Yes.
Okay. Yeah. So I mean, demand, and also presentation, kind of touching on
different approaches across different LSE experiments. So also, due to time, it's
going to be a fairly high level, but I'll try to touch on the main things. And so
kind of why some kind of motivation why we're doing this at all, I think
sometimes, you know, in our kind of day to day life, we kind of forget how special
the Elysee is. But it's really special on the kind of data and the results that we
are extracting from this machine are pretty unique. And also the analysis that
we use to extract this results are also unique. And so both of these things, kind
of merit preservation for posterity. And so you can kind of ask yourself, like,
what is the scientific output that we have beyond the papers and you know, since
we're a kind Have a unique machines, you know, we kind of should strive to make our
results as useful as possible and, you know, also make our data available in
formats that are as useful as possible. Okay, so in this kind of area of activity,
that kind of three main directions of what's going on, there's the kind of data
like preservation of data products that we kind of extract from our analysis and high
level data products. And then there's kind of analysis preservation type stuff where
you try to preserve the analysis workflow itself. And then there's the third branch,
which is kind of more towards open data, and then, you know, you just open up the
data for researchers and people outside of the collaborations. Okay, so I'll go
through all these three things. So that data I think everybody is familiar with
type data ends really been a crucial piece of cyber infrastructure for our field.
It's basically the main destination where we kind of put a numeric, machine readable
data that relates to our publications online for people to reuse. And it's
really like a destination for high quality. But small data products, right.
So traditionally, this has been, you know, our kind of started as just a digitization
of the tables that are in the papers, but then it kind of evolved into a much wider
set of data products. And so all the LSD experiments are participating in that. And
so there are different levels of kind of percentages of the analysis that have a
corresponding data product, have to have data record, but in principle, most of the
experiments are kind of using that as a platform.
So that's good. And so as I said, we kind of started out with just having tables,
but now kind of experiments upload all types of information that relates to the
analysis there. So it kind of goes from c++ An episode, you know, spectrum files
for searches, likelihoods machine learning models and all that stuff. And so I think
heptane has been proven
to be a really crucial piece.
And so just an example of what kind of things we're covering there. So. So one of
the things that a lot of people can alter the color colorations like to do is to
kind of re implement the analysis so that they have like a fast approximate version
of the event selection procedure, for example. And so, for example, Atlas kind
of puts up c++ snippets, and kind of pseudo code. There are some times we also
have rivet analysis for the different analysis, and then they're kind of linked
on that data. And But then one thing that I want to point out is so as Ben was
saying, obviously, we all expect machine learning to be like a major component of
our analysis tool chain going forward. And so one of the things that is kind of a
little bit hard as okay If you use machine learning heavily, how do you actually put
that out there. And so, normally, you know, if you write a replica routine, you
can go through the cuts that you're doing. And so with machine learning, this becomes
a little bit trickier, but at least an Atlas. We have one example where we put
the entire machine learning model publicly available on hub data on the record that
is
associated to the analysis. And so you
have all the weights. And then if you have a simulation that is good enough to
reproduce the inputs to this machine learning model faithfully, you can use
that public model to kind of evaluate your multivariate function. And so that's
pretty good. So one of the things that I want to kind of point out in this talk,
kind of an expose nature is the kind of the public like that. So there has been
kind of a lot of attention to unlikelihood. So I just want to kind of
motivate why that is. So as far as we know, basically, what we try to do in
mobile But then also to kind of show that can we try to extract, you know,
information about the theory that we kind of assume that produces our data from the
data itself, right? And then if you kind of try to specify this inference problem,
you know, you have to like, do it, and then you have maybe your private release
over the theory. So one part is kind of the job of the theorist. And then the
likelihood is really the part that kind of summarizes what the experiments do, right?
So it's basically
quantifying how likely the data is given a theory. And
really, it's kind of like a focal point of the entire analysis chain where all the
different decisions about performance optimizations or data acquisition
operations or the you know, the analysis, choices that you do in your analysis kind
of all get reflected in this, like there's a really high information density data
product if we are able to preserve this and then all the standard inference
results like limit or
yield tables or you know, data MonteCarlo plots
are basically a result Just likelihood. So it's kind of like a bottleneck. And that
makes it really valuable to be preserved. And so oftentimes what people do outside
theorists they go to have data, right? And then they take all their, all the tables,
like the background distribution that are published on that data, and they try to
construct a likelihood out of that information. And but that's obviously
lost, because we're not like the fidelity of the information doesn't allow you to
reconstruct the likelihood. And so there's this idea of, Okay, what would happen if
we would just provide the likelihood directly on that data? Because internally
as experiments we have that. And so this is not a new ideas, it has been kind of
long in the making. So there's the first fight conference at CERN in the year 2000.
There was kind of like a discussion around this idea and more or less everybody.
Like, among this, this group of experts kind of agreed that it would be a good
idea for the OSI experiments to publish to like the function and but then There are
various technical limitations to that idea, and also social sociological
limitations. And so the first kind of step in that direction was to kind of just find
a serialization format. And so this is kind of the introduction of this route
workspace. And then in 2012, Atlas kind of published the first kind of profile
likelihood. But that's of course limited, because, okay, this is the likely
assumption, after you profiled out all numerous parameters. So that's, that's
useful, but doesn't allow you to do combinations and stuff like that. And
then, a couple years later, CMS also published a simplified likelihoods, which
kind of have the same problem. So it's very useful and kind of a simple
form of the likelihood, but it doesn't allow you to
do like actual combinations where you can vary the numerous parameters across
multiple analyses, you know, systematically So one thing that now
happened maybe half a year, or a year ago, is that we have the full, first full
likelihood release often Experiment. So this is Atlas kind of effort where we kind
of took the idea that we take a bent likelihood and then an Atlas, we use this
format called this factory, and a lot of analysis kind of use this format. And it's
nice because you can find a pretty nice schema for it. And so you can define a
data product around it. And so we have like a publicly available likelihood on
health data. And that allows external people to reproduce like the key part of
the analysis, which is this exclusion contour, which delineates,
you know, theories that are excluded or not excluded
to the exact same fidelity as
inside of the experiments, which I think is a pretty good stuff. And so this has
been a bit of a milestone and kind of open data products for the LSE. Okay, so aside
from open data products, there's also that kind of this use case of internal reuse.
So this is kind of this idea To preserve the analysis for internal, you know,
projects. And so their efforts were all of the OSI experiments to foster analysis
preservation, and then there are different ingredients that you need to do for
analysis preservation. So first of all, you know, the analysis is basically
implemented in software. So there are different software packages that need to
be preserved. And then, but only having a software is not enough, you also need to
know what to do with the software. So you need to kind of capture, like what
commands you need to run. And since your analysis is likely a multi step procedure,
you also need to capture the workflow. So that you know first you need to do the
next election, then you need to do the statistical analysis and stuff like that.
And then finally, you also need to preserve like data assets, like your
background,
in tuplets, and all that stuff.
And so Sara kind of provides some infrastructure to assist the experiments
in this effort. So there's this Viana project that basically provides like a
workflow as a service. So you can describe Your analysis like is a sequence of steps.
And then you can kind of run it on this platform.
And then there's also the cap, which stands for
certain analysis preservation portal, which basically allows you once you can
define, or once you define your analysis as like the sequence of steps as this
workflow, you can kind of take this description and then put it into the
center loss preservation framework. So that later on if you want to reuse your
analysis, you can
kind of pull it up and then
extract the workflow definition out of it, and then rerun the analysis. Okay, so how
do we do this? So the capturing of the software is basically done through
containers. So probably by now, a lot of people are familiar with this. So this is
a new technology that kind of grew out of industry to kind of package software in a
portable way. And so the sometimes kind of referred to as Docker containers,
and so this has been kind of
revolutionising how we can package the software. Kind of Part of away and has
been picked up by all of the experiments and cannabis universally seen as the
solution to this problem. And so an atlas were kind of providing,
you know, official base images, and CMS and
Alison LCB are doing something similar. And so that is largely solved even though
initially, if you think about it, you know, it's kind of sounds like the most
complicated thing to be doing. Like, you know, you need to not only preserve your
analysis software, but all the dependencies, all the root version,
Compiler version, all that stuff. And it sounds daunting, but through this
technology, it's actually almost the easiest part and this analysis reservation
problem. And then for the workflow, basically, as a sense of Rianna. It's kind
of this platform that Sam provides to run this workflow, and it kind of uses this
concept of workflow languages. So if you're familiar with continuous
integration or things like that, basically allows you to define a pipeline in a
declarative way. And so this is actually quite heavily used, also like in fields
outside of financial physics, like bioinformatics. And so it's kind of now
starting to creep in into a high energy physics and kind of allows you to go
beyond the pure software presentation, but actually preserve the full analysis
workflow so that you can actually execute the analysis and don't need to remember
what the steps are. Okay, so one of the major use cases for this type of internal
presentation reuses, the use case of reinterpretation and so this something
goes under the name of recast. So the idea is here that you have your analysis, which
might be a search. And so, you know, explore some corner face base that is
interested for interesting for some class of the onset of model, physics, and then,
okay, you probably are not finding new physics, but you might be able to set
limits on this class of models, but the space plus region that you studied, might
actually also be sensitive to a different class of models. And so the idea here is
that since you already kind of studied the space space, and you have your analysis
workflow as a tool, basically to look into the space space, you preserve this
analysis. And then once a new class of models comes around, it seems to be, you
know, forward, which this face base region seems to be sensitive, you can kind of
reuse that and then extract limits.
And so Atlas has been
pushing this and an Atlas. All the major search groups that do such a probe beyond
animal physics now require the analysis groups to preserve the analysis in this
kind of reusable way. So that, you know, maybe a year down the line, if there's a
new model, we can actually redo the analysis and then extract new limits. And
so we've kind of seen this happen actually, so that where we have kind of
new scientific results based on this pretty technical requirement, to be
honest, right, so you require people to document Images, all that stuff. But after
you go through this exercise, you actually extract new science out of it
at a cost that is much less than the cost that
is required to set up a new analysis that is dedicated for this new class of models.
And so I just like to put in three different papers or publications from
Atlas that kind of uses technology to extract new limits and based in kind of
regions of theory space that are previously uncovered, which is nice to
see. Okay, so far, so now's the presentation. So this is kind of this
portal where we can save information related to the analysis. So this is also
more meant for internal usage. So all the experiments are kind of working on kinda
integrating their internal databases, but there's so there are two screenshots for
lacp and CMS and so here the focus from so this is developed by the library section
at CERN, the same people that do like inspire and video and all these things.
And so the focus here is to kind of Make it easy for analysis teams to submit their
information, what their analysis entails and then also make it easy to discover
analyses that have specific feature. So like, ideally, it would be kind of working
in a way that you're looking for analysis that use like a specific trigger or like a
specific collection of objects, you'll be able to query this database and find
analysis that match these criteria. Okay, so the third kind of column of this topic
is the Open Data stuff. And so all the experiments have open data programs. And
here again, cern is providing infrastructure with the CERN open data
portal. And so for Atlas OCP, and Allison's mostly focus on outreach. And so
I've kind of put in some examples of the different, you know, plots that you can
make on this open data from these experiments. But CMS has a much more
expansive Open Data protocol They, where it's not only for outreach and educational
resources for research. And so what is kind of nice to see is that we actually
see an external ecosystem slowly developing around this type of open data.
And there's a workshop in October for external people to kind of learn more
about this data that I put it down the link on the slide. And so we've seen a
number of papers appear on the based on this certain open data. And so no, one
major theme there is the use of development of machine learning methods
based on this CMS open data. And so I've put down some examples here. And then
there's also kind of, well, that like a schedule that is already defined well in
advance to see where these open data release are going to be at at different
points in time. And so this kind of makes it makes it predictable and kind of allows
us ecosystem to develop outside of the experiment. Okay, so this brings me to my
conclusion. So I think The OSHA requirements are pretty strong analysis
data preservation programs. And so some of the technological progress actually helps
drive to finish off the presentation, for example, the use of containers and stuff
like that. And one important component in this entire endeavor is the availability
of cyber infrastructure for the different components. So here we have a data CERN
analysis, reservations and open data, riana recast and all these things that
kind of allowed the community to adopt these practices in a kind of systematic
way. And yeah, that's basically my
conclusion. Thanks.
Thank you very much. Just so we have time for a couple of questions. Everyone, can
you hear me? Yes, yes, please. So just
presentation and work of our own
wondering on the cast, and interpretation
what are the policy for for citing the underlying source of data and inflammation
and whatnot, if it's not already within collaborations, I see that here you have
two papers that are a justification, but then one that would be requesting is
there. So how do you do this? Give credits for the data and the experiments.
So in this example, kind of it's all done by the collaboration. So these are
reinterpretations that are performed by the collaboration right and then
internally if you read these papers, they will reference like what the input
analysis are that they use for the reading to petition, so the idea is here that you
maybe have like a. So as this gets more streamlined, you know, you might have like
a first like analysis with a benchmark model that just kind of explores the space
space. And then you'll have like a sequence of follow up publications with
references, original publication, but then kind of explore a bit more specific models
that were just face based and sensitive. If you're talking about external
reinterpretation, where it's basically okay we have an analysis then we put out
some information on health data that allows external people to reinterpret,
then like the assumption is that those excellent a range of potential site to
have data record so if you, for example, use the political or the rebut analysis
that was kind of released as part of the publication procedure, you'll need to cite
these things and the tools that you've used, but I'm done. That's enough.
Thanks a lot. I have actually a very quick question more on the data presentation
side, that side. And so I think that in the last period, we have seen more and
more groups and collaboration using derive data set that are not root based. So in
some sense, this could, at least in my experience, it could create some long term
preservation problem, of course, those would be derived data. So the argument is,
one can always go back to the original data instead of reconstructing data. But
how do you see this evolving in the long term at CERN. In particular, I'm referring
also to this effort of, of having our data frame to allow better storage formats for
people that use Python data and software. For example,
So I mean, the crucial thing is whatever format you're kind of doing long term data
preservation has like a specification and hopefully, more than one implementation of
reading the data. And so the agreement with all the experiments are so good. I
mean, you need to somehow balance. So I don't think there's going to be like a
specific open data format, and all the other experiments will agree on, but they
will know because you need to, in order to make this work at all, you need to make it
easy for these collaborations to release the data. And so it will likely be in
formats that experiments defined, but then experiments also need to release the
software to read the data, right? So an atlas case, for example, we are not using
flat tea trees, but we have like an event data model on top of that, but that's also
it's also public. And so it's the same for CMS where CMS is W is also open source.
And so as long as the software is available, To read the data, I think
you're covered whether or not separately the experiments kind of explore
alternative data formats, like our uncouple, or, you know, HDFS or something
like that, that I think is a separate discussion, but in the end open data
format will likely be what the experiments use internally. You know, whether that's
nano ad or fortnight or something like that.
Okay, thanks.
Is there any other questions for Lucas? If not, I think we can move to the last
presentation of the section given by Stetson on an island scriptural languages
for the Elysee