Christopher Brown: Touch on quite a few different things. So I think I'll sort of start off with some of the the hardware side of things. So Cms cms, phase 2 with the tracker and trigger. Then sort of delve into the algorithms of the trigger. So how we're going to do this track finding, and then how we're going to use them in our trigger, and then I'll also touch on some of the physics reach as well. So hopefully, there's something for for everyone within this talk.
Christopher Brown: So just to give a brief introduction to track finding and what it means for Cms. We collide protons at 40 Megahertz. And so we have thousands of charged particles in every single event. So you can see here a event display from Cms and the yellow lines of the tracks in an event. So it's a very dense environment with lots of tracking to be done. The aim of the tracking is to reconstruct the trajectories of charged particles, then also to find the vertices. So
Christopher Brown: we care about how they've moved through the detector. But then, also where they come from in the detector, because that can give us a handle. On what event happened to produce those tracks. We need high granularity pixel detectors at the very innermost parts of the detector. So that's for the vertex. Finding the materials that your tracker is made out of needs to be radiation hard.
Christopher Brown: You're looking at 10 to 15 years between the replacement. So they have to be surviving this very hard radiation environment for a long time. They need to have a low material budget. So we don't want to have lots of multiple scattering and electron Bremstraland. So we want to basically have a as thin a possible tracker as possible. And then Cms and then Atlas as well, although the experiments have large magnetic fields that allow us to see curve track trajectories, and so, therefore, we can
Christopher Brown: determine the the Pt. Or the the momentum of the particle as well.
Christopher Brown: because this is going to be a very jargon. Heavy talk. I'm going to touch on a few things that I'll probably say quite a lot just to introduce them now without having to keep on telling you what they mean. So one of the major challenges within the Nhc is the pile up. So we have simultaneous protein approach interactions, every single bunch crossing. And this is going to impact our trigger in quite a large way, and is actually one of the motivations for
Christopher Brown: completely replacing the trigger in the highly a pixel detector. I'm sure many people know, but this is a silicon set, divided into micrometer pixels, and this is the innermost detector in Cms, and it's the highest granularity. A strip detector is another silicon detector, but instead of having pixels, it has strips, which means they have good measurement in one direction. But these are cheaper and have a lower data rate. So we have them outside of the pixel detector.
Christopher Brown: Pseudo rapidity is a kind of a collider specific term. It's this angle from the beam line that we use, and it's logarithmic. Pt, which is your transverse momentum or your momentum in the R. 5 plane. And then I was going to talk about modules which is going to be your detector elements and then hit is a single point of a particles directory which is interaction detector, and then a stub is a part of a particles directory. So use that for sort of seed reconstruction, and I'll talk about that quite a lot within the track finding world.
Christopher Brown: So I'll sort of jump into the hardware side of things to start off with. I'll give a very brief introduction to Cms. I'm sure everyone has seen this diagram a thousand times. But Cms, is this multipurpose detector on the very innermost part of the detector is our silicon tracker. Within that is the pixel detector, and then you have the silicon tracker outside of that.
Christopher Brown: and that's used for reconstruction. Those charged particle trajectories. On outside of that we have an electromagnetic colorimeter. And so this is used for
Christopher Brown: well, electromagnetic Id. So electrons and photons, but then also give us some information about the Hadrons passing through. If they're charged, then we'll also have the hadron calorimeter. So this will give us the energy of the Hadrons going through on the outside of the solenoid. We then have neon detectors, which specifically are divided into lots of different types of detectors that all
Christopher Brown: tasked with with detecting beyonds, and one of the central features of Cms. Is this large magnetic field, the fortes of magnetic field that we have both the the
Christopher Brown: this, the return yoke of the magnet inside the the neon chambers that allow us to have this this doubly curved track for muons that give us very good resolution on on neon tracks. So the current Cms tracker this kind of called the phase. One tracker is divided into 2 main things. So the inner tracker is that pixel
Christopher Brown: pixel detector that I spoke about. This is the very closest to the beam line of any of the detectors, so, in fact, it was replaced in 2021, or at least the 1st layer of it was replaced in 2021, and it records particle trajectories up to an meter of 3, and then we have the outer tracker that is, then up to an meter of 2.4, with these Dublin single side modules that give you hits and etc. I think there's overall 11 layers of tracker that the particles pass through
Christopher Brown: and so that's just a picture of the the pixel detector. And then that's a picture of the outer tracker silicon detector.
Christopher Brown: In terms of our trigger system. We have a 2 stage trigger system. So we have a level, one hardware trigger that takes very low granularity information, and makes a quick decision of whether or not to keep an event. After that we read that out. If if the event is kept, that's read out to the high level trigger that then does a more complicated event, reconstruction on CPU, and then that reads out to a
Christopher Brown: offline farm. So I'll focus on the level one trigger, because that's the the main point of my expertise. The current phase. One trigger has about a 4 microsecond latency budget. It reads out 100 kilohertz. And so, taking that initial 40 Megahertz input rates, we then have lose about. Well, we have to filter out 99.7% of our data. So it has quite a significant job in trying to find those interesting events. And like stall. The noise.
Christopher Brown: The current trigger is divided into sort of 3 main parts, the calorimeter trigger, the muon trigger and the global trigger. The color trigger basically takes parts of the calorimeter, so 5 times 5 superclusters and then tries to find jets and sums within that, so has a jet. Finding algorithm, then also does overall sum so total missing transverse energy and total hydraulic energy in the detector.
Christopher Brown: But it also does electron and photon isolation. The Muon trigger. This does muon track. Reconstruction so uses all those different muon systems to try and do a full reconstruction of the muon track.
Christopher Brown: and at the moment we use a bdt for Pt. Regression. So this is one of the newer things within the Muon track finders. We use this fast machine learning. Bdt approach for doing the Pt. Regression.
Christopher Brown: Those both the current trigger and the Muon trigger then combined in the global trigger which has around 128 different algorithms to do event selection based on number of jets, amount of missing energy in the detector or muons, and that just has a single bit output that then tells the rest of the detector to read out the buffers, or to discard all the information that is kept in those buffers. One of the newer things that we have
Christopher Brown: currently in implemented in our global trigger, is this autoencoder anomaly detection. So this is a machine learning technique where you are basically looking for anomalous events amongst all your inputs. And this is one of the
Christopher Brown: interesting things that we're kind of currently developing in light of thinking about going forward to phase 2 where we're going to be very focused on doing lots of different machine learning within our sector. So this is kind of a test bench for some of the machine learning that we're going to be doing in the phase 2 trigger. And so that's a picture of the current trigger. So this, I think, is the global trigger crate. And you see that it's divided up into these boards, and each one of them hosts an Fpga that is running some part of those algorithms. And it's all connected up by fiber optic.
Christopher Brown: So looking forward. So that was the the phase one detector. So that's what's currently in in use, but looking forward to the future, which will be the high lumi electricity?
Christopher Brown: So this will happen in 2020, well commissioned in 2029 onwards, and the main feature of the highly Chc. Will be the larger data set. So at the end of Run 3, we expect to have about 300 inverse vector bands. But then over the highly Lhc lifetime, we're looking at 3,000 to 4,000 inverse vector bands. So that data set is going to be about 10 times larger. And so this is very good for rare beyond sample physics searches, and then also very good for very high precision measurements as well. One of the key things that
Christopher Brown: is going to make this happen is going to have more particles per bunch, and then a larger squeezing of those bunches per
Christopher Brown: per orbit. And what this means is that that pile up that simultaneous protein approach interaction per bunch crossing is going to go from around 60 in the current detector to about
Christopher Brown: 200 at the kind of ultimate high luminology. So the number of particles in the detector is going to go up. And so that's going to make our trigger not be able to cope. So what was found is that if we, if we were to leave the same trigger in our current detector that the rate that it would have to read out at would go up to 4. Megahertz compared to the current 110
Christopher Brown: kilohertz. And so obviously we need to do something to to rectify that there's also the the problem of radiation damage. So the current detector is.
Christopher Brown: I'd say.
Christopher Brown: feeling the effects of radiation damage given. It's the end of Run 3. We're extending it for another few months as well. So we, the current radiation damage in the the tracker specifically, and then the end caps as well is too high for us to not to continue them into the highly, so they need to be replaced. So Cms is going to be upgraded, and the the level one trigger is a significant part of that upgrade to allow us to to fight against that higher pile up that we'll be facing.
Christopher Brown: So this is kind of a an overview of all the different parts that Cms is going to be upgraded.
Christopher Brown: Basically, the entire detector is going to be upgraded in some way. The track is going to be completely replaced. And I'll go into a lot of detail about exactly how that's going to happen. We're having brand new end caps for the high. Well, this will give us a high granularity calorimeter, actually using silicon detectors for the innermost layers
Christopher Brown: compared to the current crystal version. So that's going to give us very high precision, high granularity, color of tree in the end caps. There's various different upgrades to muon systems. So new systems currently being commissioned right now will be, we added. We can have this timing layer that allows us to to hopefully filter out, pile up in, in, based on on the timing of events, and then
Christopher Brown: various new electronics for the barrel camera, which is as well. And then also the trigger is going to be completely replaced. So I'll go into the details of this as well. But this is going to for the 1st time have tracks in the trigger, and this is going to give us a very good handle on the pile up more so than we can do at the current in the current trigger that doesn't use tracks at all.
Christopher Brown: So. But yeah, I guess the the kind of underlining thing is that it's basically a new detector that we're going to have to learn how to use. And there's lots of opportunity to do interesting physics with this new detector.
Christopher Brown: So first, st I'll kind of talk about a bit about the the trigger upgrade.
Christopher Brown: As I said before, if we were to leave the same trigger in the detector at the moment, we'd have a rate of about 4 megahertz to maintain the exact same physics selectivity that we currently have. And that's unsustainable. So we're replacing the trigger. We're going to take that 110 kilohertz up to 750 kilohertz, that 4 microsecond budget is going to go up to 12 and a half microseconds, and then we're going to replace all of the the boards with new boards with bigger fpgas. And so that gives us a lot of
Christopher Brown: flexibility in the kind of physics that we can do at the trigger and allows us to to do far more complicated algorithms as well.
Christopher Brown: We'll also see upgraded
Christopher Brown: end cap and barrel caloric to electronics, which gives us a far higher granularity at level one. So instead of that, 5 by 5 super clusters, we'll be using the full granularity of the crystals in the calendar, which is so that we weren't able to do at phase one the other thing. And this is kind of the main point of the talk is, is the use of tracks from the tracker at level one. And so we're going to be doing full 40 megahertz readouts of the tracker or the outer tracker, and doing track reconstruction at that 40 megahertz to allow us to to feed into the trigger as well. This allows us to do
Christopher Brown: far more complicated algorithms, such such as particle flow. But then also vertex, finding that allows us to do levels of pile up identification that we weren't able to before. And so this is going to give us a far better handle on the pile up. That's going to be impacting our trigger decisions.
Christopher Brown: So I then go back to the phase phase one tracker, and then kind of list through some of the upgrades that are going on in the tracker, and then I'll list how how this kind of impacts the trigger as well. So this is the current schematic of the phase, one tracker. And then this is the schematic of the phase. 2 tracker. So one of the main things you can see here is that our inner tracker has gotten far larger. So there's far more disks of the inner tracker.
Christopher Brown: This gives us a far better eater coverage. So we're able to see those tracks that are going very close to beamline compared to before. But then, also, we're actually seeing fewer layers in the outer tracker. And we're using this double sided silicon design for all of our outer tracker modules, and I'll go into exactly how they work as well. So the inner tracker, the eater is going up to 4. We have smaller pixel sizes, so we have high
Christopher Brown: granularity. And then this black inner layer of the pixel is going to be a 3D. Pixel, and it's also going to be replaceable. So expect to have to replace that at least once in the high luminosity lifetime, because it is so close to the, to the actual interaction
Christopher Brown: after tracker instrumentation is now going up to an ether of 2.8, so more than the 2.4 4, we see this tilted geometry that we weren't able to implement before, and this gives us better efficiency in the kind of the transition region between the barrel and the end cap, and then we have 2 types of modules, these pixel strip modules, which are half pixel and half strip, and then also 2 strip modules as well. And so that's going to give us a better granularity in the
Christopher Brown: kind of the innermost parts of the tracker compared to before, where we just had strip detectors. So the inner tracker. So here's a zoom in of the inner tracker. It's going to be around 4,000 modules with a variety of different numbers of readout chips. So in the innermost layers going to have more readout chips because the data rate is going to be higher.
Christopher Brown: And this kind of a schematic of it all. I won't go into a huge amount of detail. There's a couple of things to back up. If if people are interested in in this kind of thing. The ass tracker is going to be divided up into these 3 main regions. Of pixel strip in the barrel, and then 2 strip in the barrel. And then there's this end, cap region that has both pixel strip and 2 strip detectors.
Christopher Brown: So one of the main features of our outer tracker is this idea of Pt modules? And this is actually going to be one of the main reasons why we can do tracking in in the level one trigger. And so what we have here is, instead of well, in every single module within the the outer tracker. We have 2 closely spare space layers of silicon within and on those layers of silicon we have a little window that is is tunable. And so
Christopher Brown: you can look for concurrent hits in both layers of the detector, those layers of the module, and if they are within a window, you know that your Pt. Of the track that passed through is going to be greater than a certain Pt. And if they're outside of the window, you know that they're going to be more curved, and so, therefore have a lower pt. And what this allows us to do is
Christopher Brown: make this on hardware cut on the momentum of the particles, and so we've designed it for a sort of a 2 Gv. Pt. Cut that says, only pass tracks of a Pt. Greater than 2 Gv. To the downstream trigger. And what this does is, it massively reduces the number of tracks that are going to the trigger, and so, therefore the trigger has a chance of being able to reconstruct them all. This is something that has really allowed us to do, tracking at the level one trigger.
Christopher Brown: and something that every single module within the art tracker will be able to do so. The difference between the kind of the pixel strip and the strip strip. The pixel strip is this sort of lower layer is a set of pixel detectors, and then on the top you have a strip detector, and then the strip strip is just 2 strip detectors next to each other. But one of the issues that you might notice here is that typically in strip detectors what you will do is have
Christopher Brown: 2 strip detectors, and then you'll offset them slightly. This idea of stereo strips, and what that allows you to do is give you precise exit or precise
Christopher Brown: coordinate resolution along the strip which you can't normally do on a single strip. But obviously in this this pt. Module design, you have to have your strips aligned with each other, and so you have you by design, have very poor resolution in Z. 0. Because of this parallel sets of strips. And this motivates using this pixel strip module. So at the bottom, you have the pixels that have
Christopher Brown: high resolution in both directions, and this allows you to give some level of better Z. 0 resolution in the entire art tracker, and it was kind of designed for around 1 Z. 0 resolution. And if you think about the number of interactions in a 200 pile up event, this will allow us to to separate out pileup events within the bunch. And so, yeah, this is the kind of the reason
Christopher Brown: why the pixel strip were chosen over, strip stripping across the entire detector.
Christopher Brown: So this is kind of what the Ps module will look like. That kind of
Christopher Brown: long in one direction and then shorter in the other. One pixel, one strip there, and in the barrel region there'll be
Christopher Brown: tilted towards the interaction point, and this allows us to have better efficiency when we have this transition region between the end cap and the barrel. The 2 strip detectors are sort of roughly squarish, with a high resolution in Anita, but poor resolution in Z. But it was decided that this doesn't usually matter in the track finding case because we have that better resolution in the inner layers.
Christopher Brown: And because we expect to have lower occupancy and low bandwidth. That's why they've they've been using the outer layer.
Christopher Brown: We could have instrumented the entire detector in these pixel strip detectors, but the cost would have been too high. And so the the decision was to to have this kind of this difference in the 2 different types of modules. So kind of talking about the readout system. Now, some more hardware. So the way that the detector will be read out is that you'll have the entire detector divided up into Nons, which I guess
Christopher Brown: 1 9th of the detector all the way around. These are read out by a detector and trigger control board at Dtc. This is then read out into the the track finder process board, and then that, then distributes tracks into the the detectors sorry into the rest of the level. One trigger, and you're looking at a few 100 tracks per event with a pt. Of 2 Gv, and that's being built out of
Christopher Brown: hits a detector around 10,000. So there's quite a lot of processing that goes on in that track finder processor. And each of these has a sort of different readout board Cms is, it's got a variety of different boards that will be doing this kind of detector readout. So we take information from the modules. This is read out into a board called the Serenity.
Christopher Brown: Then they're read out into an Apollo board, and then it's read out into the trigger, which has 2 different types of boards to do the actual processing of the data for the level and trigger. A couple of words on the trigger atca boards. This is kind of another part of the hardware story of all this. All we have sort of various different groups working on different hardware standards and and different boards. But the general idea is that we have this Atca standard with
Christopher Brown: very large Fpgas mounted onto these boards. So this is one of the boards. It's the the Apollo board that will be running the actual track finder. Algorithm. It has this dual Fpga design with the input and output happening in in fiber optic.
Christopher Brown: The dual Fpga design allows us to to have a reasonably complicated track finder algorithm, that is going to be split between the 2 fpgas and then read out into to the the trigger system
Christopher Brown: and then. So on the left. I have just the the picture of the actual Fpgas themselves on on what we call the command module, and then this is plugged in into this other Service module, which actually holds all the the power, the the clocking the board control, and then a CPU for for being able to to deal with the reprogramming of the board. And then underneath that heat sink is is the Epjs themselves.
Christopher Brown: This is then? Well.
Christopher Brown: this is read out into the trigger, which uses another set of different Atta boards. So the main 2 here we have here is is serenity and Apx. Serenity is developed in the Uk and Germany by various colleagues, and then in the Apx. That's developed in the Us. By Wisconsin, and then they have sort of different roles within the trigger. Some systems will divide their algorithm equally between the 2 types of board, and then other algorithms will sort of divide them in different ways.
Christopher Brown: But the kind of the the overall message of these boards is that single Fpj. Very large chips have on board CPU for sort of board
Christopher Brown: control and reprogramming. They have Ipmc, which is a kind of a standard that cerns developing for dealing with the board, turning it off and on again, things like that using firefly optics that give us very high data rates going up to sort of 30 terabits per second, or sorry, 63 terabits per second on each board, and then the entire trigger system will have around
Christopher Brown: 250 boards. So it's a very big project with a lot of moving parts. And at the moment we're moving into the the pre-production and production of these boards in readiness for the actual trigger. Turn on in the 2030 s.
Christopher Brown: so another thing just to mention is that all of these boards will be housed in these kind of large crates which have sort of
Christopher Brown: typical extra boards that that deal with the rack and then also the the kind of
Christopher Brown: the clocking of the boards as well. So we have this dth that deals with common clocking for the boards in a rack, and Ethernet switch for for rack management. So it'd be able to turn off and control each of the boards. And then we're also developing online software for board control and then monitoring the boards themselves, and then understanding
Christopher Brown: what boards are working and reprogramming and things like this. So there's a lot of moving parts when it comes to the entire trigger project. So just highlight a few few interesting things. I think that's everything I have on hardware. So now I'll move on to the actual trigger algorithms themselves. So how do we go from 10,000 stubs in the chat to a trigger decision? And why? We well, how can we use these tracks in interesting ways.
Christopher Brown: So
Christopher Brown: as I've introduced there's a lot of different constraints to our tracking problem. We have this 200 pile up at 40 megahertz to to process. And you can imagine that if you look at the event display on the the right, imagine this but twice as dense. And that's exactly kind of the the level of of pile up that Cms going to be
Christopher Brown: going to be impacted. So the other thing that we have to worry about is the overall latency of the trigger. So I said before that we have this 12 and a half microsecond budget for for trigger decisions, and that's dictated by the the size of the buffers on on the trigger balls themselves so or sorry on the the front end. Electronics of the detector, so we can only store so many events. So we have
Christopher Brown: 12 and a half microseconds to to decide whether or not we want that event, and of that 12 and a half microseconds, about 5 microseconds will be receiving the data and then also producing the tracks. So we're very tight in budget for the track finding, but as a total amount of the the overall trigger latency, the track has quite a large amount of time. One of the key things is, we need to be able to reconstruct a vertex with our tracks.
Christopher Brown: This is very key for pile up. If we can find the primary interaction and event, we can cut out all the other pileup events, and then actually trigger on that primary interaction rather than any of the pile up. So, finding that vertex is very important, and we need to be able to pass well reconstructed interesting tracks to downstream algorithms. So we can do more complicated reconstructions like particle, flow, and pile up for particle identification as well.
Christopher Brown: And how are we going to to deal with these constraints.
Christopher Brown: to be able to deal with that high level of pile up at 40. Megahertz, the the algorithm itself is going to be paralyzed. So both in in time and space. So in kind of in spatially. We're going to be dividing detector up into to 9 nons. And so each of them is is
Christopher Brown: processed in parallel. And then we have multiple bores, in every single nonent processing a given
Christopher Brown: chunk of data. So you can receive a new event in one board while the other ones. Processing the previous event. And so we, we deal with that level of data flow with this kind of parallelization. This 5 microseconds is divided up into to one microsecond of stub processing, so receiving the stubs from the detector, and then divide them out into the the non, and then sending them out into the the boards that are doing the processing. And this is a big routing task that needs to be done.
Christopher Brown: And then we have about 4 microseconds of track finding and so that's taking the stubs and then outputting
Christopher Brown: nice, clean, well reconstructed tracks to reconstruct a vertex. We need about a millimeter track. Resolution in Z. 0 to be able to unambiguously divide up a 30 cm beam line into about 200 events. So that's kind of the level of track resolution we need in Z. 0. But then we also need well reconstructed eta welcome to Phi, and then also.
Christopher Brown: the the momentum of the track, but then it'll also be nice to have some level of quality
Christopher Brown: flag to pass downstream as well. So there's quite a lot of tasks that the track finding needs to be able to do. There's just a few wants that we'd like. It'd be nice to have high and flat efficiency for all tracks across that iter region that we have. It would be nice if we didn't have dips in the efficiency at certain regions. So it's important to make sure that the algorithm works at all parts of the detector, we need to have a low fake rate. So part of tracking is that you will
Christopher Brown: create these tracks that don't actually come from real hits in the detector. Because of the large combinatorics. You can accidentally fit a line through some points that don't actually come from a real track. So we need to have a low fake rate. The algorithm needs to be robust to a breaking detector. One of the real parts of running this detector is that parts of it will break, or parts of it will switch off
Christopher Brown: or degrade over time. So we need to be robust to that and have a an algorithm that is able to to cope one of the the ones that is going to be very useful for the the level and trigger is this displaced track finding so having a another parameter where you know how far away from the the beam line your your track has come from, and being able to fit to that is going to be very important for
Christopher Brown: things like Bj. Tagging, or or general tagging. Then it's also useful for for a lot of different physics signatures that might have that display signature. So display track finding is a is very much a want of the true community, but is also very difficult to to realize in in hardware.
Christopher Brown: So how are we going to do the track finding I'm going to talk about just the prompt track finding here. So this is just taking those tracks and fitting them to coming from the center of the detector. I won't talk about the space track finding, which is a completely different algorithm. But I'll speak about this one. And we call it this hybrid algorithm, and it has to reconstruct all the tracks in the detector with a Pt. Greater than 2 Gv. Which was our
Christopher Brown: cut on hardware, and then they have to have an eta less than 2.4, because that's the instrumentation of the detector.
Christopher Brown: We have 4 microseconds to process around 10,000 stubs, and we form around 100 tracks per event. A lot of those stubs will be discarded because they will be a Pt. That isn't compatible. That hardware cut on the actual modules themselves isn't going to be 100. So a lot of those stubs are lost in the processing step, and you don't expect around 6 stubs per track. So a lot
Christopher Brown: them are lost just to not actually meeting that Pt. Of 2 Gv. Cut. But if we weren't to have that cut, we'd have a lot more than 10,000 stubs to process. The algorithm itself is divided up into 3 main parts. The 1st one is this row search algorithm that basically looks for
Christopher Brown: track seeds within the large number of hits in the detector. The second part is to use a karma filter to take those track seeds and then find the best stop candidates for a given track, but then also fit those track parameters to a high level of resolution. And then the final part is to then evaluate the track quality. And so to do this, we use the boosted decision tree machine learning technique to basically look at a track and try and work out if it came from a real real particle or not.
Christopher Brown: So I'll divide a sort of dig into each of those sections, the 1st one being the trackers algorithm. So this is a this, this track seeding step. And in the 1st step you'll take 2 stubs in the detector, and you'll basically draw a line through them and say, that's my initial track seed. And there's lots of different combinations of both the barrel and end cap layers. So in parts
Christopher Brown: different parts of the detector we have different seeding layers to say, Look at these layers and look for 2
Christopher Brown: hits that look like they will produce a track, and we produce lots of these different little tract seeds. These are then projected outwards. So we say, we know we can guess what kind of pt, that that little stub is. That tract is, and we project them out outwards, based on a beam spot constraint. So I guess, is whether the prompt track finding comes in. We say, well, we know it came from the beam spot, and so therefore create this
Christopher Brown: window, going outwards into the rest of the layers, and you create this little search window to look for more stubs that will be compatible with that track. We then add those matching stubs to that track candidate, and this is based on the
Christopher Brown: stubs that have the smallest residual. So the ones that are as close to the expected line as we. We'd like them to be. And we we have about a minimum of 4 stubs to produce a track, and then a maximum of 6, because we only have 6 detector layers.
Christopher Brown: to to deal with. So you can't have more than that and then the step 4 is to to basically produce that track candidate, and that's passed on to a track merger and then carbon filter. The track merger is looking for tracks that are basically the same. So so this will happen where you you accidentally produce the same track
Christopher Brown: multiple times. And you basically choose the one that is the best residuals. And then there's also additional things we have to worry about because of this non approach. We actually have overlap regions between every single processing. So you also need to be able to attempt to reduce them as well the camel filter. So this takes those those track candidates to produce a camel filter state
Christopher Brown: and then use the track residuals from the previous step to also produce a covariance matrix. We then iteratively add each of the the stop candidates to that track, each layer at a time and update the state
Christopher Brown: using the camera filter framework. This State propagation then estimates what the track will look like in the next layer, and then you add your your new track candidate or your new stub to that track candidate updates the intensity and it'll improve the track. State iteratively, and it'll also remove any of the tracks which have incompatible stubs. So it'll find those tracks that
Christopher Brown: weren't good enough to be propagated out through the filter, and the track fit iteratively improves, as you. You add, a stub in each letter, and this produces well reconstructed high quality tracks with good parameter residuals as well. And so this is kind of borne out when you look at the actual performance of the track finder. So the plot on the left is the
Christopher Brown: efficiency of track finding across iter. So you can see that we have a high efficiency for finding tracks above 95% for tracks greater than Pt. Of 2 Gv. And a little bit higher for those greater than 8 Gv.
Christopher Brown: You can see there's a little bit of a dip in this kind of transition region. So around each of one. And that's just because it's it's a bit more difficult to fit tracks when you have a transition between the barrel and the end cap layers
Christopher Brown: on the right is the the Z 0 resolution. So I said that
Christopher Brown: for vertexing to be viable, we need this 1 track resolution. And you can kind of see in the barrel region we we reach that with this point 1 cm. So 1 at 0 resolution. But this gets worse into the end cap regions because of just the the
Christopher Brown: the geometry of the the detector. Because you have these different end cap region end cap layers and they'll have different precision of of
Christopher Brown: different precision of of the the eta, variable as you look at the track as it passes through.
Christopher Brown: But this is kind of expected. And there's not much we can do about it, but it is good enough for the the vertex finding and I'll talk a bit about the vertex finding and and what it does with this kind of this
Christopher Brown: this image of having worse resolution in these regions. But the vertex finding is good enough for well, this this resolution is good enough for the vertex finding. So one of the things I mentioned is this idea of a fake rate. So we have these these tracks that will not come from genuine charged particles.
Christopher Brown: and these are an issue for downstream algorithms. So if we're expecting our downstream algorithms to to do things like calculating the total number of total amount of missing energy in the detector. If you had a single extra high Pt. Track
Christopher Brown: that didn't exist because of the fake rate. This will reduce your efficiency and things like missing transverse energy at the moment we use well, we can use the Chi-squared fit parameters. So pass the current filters to output this uncertainty in the track parameters, and we can use them to reduce the fakes. This boss on the right basically shows you the number of fakes per as Pt increases.
Christopher Brown: And you can see at sort of high Pts about 10% of our tracks going to be fake. So this is a particularly
Christopher Brown: large effect. So we need to do something about it. And so yeah, those those chi-squared fit parameters work well enough, but there is a complex dependent of the dependence of these Chi-squares in in different E 2 and Z. 0 regions. So
Christopher Brown: having a single cut on Chi-squared isn't going to really be good enough to to maintain that high track, finding efficiency across iter. And so just doing a simple cut isn't going to be good enough. And so what if we just used a machine learning technique to find an optimal cut in many dimensions? And so this is what a boost decision tree does. It's finding an optimal set of cuts for every track in a number of different parameters. So Chi squared is in there. So the number of stubs are in there and then.
Christopher Brown: Different features of the track that we we might think are going to impact its quality are fed into this this decision tree and you can see on the bottom the right that it's track finding efficiency versus non genuine fraction. So fake rates is better than just the Chi squared cuts by itself. One of the nice things is we actually have a a sliding threshold that we can pass downstream. So instead of just sending
Christopher Brown: just removing the the tracks, we can send a quality variable to downstream algorithms. And they can decide how
Christopher Brown: types they want to to cut on quality of tracks.
Christopher Brown: Something else that is part of this machine learning framework is the idea that you could retrain and retune this algorithm. As your track finding evolves. So maybe you, you improve your track. Finding incrementally, you can retrain this Bdt and immediately redeploy it. Or maybe your detector degrades in some way, and your track finding isn't producing the same quality of tracks that you're expecting. You can update your Bdt. And then you'll be able to to maintain that same level of
Christopher Brown: fake rejections you had before. So it's 1 of the nice features of using machine learning models in this kind of scenario. So
Christopher Brown: we have these tracks. We have several 100 tracks coming out of the trigger. Sorry coming out the track finder. Now the question is, what do we do with them? And what's the best way of of utilizing them to to improve our trigger efficiency. So there's lots of different places where the the tracks are used and all kind of
Christopher Brown: take you through the pathways that they're used in. The 1st one is this global track trigger. So what this is is a event level quality that uses just tracks. So it takes all of the tracks and events, and it looks for event level qualities that we can, we can determine from just tracks alone. So one of the ones is vertex finding. So something I mentioned quite a lot. The idea of vertex finding can be done with just tracks. We also do things like track based missing
Christopher Brown: transverse energy and track based jets. These are not so good as the full color match version of them, but they're still quite useful.
Christopher Brown: And then you also look at exotic decays.
Christopher Brown: so we do. A w to 3 pi
Christopher Brown: exotic decay chain looking for just 3 pines coming out of the tracks. So those are also quite interesting as well. So vertex finding something I mentioned before, and we'll dig into now because it was something I directly worked on. The vertex finding is a pretty simple algorithm. You basically take all the tracks. You then
Christopher Brown: weight them by pt, and you then pass a a sliding window across that histogram of of Pt. Weighted. Sorry you wait. You histogram them in Z. 0, weighted by their pt, so you have this sort of histogram of of
Christopher Brown: peaks of Pt, and you basically want to find the highest Pt. Peak. You don't want to be sensitive to single track things. So you run this 3 bin sliding window to kind of smooth out that histogram. You then find the the Z 0 peak of that that histogram, and then you do a tractor vertic association based on eta. So at higher it is. You have a larger
Christopher Brown: value that you cut on about larger distance away from the vertex you cut on. So that's how we take into account that loss in said 0 resolution is the tracker in in need from the tracker? And so this is a a working algorithm very nice. But there's a few things that we could improve and we we did so. And
Christopher Brown: one of the things we looked at was basically replacing this with again, a neural network, a machine learning approach. So instead of using Pt, we learn a perfect weighting for all the tracks that take into account multiple track parameters. And so something you can do here is take into account each of the tracks. So you know that your highest tracks have worse Z. 0 resolution. So you might want to weight them lower
Christopher Brown: than a low eater barrel region. Very well defined track.
Christopher Brown: And so this Dnn learns how to do that. Instead of just smoothing out with a 3 bin sliding window, we use a Cnn to basically try and find more complicated looking peaks in the tape in the histogram. You can do the same kind of Z 0 Peak finder. But then
Christopher Brown: we also then use a Dnn. For classifying primary vertex tracks. So being able to associate your track to your vertex in a more complicated way than just saying, Is it X centimeters away from the vertex, and this gives us a better efficiency for finding those primary vertex tracks.
Christopher Brown: We do this in one big neural network. And one of the nice things is that you can kind of see the impact of this. So the plot on the top is is showing you the Z 0 resolution. Sorry. The primary vertex residual, which we drop because of this improved vertex finding performance. And then the plot on the bottom is the receiver receiver operating characteristic curve for the character Vertex Association, and you can see that this blue, blue, green line is is better than the red point, showing that we're able to more
Christopher Brown: accurately associates tracks to the vertex than before. And this is really important for being able to cut out pile up in the downstream algorithms. So talking about cutting out pile up in downstream algorithms. One of the other things that the well, I'll come on to that. One of the other things that the track finder does is then pass its tracks to the new one finders and so
Christopher Brown: what we can do here is is link tracks and muon objects together. And you can just do this based on E to 5 cuts. And what we see here is is basically better muon reconstruction. We have sharper turn on, so the bottom the top shows you a sharper turn on for being able to to
Christopher Brown: trigger on on neons. But then you also have a lower rate. So you're able to more effectively find these these Muons and cut out the the, perhaps the fake ones that you've produced as well at the moment we have 2 algorithms where I have a track to muon object linking, or we have a track to Muon Stub. Also, just
Christopher Brown: Muon hits in the detector linking. And this is a bit more effective in overlap regions. So we have both these algorithms in parallel. And we'll kind of decide which one's the better based on regions of the detector. But I guess the underlying story is that the Muon detection is is good. We can make it even better with the cracks in the trigger.
Christopher Brown: One of the more complicated parts of the level one trigger is this bit in the middle, so this is the correlated trigger. So this is also taking tracks. But then it's also taking information from the entire detector. And the name of these layers is basically to do particle, flow and so particle flow is an algorithm that links objects from multiple subsystems. And it basically allows you to take advantage of information from the track finder to improve your object reconstruction in the color images, so the color images will have
Christopher Brown: not the best energy reconstruction. And so using information from the full detector.
Christopher Brown: When you reconstruct those particles in the detector, you can improve your energy efficiency, sorry energy, reconstruction. And this is an argument we can now do in the trigger, because we have the tracker available to us as well, and what we can also do with those sort of reconstructed objects is then to look at weighting them, based on how far away from the primary vertex they are. And this is the idea
Christopher Brown: of pile up per particle identification, or the puppy algorithm that looks to try and remove tracks that we know don't come from a primary vertex. And so you can kind of see this on the right. So you go step one is, you have this primary vertex and pilot vertex, both impacting a jet. For example, you then try and remove the charged particles, because, you know, they don't come from the primary vertex anymore, because you have that vertex that makes your jet resolution better. But then you can also weight the neutral particles because you don't have the track.
Christopher Brown: but you can weight them based on how far away they were from that vertex. You can wait them lower because they're further away from the vertex. And then, as you can see, you improve your jet reconstruction because you don't have the impact of that other vertex that was near it. One of the other things that is nice is this also reduces the number of candidates. So if you're able to reduce the number of particle flow candidates for downstream algorithms. You can do more complicated algorithms. And so track finding is a big part of doing that particle flow.
Christopher Brown: One of the other things we can we do in this correlated trigger is then track match, electron Id. So, using the track finder with the calorimeter in tow to be able to do electron identification. And again, we use that decision tree to improve this performance. But this is something that we are able to do now. So rather than just using calorimeter information for electron gamma classification, we can then also now do track match electron Id, which gives us a better efficiency.
Christopher Brown: The second part of our correlated trigger is a
Christopher Brown: allows us to do more complicated algorithms with those particle flow counters. So this will be doing jet reconstruction. They'll also be doing sort of things like et miscalculations. And then e gamma isolation and tile tagging.
Christopher Brown: It has better reconstruction because we're able to to remove the pile up. So this plot on the right basically shows you the reconstruction of 2 jets in the event based on a, a, an algorithm that we have and these jets and then can be sent to the the global trigger for the final decision based on the menu that we have in the global trigger.
Christopher Brown: and what's nice about this layer is that because we've been able to reduce the number of particle candidates going in with the puppy and particle flow we able are now able to do more complicated jet tagging. So something that I've been working on recently is the idea of neural networks for jet tagging. And so understanding jet origin is obviously very useful for the downstream global trigger. Because then we can have event specific taggers, so you can look for
Christopher Brown: Die Higgs to 4 Bs if you can tag all 4 B jets. And so B jets are one of the things we we tag or we focused on to start with. But then we've also looked at tile taggers. So being able to identify tiles more efficiently based on lots of different information, and the future is to use just a single model that does all jet tagging. This is kind of akin to
Christopher Brown: what we do offline. So at the moment we have very complicated neural networks for for offline, jet tagging and Cms, where what we're trying to do here is trying to take some of that
Christopher Brown: expertise and put it into our trigger itself. So you can do some level of of jet tagging at the Cms. Trigger means that we keep more data. And then we can do more interesting things later, when we do offline analysis. So even a poorly, not very good jet Tagger will
Christopher Brown: impact our overall data sets more than having a if we have a great offline Jet Tagger, and no information to to give it. And no data that we stored. It's not gonna do so well. But if we we have a jet tagger, even a bad one in the love on trigger.
Christopher Brown: we store more data and have more data for that offline analysis to look at. So it's important to have these kind of complicated algorithms in our in our trigger. So that was kind of a whistle. Stop tour of some of the stuff that our triggers doing.
Christopher Brown: how does that impact physics. This will be reasonably short, because, I am not someone who focused on physics. I'm more of a trigger outcome person. But this will hopefully give some of the flavors of the kind of things that we can now do. With Cms and hope to do with Cms going forward into the highly era. So one of the 1st things we'll be able to do with this better tracker is
Christopher Brown: because of the better granularity. We have better 2 track separation. So this plot just shows you in red versus black. The red is our new track, the phase 2 tracker. This will have better 2 track separation which gives us better efficiency for jet tagging and jet finding in offline. And so obviously, this is this is great for a lot of different physics.
Christopher Brown: One of the other things we'll see is because of that better granularity. We'll see better resolution and track parameters. So you can see in both cases the red line is below the black line, because it has better resolution on on things like Pt. And displacement for the the for the offline tracking
Christopher Brown: and you also see that there is better coverage. So there's regions of the phase space that we're now able to reconstruct tracks, and that we weren't able to before. And this is obviously great for various different physics, and especially for business physics where we have high as well.
Christopher Brown: This is a plot of complicated plot. Showing the Bj efficiency. And you can see that the Bj efficiency is gonna be it's gonna be very good.
Christopher Brown: Because of that improved displace track displace tracking resolution. And this kind of drops off at high eaters. But these high eater regions we didn't have a tool in phase one so this is kind of brand new information that we have for the offline analyses going forwards in in phase 2.
Christopher Brown: One of the things that we also will see is because of the tracker. Complementing our
Christopher Brown: high granular limiter. We have a far increased eta coverage for things like Vbf reconstruction, and so comparing kind of the the
Christopher Brown: red red circles, blue triangles and green dots as green squares compared to the kind of the lines current. Sorry compared to the the black
Christopher Brown: crosses. That's phase 2 versus phase one. And you can see that we have this far higher efficiency at Hiita because of this complementary increase in both the tracker eater, and then also the the end cat calorieta as well. And so Vbf is going to be improved by by this. So this thing very nice for for physics analysis as well.
Christopher Brown: and so kind of a final thing here is just to show you some preliminary
Christopher Brown: projections for Cms going forwards. The plot on the well, the kind of the plots on the left are showing you the Higgs coupling projections at the end of Run 3. We have this level of precision on these various different parameters, or these various different couplings, and you can see that going from run 3 to highly see that massive increase in statistics is going to give us far better.
Christopher Brown: far, far better.
Christopher Brown: precision on those those parameters. So it's obviously going to be very good for that kind of physics. And then one of the key measurements that the high Lumi is looking for is this di Higgs observation because of the high data set and then improved triggering on on various different
Christopher Brown: various different events, we'll be able to improve the or we'll be able to actually see that observation of the Diehighs production in Nhc. And so this kind of why, why we're doing this. And all of the work we we focus on in developing trigger algorithms is obviously to to try and boost this kind of performance. So, to conclude, I hope I've
Christopher Brown: motivated that upgrading the tracker is essential for Cms, not only because of the radiation damage, but also so we can use it in the level. One trigger. That tracking algorithm within the level one trigger is is nearing its completion, but it also allows us to do things like particle flow, and then part
Christopher Brown: pile pile up per particle identification at level one. And this is going to give us far better object reconstruction, and then also do almost event well up to event level reconstruction in our level. One trigger, something that we've never been able to before. Throughout that trigger we're going to be using new machine learning techniques to be able to boost performance. And I've hopefully shown you a couple of the flavor of kind of the things that we're going to be able to do. And
Christopher Brown: there is a big program of improving those kind of techniques across our trigger. And I guess it's up to the physicists to start thinking of what analyses and measurements we make. I guess it's up to the trigger people to try and make that data set as good as possible and as big as possible.
Christopher Brown: So that wraps up everything I have. I welcome any questions.