We use MasterCode to perform a frequentist analysis of the constraints on a phenomenological MSSM model with 11 parameters, the pMSSM11, including constraints from
∼
36
/fb of LHC data at 13 TeV and ...PICO, XENON1T and PandaX-II searches for dark matter scattering, as well as previous accelerator and astrophysical measurements, presenting fits both with and without the
(
g
-
2
)
μ
constraint. The pMSSM11 is specified by the following parameters: 3 gaugino masses
M
1
,
2
,
3
, a common mass for the first-and second-generation squarks
m
q
~
and a distinct third-generation squark mass
m
q
~
3
, a common mass for the first-and second-generation sleptons
m
ℓ
~
and a distinct third-generation slepton mass
m
τ
~
, a common trilinear mixing parameter
A
, the Higgs mixing parameter
μ
, the pseudoscalar Higgs mass
M
A
and
tan
β
. In the fit including
(
g
-
2
)
μ
, a Bino-like
χ
~
1
0
is preferred, whereas a Higgsino-like
χ
~
1
0
is mildly favoured when the
(
g
-
2
)
μ
constraint is dropped. We identify the mechanisms that operate in different regions of the pMSSM11 parameter space to bring the relic density of the lightest neutralino,
χ
~
1
0
, into the range indicated by cosmological data. In the fit including
(
g
-
2
)
μ
, coannihilations with
χ
~
2
0
and the Wino-like
χ
~
1
±
or with nearly-degenerate first- and second-generation sleptons are active, whereas coannihilations with the
χ
~
2
0
and the Higgsino-like
χ
~
1
±
or with first- and second-generation squarks may be important when the
(
g
-
2
)
μ
constraint is dropped. In the two cases, we present
χ
2
functions in two-dimensional mass planes as well as their one-dimensional profile projections and best-fit spectra. Prospects remain for discovering strongly-interacting sparticles at the LHC, in both the scenarios with and without the
(
g
-
2
)
μ
constraint, as well as for discovering electroweakly-interacting sparticles at a future linear
e
+
e
-
collider such as the ILC or CLIC.
Supersymmetric dark matter after LHC run 1 Bagnaschi, E. A.; Buchmueller, O.; Cavanaugh, R. ...
European physical journal. C, Particles and fields,
10/2015, Letnik:
75, Številka:
10
Journal Article
Recenzirano
Odprti dostop
Different mechanisms operate in various regions of the MSSM parameter space to bring the relic density of the lightest neutralino,
χ
~
1
0
, assumed here to be the lightest SUSY particle (LSP) and ...thus the dark matter (DM) particle, into the range allowed by astrophysics and cosmology. These mechanisms include coannihilation with some nearly degenerate next-to-lightest supersymmetric particle such as the lighter stau
τ
~
1
, stop
t
~
1
or chargino
χ
~
1
±
, resonant annihilation via direct-channel heavy Higgs bosons
H
/
A
, the light Higgs boson
h
or the
Z
boson, and enhanced annihilation via a larger Higgsino component of the LSP in the focus-point region. These mechanisms typically select lower-dimensional subspaces in MSSM scenarios such as the CMSSM, NUHM1, NUHM2, and pMSSM10. We analyze how future LHC and direct DM searches can complement each other in the exploration of the different DM mechanisms within these scenarios. We find that the
τ
~
1
coannihilation regions of the CMSSM, NUHM1, NUHM2 can largely be explored at the LHC via searches for
/
E
T
events and long-lived charged particles, whereas their
H
/
A
funnel, focus-point and
χ
~
1
±
coannihilation regions can largely be explored by the LZ and Darwin DM direct detection experiments. We find that the dominant DM mechanism in our pMSSM10 analysis is
χ
~
1
±
coannihilation: parts of its parameter space can be explored by the LHC, and a larger portion by future direct DM searches.
Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences ...come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility.
The pMSSM10 after LHC run 1 de Vries, K. J.; Bagnaschi, E. A.; Buchmueller, O. ...
European physical journal. C, Particles and fields,
09/2015, Letnik:
75, Številka:
9
Journal Article
Recenzirano
Odprti dostop
We present a frequentist analysis of the parameter space of the pMSSM10, in which the following ten soft SUSY-breaking parameters are specified independently at the mean scalar top mass scale
M
SUSY
...≡
m
t
~
1
m
t
~
2
: the gaugino masses
M
1
,
2
,
3
, the first-and second-generation squark masses
m
q
~
1
=
m
q
~
2
, the third-generation squark mass
m
q
~
3
, a common slepton mass
m
ℓ
~
and a common trilinear mixing parameter
A
, as well as the Higgs mixing parameter
μ
, the pseudoscalar Higgs mass
M
A
and
tan
β
, the ratio of the two Higgs vacuum expectation values. We use the MultiNest sampling algorithm with
∼
1.2
×
10
9
points to sample the pMSSM10 parameter space. A dedicated study shows that the sensitivities to strongly interacting sparticle masses of ATLAS and CMS searches for jets, leptons
+
signals depend only weakly on many of the other pMSSM10 parameters. With the aid of the Atom and Scorpion codes, we also implement the LHC searches for electroweakly interacting sparticles and light stops, so as to confront the pMSSM10 parameter space with all relevant SUSY searches. In addition, our analysis includes Higgs mass and rate measurements using the HiggsSignals code, SUSY Higgs exclusion bounds, the measurements of
BR
(
B
s
→
μ
+
μ
-
)
by LHCb and CMS, other
B
-physics observables, electroweak precision observables, the cold dark matter density and the XENON100 and LUX searches for spin-independent dark matter scattering, assuming that the cold dark matter is mainly provided by the lightest neutralino
χ
~
1
0
. We show that the pMSSM10 is able to provide a supersymmetric interpretation of
(
g
-
2
)
μ
, unlike the CMSSM, NUHM1 and NUHM2. As a result, we find (omitting Higgs rates) that the minimum
χ
2
=
20.5
with 18 degrees of freedom (d.o.f.) in the pMSSM10, corresponding to a
χ
2
probability of 30.8 %, to be compared with
χ
2
/
d
.
o
.
f
.
=
32.8
/
24
(
31.1
/
23
)
(
30.3
/
22
)
in the CMSSM (NUHM1) (NUHM2). We display the one-dimensional likelihood functions for sparticle masses, and we show that they may be significantly lighter in the pMSSM10 than in the other models, e.g., the gluino may be as light as
∼
1250
GeV
at the 68 % CL, and squarks, stops, electroweak gauginos and sleptons may be much lighter than in the CMSSM, NUHM1 and NUHM2. We discuss the discovery potential of future LHC runs,
e
+
e
-
colliders and direct detection experiments.
Abstract
The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the community model organism genetic and genome resource for the laboratory mouse. MGD is the authoritative source for ...biological reference data sets related to mouse genes, gene functions, phenotypes, and mouse models of human disease. MGD is the primary outlet for official gene, allele and mouse strain nomenclature based on the guidelines set by the International Committee on Standardized Nomenclature for Mice. In this report we describe significant enhancements to MGD, including two new graphical user interfaces: (i) the Multi Genome Viewer for exploring the genomes of multiple mouse strains and (ii) the Phenotype-Gene Expression matrix which was developed in collaboration with the Gene Expression Database (GXD) and allows researchers to compare gene expression and phenotype annotations for mouse genes. Other recent improvements include enhanced efficiency of our literature curation processes and the incorporation of Transcriptional Start Site (TSS) annotations from RIKEN’s FANTOM 5 initiative.
We report the results of a global analysis of dark matter simplified models (DMSMs) with leptophobic mediator particles of spin one, considering the cases of both vector and axial-vector interactions ...with dark matter (DM) particles and quarks. We require the DMSMs to provide all the cosmological DM density indicated by Planck and other observations, and we impose the upper limits on spin-independent and -dependent scattering from direct DM search experiments. We also impose all relevant LHC constraints from searches for monojet events and measurements of the dijet mass spectrum. We model the likelihood functions for all the constraints and combine them within the MasterCode framework, and probe the full DMSM parameter spaces by scanning over the mediator and DM masses and couplings, not fixing any of the model parameters. We find, in general, two allowed regions of the parameter spaces: one in which the mediator couplings to Standard Model (SM) and DM particles may be comparable to those in the SM and the cosmological DM density is reached via resonant annihilation, and one in which the mediator couplings to quarks are
≲
10
-
3
and DM annihilation is non-resonant. We find that the DM and mediator masses may well lie within the ranges accessible to LHC experiments. We also present predictions for spin-independent and -dependent DM scattering, and present specific results for ranges of the DM couplings that may be favoured in ultraviolet completions of the DMSMs.
Significance Whether humans make choices based on a deliberative “model-based” or a reflexive “model-free” system of behavioral control remains an ongoing topic of research. Dopamine is implicated in ...motivational drive as well as in planning future actions. Here, we demonstrate that higher presynaptic dopamine in human ventral striatum is associated with more pronounced model-based behavioral control, as well as an enhanced coding of model-based signatures in lateral prefrontal cortex and diminished coding of model-free learning signals in ventral striatum. Our study links ventral striatal presynaptic dopamine to a balance between two distinct modes of behavioral control in humans. The findings have implications for neuropsychiatric diseases associated with alterations of dopamine neurotransmission and a disrupted balance of behavioral control.
Dual system theories suggest that behavioral control is parsed between a deliberative “model-based” and a more reflexive “model-free” system. A balance of control exerted by these systems is thought to be related to dopamine neurotransmission. However, in the absence of direct measures of human dopamine, it remains unknown whether this reflects a quantitative relation with dopamine either in the striatum or other brain areas. Using a sequential decision task performed during functional magnetic resonance imaging, combined with striatal measures of dopamine using ¹⁸FDOPA positron emission tomography, we show that higher presynaptic ventral striatal dopamine levels were associated with a behavioral bias toward more model-based control. Higher presynaptic dopamine in ventral striatum was associated with greater coding of model-based signatures in lateral prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum. Thus, interindividual variability in ventral striatal presynaptic dopamine reflects a balance in the behavioral expression and the neural signatures of model-free and model-based control. Our data provide a novel perspective on how alterations in presynaptic dopamine levels might be accompanied by a disruption of behavioral control as observed in aging or neuropsychiatric diseases such as schizophrenia and addiction.
We discuss the allowed parameter spaces of supersymmetric scenarios in light of improved Higgs mass predictions provided by FeynHiggs 2.10.0. The Higgs mass predictions combine Feynman-diagrammatic ...results with a resummation of leading and subleading logarithmic corrections from the stop/top sector, which yield a significant improvement in the region of large stop masses. Scans in the pMSSM parameter space show that, for given values of the soft supersymmetry-breaking parameters, the new logarithmic contributions beyond the two-loop order implemented in FeynHiggs tend to give larger values of the light CP-even Higgs mass,
M
h
, in the region of large stop masses than previous predictions that were based on a fixed-order Feynman-diagrammatic result, though the differences are generally consistent with the previous estimates of theoretical uncertainties. We re-analyse the parameter spaces of the CMSSM, NUHM1 and NUHM2, taking into account also the constraints from CMS and LHCb measurements of
BR
(
B
s
→
μ
+
μ
-
)
and ATLAS searches for
/
E
T
events using 20/fb of LHC data at 8 TeV. Within the CMSSM, the Higgs mass constraint disfavours
tan
β
≲
10
, though not in the NUHM1 or NUHM2.
Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish ...predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches – Bayesian model selection and generative embedding – which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.
•Reviews computational neuroimaging strategies for single patient predictions.•Generative models for inferring individual disease mechanisms in psychiatry and neurology.•Mapping inferred mechanisms to clinical predictions by Bayesian model selection and•generative embedding.•Links a mechanistic model-based approach to statistical perspectives by machine learning.
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