interpolate
between the output of an LHC detector simulation using machine learning
techniques, thus obtaining a superfast likelihood calculator for LHC-based SUSY
parameter fits. We further investigate how much training data is required to
obtain usable results, finding that approximately 2000 points are required in
the CMSSM to get likelihood predictions to an accuracy of a few per cent. The
techniques presented here provide a general approach for adding LHC event rate
data to SUSY fitting algorithms, and can easily be used to explore other
candidate physics models.Comment: 20 pages, 7 figures, replaced to correct author contact detail
(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
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/
Should we still believe in constrained supersymmetry?
We calculate Bayes factors to quantify how the feasibility of the constrained
minimal supersymmetric standard model (CMSSM) has changed in the light of a
series of observations. This is done in the Bayesian spirit where probability
reflects a degree of belief in a proposition and Bayes' theorem tells us how to
update it after acquiring new information. Our experimental baseline is the
approximate knowledge that was available before LEP, and our comparison model
is the Standard Model with a simple dark matter candidate. To quantify the
amount by which experiments have altered our relative belief in the CMSSM since
the baseline data we compute the Bayes factors that arise from learning in
sequence the LEP Higgs constraints, the XENON100 dark matter constraints, the
2011 LHC supersymmetry search results, and the early 2012 LHC Higgs search
results. We find that LEP and the LHC strongly shatter our trust in the CMSSM
(with and below 2 TeV), reducing its posterior odds by a factor
of approximately two orders of magnitude. This reduction is largely due to
substantial Occam factors induced by the LEP and LHC Higgs searches.Comment: 38 pages, 14 figures; version as published in EPJ