Category: Susy in the conformal world

Susy in the conformal world

In particle physicssupersymmetry SUSY is a conjectured relationship between two basic classes of elementary particles : bosonswhich have an integer-valued spinand fermionswhich have a half-integer spin.

A supersymmetrical extension to the Standard Model could resolve major hierarchy problems within gauge theoryby guaranteeing that quadratic divergences of all orders will cancel out in perturbation theory. In supersymmetry, each particle from one group would have an associated particle in the other, known as its superpartnerthe spin of which differs by a half-integer.

These superpartners would be new and undiscovered particles; for example, there would be a particle called a "selectron" superpartner electrona bosonic partner of the electron. In the simplest supersymmetry theories, with perfectly " unbroken " supersymmetry, each pair of superpartners would share the same mass and internal quantum numbers besides spin.

Since we expect to find these "superpartners" using present-day equipment, if supersymmetry exists then it consists of a spontaneously broken symmetryallowing superpartners to differ in mass.

Where in the World are SUSY and WIMPs?

There is no experimental evidence at this time that supersymmetry is correct, or whether or not other extensions to current models might be more accurate.

In part, this is because it is only since around that particle accelerators specifically designed to study physics beyond the Standard Model have become operational i. The main reasons for supersymmetry being supported by some physicists is that the current theories are known to be incomplete and their limitations are well established, and supersymmetry could be an attractive solution to some of the major concerns.

Direct confirmation would entail production of superpartners in collider experiments, such as the Large Hadron Collider LHC. The first runs of the LHC found no previously-unknown particles other than the Higgs boson which was already suspected to exist as part of the Standard Modeland therefore no evidence for supersymmetry.

Indirect methods include the search for a permanent electric dipole moment EDM in the known Standard Model particles, which can arise when the Standard Model particle interacts with the supersymmetric particles. Such EDM experiments are also much more scalable than conventional particle accelerators and offer a practical alternative to detecting physics beyond the standard model as accelerator experiments become increasingly costly and complicated to maintain.

These findings disappointed many physicists, who believed that supersymmetry and other theories relying upon it were by far the most promising theories for "new" physics, and had hoped for signs of unexpected results from these runs. To reconcile the lack of experimental evidence for SUSY, some researchers suggest that the string theory landscape could have a power law statistical pull on soft SUSY breaking terms to large values depending on the number of hidden sector SUSY breaking fields contributing to the soft terms.

Light higgsino pair production in association with hard initial state jet radiation leads to a soft opposite-sign dilepton plus jet plus missing transverse energy signal. There are numerous phenomenological motivations for supersymmetry close to the electroweak scale, as well as technical motivations for supersymmetry at any scale. Supersymmetry close to the electroweak scale ameliorates the hierarchy problem that afflicts the Standard Model. The observed hierarchy between the electroweak scale and the Planck scale must be achieved with extraordinary fine tuning.

In a supersymmetric theoryon the other hand, Planck-scale quantum corrections cancel between partners and superpartners owing to a minus sign associated with fermionic loops. The hierarchy between the electroweak scale and the Planck scale is achieved in a natural manner, without miraculous fine-tuning.

The idea that the gauge symmetry groups unify at high-energy is called Grand unification theory. In the Standard Model, however, the weakstrong and electromagnetic couplings fail to unify at high energy. In a supersymmetry theory, the running of the gauge couplings are modified, and precise high-energy unification of the gauge couplings is achieved.

The modified running also provides a natural mechanism for radiative electroweak symmetry breaking. TeV-scale supersymmetry augmented with a discrete symmetry typically provides a candidate dark matter particle at a mass scale consistent with thermal relic abundance calculations.

Supersymmetry is also motivated by solutions to several theoretical problems, for generally providing many desirable mathematical properties, and for ensuring sensible behavior at high energies.

Supersymmetric quantum field theory is often much easier to analyze, as many more problems become mathematically tractable. When supersymmetry is imposed as a local symmetry, Einstein's theory of general relativity is included automatically, and the result is said to be a theory of supergravity.

It is also a necessary feature of the most popular candidate for a theory of everythingsuperstring theoryand a SUSY theory could explain the issue of cosmological inflation.

susy in the conformal world

Another theoretically appealing property of supersymmetry is that it offers the only "loophole" to the Coleman—Mandula theoremwhich prohibits spacetime and internal symmetries from being combined in any nontrivial way, for quantum field theories like the Standard Model with very general assumptions.

A supersymmetry relating mesons and baryons was first proposed, in the context of hadronic physics, by Hironari Miyazawa in This supersymmetry did not involve spacetime, that is, it concerned internal symmetry, and was broken badly. Miyazawa's work was largely ignored at the time.

Gervais and B.In particle physicssupersymmetry SUSY is a conjectured relationship between two basic classes of elementary particles : bosonswhich have an integer-valued spinand fermionswhich have a half-integer spin. A supersymmetrical extension to the Standard Model could resolve major hierarchy problems within gauge theoryby guaranteeing that quadratic divergences of all orders will cancel out in perturbation theory.

In supersymmetry, each particle from one group would have an associated particle in the other, known as its superpartnerthe spin of which differs by a half-integer. These superpartners would be new and undiscovered particles; for example, there would be a particle called a "selectron" superpartner electrona bosonic partner of the electron.

In the simplest supersymmetry theories, with perfectly " unbroken " supersymmetry, each pair of superpartners would share the same mass and internal quantum numbers besides spin. Since we expect to find these "superpartners" using present-day equipment, if supersymmetry exists then it consists of a spontaneously broken symmetryallowing superpartners to differ in mass.

There is no experimental evidence at this time that supersymmetry is correct, or whether or not other extensions to current models might be more accurate. In part, this is because it is only since around that particle accelerators specifically designed to study physics beyond the Standard Model have become operational i. The main reasons for supersymmetry being supported by some physicists is that the current theories are known to be incomplete and their limitations are well established, and supersymmetry could be an attractive solution to some of the major concerns.

Direct confirmation would entail production of superpartners in collider experiments, such as the Large Hadron Collider LHC. The first runs of the LHC found no previously-unknown particles other than the Higgs boson which was already suspected to exist as part of the Standard Modeland therefore no evidence for supersymmetry.

Indirect methods include the search for a permanent electric dipole moment EDM in the known Standard Model particles, which can arise when the Standard Model particle interacts with the supersymmetric particles.

Such EDM experiments are also much more scalable than conventional particle accelerators and offer a practical alternative to detecting physics beyond the standard model as accelerator experiments become increasingly costly and complicated to maintain. These findings disappointed many physicists, who believed that supersymmetry and other theories relying upon it were by far the most promising theories for "new" physics, and had hoped for signs of unexpected results from these runs.

To reconcile the lack of experimental evidence for SUSY, some researchers suggest that the string theory landscape could have a power law statistical pull on soft SUSY breaking terms to large values depending on the number of hidden sector SUSY breaking fields contributing to the soft terms.

Light higgsino pair production in association with hard initial state jet radiation leads to a soft opposite-sign dilepton plus jet plus missing transverse energy signal.

There are numerous phenomenological motivations for supersymmetry close to the electroweak scale, as well as technical motivations for supersymmetry at any scale. Supersymmetry close to the electroweak scale ameliorates the hierarchy problem that afflicts the Standard Model. The observed hierarchy between the electroweak scale and the Planck scale must be achieved with extraordinary fine tuning.

In a supersymmetric theoryon the other hand, Planck-scale quantum corrections cancel between partners and superpartners owing to a minus sign associated with fermionic loops. The hierarchy between the electroweak scale and the Planck scale is achieved in a natural manner, without miraculous fine-tuning.

The idea that the gauge symmetry groups unify at high-energy is called Grand unification theory. In the Standard Model, however, the weakstrong and electromagnetic couplings fail to unify at high energy. In a supersymmetry theory, the running of the gauge couplings are modified, and precise high-energy unification of the gauge couplings is achieved.

The modified running also provides a natural mechanism for radiative electroweak symmetry breaking.

TeV-scale supersymmetry augmented with a discrete symmetry typically provides a candidate dark matter particle at a mass scale consistent with thermal relic abundance calculations. Supersymmetry is also motivated by solutions to several theoretical problems, for generally providing many desirable mathematical properties, and for ensuring sensible behavior at high energies. Supersymmetric quantum field theory is often much easier to analyze, as many more problems become mathematically tractable.

susy in the conformal world

When supersymmetry is imposed as a local symmetry, Einstein's theory of general relativity is included automatically, and the result is said to be a theory of supergravity. It is also a necessary feature of the most popular candidate for a theory of everythingsuperstring theoryand a SUSY theory could explain the issue of cosmological inflation. Another theoretically appealing property of supersymmetry is that it offers the only "loophole" to the Coleman—Mandula theoremwhich prohibits spacetime and internal symmetries from being combined in any nontrivial way, for quantum field theories like the Standard Model with very general assumptions.

A supersymmetry relating mesons and baryons was first proposed, in the context of hadronic physics, by Hironari Miyazawa in This supersymmetry did not involve spacetime, that is, it concerned internal symmetry, and was broken badly. Miyazawa's work was largely ignored at the time.

susy in the conformal world

Gervais and B. Sakita in[27] Yu.Much of it was the same material about how split SUSY is the best idea still standing. Unfortunately, at the end he seems to now have changed his mind and be arguing that the best thing for theorists to do is to keep tweaking the models that failed at the LHC:.

That is a perfectly decent attitude to take, but I would like to at least tell you that you should study some of the history of physics. These are two different attitudes towards connecting theory and experiment. If you take the more top-down attitude, just keep fixing things a little bit. If you had to pick the single most influential theorist out there on these issues, it would probably be Arkani-Hamed.

This kind of refusal to face reality is I think a significant factor in what has caused Sabine Hossenfelder to go on her anti-new-collider campaign.

I have followed your blog since its beginning along with reading your book. In your many posts you discussed in detail what was wrong with both string theory and supersymmetry. Although I could not judge the correctness of your explanations in detail, what I found most convincing was that you encouraged critical comments in your forum and I never saw a serious or convincing rebuttal of your arguments.

It should be emphasized, that you stated this opinion long before the experimental results were available that now seem to support you. I do not think the hep community gives you the appropriate credit. Not only were you brave enough to stick your neck out, but, apparently, you were right.

One is a great facility with the mathematical formalism, etc. The other is a physical intuition that gave you the confidence to shout out that the emperor had no clothes. The fact that these brilliant physicists are not willing to give up these ideas even in the face of experimental evidence is just more confirmation of how strong a lure the mathematical ideas must be, along with their lack of the appropriate physical intuition.

First of all, skepticism about SUSY and string theory has always been fairly widespread among theorists. A sizable fraction of theorists never have worked on SUSY or string theory and for most such physicists, one reason was skepticism that these highly speculative models were all that promising.

All I can take credit for is being more vocal and obnoxious with my skepticism than most. In his talk, he does a good job of locating the start of all this trouble in the GUT hypothesis. Yes, this kind of refusal is the reason why theorists in these fields have not come up with useful predictions for decades. It illustrates that the self-correction in this community is not working. The absence of reliable predictions for new discoveries at the next larger collider means that such a machine is presently not a promising investment.Renormalization Group.

Another project started in Rome 3 years ago came to an end. I was an outsider invited to give a review talk about the conformal bootstrap, but I did listen to the other talks.

Many of them were about the Parisi-Sourlas supersymmetry and dimensional reductionor rather why they are absent, in the Random Field Ising and other related models.

After a quick look, it became clear that th is is interesting both from CFT and R enormalization Group perspectives, and that something could be done complementary to the existing treatments. The first paper devoted to supersymmetric CFT aspects appeared in Decemberand the second paperabout the RG aspects, is finally out in September The RG paper is much longer and more subtle. Complexity stems from the fact that two out of three potentially destablizing perturbation classes susy-writable, non-susy-writable, and susy-null cannot even be written in the SUSY variables.

See the paper for full details. Now that the conformal bootstrap is well and running, I feel a bit sorry for the non-perturbative RG. Majority of high-energy physics researchers view its most popular current incarnation Functional Renormalization Group with suspect, as a bunch of unjustified approximations and truncations. My point of view is that Wilson was right, the strongly coupled fixed points exist in a true mathematical sense, and the problem is that we simply haven't found yet a numerically stable implementation of Wilson's vision.

Where to start? Mathematicians proved many theorems using the renormalization group, so they must know how to control it rigorously.

Supersymmetry

It would seem natural to turn to them for inspiration, but the mathematical RG papers look so daunting See my talk at a Princeton workshop about the dream to understand better the rigorous RG and how to make it useful for practical calculations.

This dream started to come true in this paperwritten in collaboration with two remarkable mathematical physicists, Alessandro Giuliani and Vieri Mastropietro. Alessandro and I met in at a conference at the Accademia dei Lincei Rome. Once Alessandro told me that he can control rigorously Wilsonian RG for fermions, I got excited about the possibility to use his techniques to construct the simplest non-gaussian fermionic fixed point, in a model of symplectic fermions with a long-range kinetic term and a quartic interaction a fermionic analogue of the bosonic long-range model I studied earlier with Connor Behan, Leonardo Rastelli and Bernardo Zan.

susy in the conformal world

It was clear from the start to Alessandro and Vieri that this construction should be possible. The final version of our paper implements instead a Banach contraction argument, arguably more "Wilsonian" in spirit while the tree construction is in an appendix and in retrospect it does look simpler.

I am quite proud of this paper. We have a nice result, written in a self-contained way. It will certainly stimulate further work, e. It would be nice to have a similar pedagogical exposition for bosonic fixed points. Finally, talking about dreams, it would be nice to have a computer-assisted construction of the fixed point of the 3d Ising model. This would be similar to how my late IHES colleague Oscar Lanford constructed the fixed point for the Feigenbaum period doubling back in I have some ideas in this direction.

My bootstrap Zoom seminar about this paper.You can read more about random decision forests here. Each tree modifies the predictions of the previously grown tree. You must specify the boosting argument in order to apply this technique. You can also list all of your ensembles.

Click here to find more information about weights. Example: 128 description optional String A description of the ensemble up to 8192 characters long. It can contain a rate (default 1), and replacement (default true), and seed parameters. This argument can be used to change the names of the fields in the models of the ensemble with respect to the original names in the dataset.

It can also be used to tell BigML that certain fields should be preferred. All the fields in the dataset Specifies the fields to be included as predictors in the models of the ensemble.

Example: flase name optional String,default is dataset's name The name you want to give to the new ensemble. This parameter is ignored for boosted trees. See the Gradient Boosting section for more information. Example: "000003" ordering optional Integer,default is 0 (deterministic).

Specifies the type of ordering followed to build the models of the ensemble. There are three different types that you can specify: 0 Deterministic 1 Linear 2 Random For more information, see the Section on Shuffling. See the Section on Random Decision Forests for further details.

The range of successive instances to build the models of the ensemble. It doesn't apply to boosted trees. Example: 16 tags optional Array of Strings A list of strings that help classify and retrieve the ensemble. If you do not specify a range of instances, the complete set of instances in the dataset will be used. If you do not specify any input fields, all the preferred input fields in the dataset will be included, and if you do not specify an objective field, the last field in your dataset will be considered the objective field.

Note that when gradient boosting option is applied to classification models, the actual number of models created will be a product of the number of classes (categories) and the iterations.

Supersymmetry

For example, if you set boosting iterations to 12 and the number of classes is 3, then the number of models created will be 36 or less depending on whether an early stopping strategy is used or not. Individual trees in the boosted trees differ from trees in bagged or random forest ensembles. Primarily the difference is that boosted trees do not try to predict the objective field directly. Instead, they try to fit a gradient (correcting for mistakes made in previous iterations), and this will be stored under a new field, named gradient.

This means the predictions from boosted trees cannot be combined with using the regular ensemble combiners.A time series model needs to be trained with time series data, i. BigML implements exponential smoothing to train time series models. Time series data is modeled as a level component and it can optionally include a trend (damped or not damped) and a seasonality components as explained below:Forecast equation Level equation Forecast equation Level equation Trend equation Forecast equation Level equation Damped trend equation Forecast equation Level equation Trend equation Seasonality equation The different components can have variations, e.

As a result of combining the different variations for each component, several models can be trained for a given objective field. Note that BigML excludes certain combinations for numerical stability reasons such as additive errors with multiplicative trends or multiplicative error and trend with additive seasonality.

BigML computes four different performance measures to select the best model for a given objective field. You can create a time series model selecting one or several fields from your dataset to use as objective fields to forecast their future values. You can also list all of your time series. This can be used to change the names of the fields in the time series with respect to the original names in the dataset or to tell BigML that certain fields should be preferred.

Example: 100 name optional String,default is dataset's name The name you want to give to the new time series. The type of the field must be numerical. Non-numeric fields will be ignored, and if not present, the right-most valid field in the dataset will be used. The period needs to be set taking into account the time interval of your instances and the seasonal frequency.

For example, for monthly data and annual seasonality, the period should be 12, for daily data and weekly seasonality, the period should be 7. It can take values from 0 to 60. If the period is set to 1, there is no seasonality. If the period is 0, or not given, BigML will automatically learn the period in your data. The range of successive instances to build the time series. Multiplicative seasonality models are only available when the objective field has strictly positive values (greater than 0).

Example: 2 tags optional Array of Strings A list of strings that help classify and index your time series. If absent, the first datetime field in the dataset whose values are continuously either decreasing or increasing. If not given, the server will auto-detect the first sequential datetime field in the dataset. All fields are optional: giving any two among start, end, and interval are enough for a full specification, since the remaining one can always be computed.

If you give all three, end is ignored and recomputed using start and interval. After the initial pass through the input data, the value of end will be adjusted to coincide with the last non-missing objective value. If the objective field has missing values at its tail, then this adjusted value will differ from the one specified or computed from start and interval.

If you do not specify an objective field, BigML. Once a time series has been successfully created it will have the following properties. Each field's id has a list of objects with the following properties: The property forecast is a dictionary keyed by each field's id in the source. Each field's id has a list of objects with the following properties: In addition to the ETS models, BigML also provides simple forecast models for each field, to be used as references for the performance of the ETS models.

Due to their trivial nature, these are always computed regardless of what ETS parameters are selected in the input. Currently, we offer three simple model types: naive, mean, and drift. Naive: this model always forecasts the last value of the observed time series. For seasonal models, it repeats the last m values of the training series, where "m" is the given period length for the field.

The parameters for this field are as follows: Mean: this model always forecasts the mean of the objective field. For seasonal models, it is similar to the naive model since the model cycles the same sequence of values for forecasts, but instead of using the last set of m values, BigML computes the mean sequence of the naive values. The parameters for this field are as follows: Drift: Draws a straight line between the first and last values of the training series.

Forecasts are performed by extending that line. The parameters for this field are as follows: Creating a time series is a process that can take just a few seconds or a few days depending on the size of the dataset used as input and on the workload of BigML's systems.Other folk travelling with Nordic on our trip had similar good tales to tell. Loved the mobile phone and GPS that we had as well as the fuel discount cards. I think that the hotel selections were great as all were centrally located and upscale.

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