WEBVTT 1 00:00:00.570 --> 00:00:02.650 Stefania Juks: Nicola, if you're ready. 2 00:00:03.100 --> 00:00:04.929 Nicola Nicassio: Yes, sir. Can you hear me? 3 00:00:05.210 --> 00:00:05.830 Stefania Juks: Yes. 4 00:00:06.530 --> 00:00:07.460 Nicola Nicassio: Okay. 5 00:00:10.670 --> 00:00:32.189 Nicola Nicassio: okay, so let's start. Good morning to everyone. I'm Nicola Incasio from the Elis collaboration. And this contribution will discuss some results on our studies on performing electron identification, using a possibly eco-friendly gas mixture for the future of barrel-rich detector at the Lhc, so I will provide an outline on the specification of this detector, then I will focus on 6 00:00:32.360 --> 00:00:42.619 Nicola Nicassio: and this possibility to achieve an A plus or minus identification announcement using S, and finally, I will show you some projections for the achievable reconstruction and physics performance. 7 00:00:42.960 --> 00:01:12.549 Nicola Nicassio: So this detector is in the context of this upgrade. We are planning for our apparatus. So basically, the main goal of our collaboration is to address the dynamics of the strongly interacting matter produced in aviation collisions. But despite the huge planned physics program up to the end of round. 4 fundamental questions will remain open questions demanding for excellent vertex in tracking particle identification that now is out of reach, so demanding for a next generation. Experiment. 8 00:01:12.630 --> 00:01:31.340 Nicola Nicassio: This is what we call the Ls. 3 upgrade. Here you can see a schematic of the state of the art detector concept with all its subsystems, and among them, for the identification of charged particles, we have a ring imaging Cherenkov detector covering the barrel region. This is the main focus of this presentation. 9 00:01:31.640 --> 00:01:57.839 Nicola Nicassio: Here you see also a schematic of the state of the art concept for this barrel ridge. So it's a proximity focusing reach using aerogel as Cherenkov radiator, separated by an expansion gap from a photosensitive surface equipped with silicon photo multipliers, and each of these modules is part of this projective geometry, with modules oriented toward the nominal collision vertex, and with the 10 00:01:57.930 --> 00:02:20.229 Nicola Nicassio: aerial sites optimized to achieve full coverage to the impinging charged particles without overlaps with such a baseline configuration we are able to achieve some target particle identification requirements among which the electron pion separation, larger than 3 sigma up to 2 gb, that is the baseline. For this detector. 11 00:02:20.550 --> 00:02:31.209 Nicola Nicassio: however, some physics, observables, including Quarkonia, the electrons would benefit a lot from extending these electro identification capabilities above 2 Gv. 12 00:02:31.230 --> 00:02:57.760 Nicola Nicassio: Up to, let's say, 4 Gb. Or more in principle. And so Theresa dedicated line on this possibility the strategy we are planning to adopt without making any modification in the detector geometry consists in filling the Reach expansion Gap. The rich vessel, basically with a proper gas with a target refractive index larger than this value to perform a Cherenkov threshold-based discrimination. 13 00:02:57.760 --> 00:03:05.670 Nicola Nicassio: So the idea is that electrons are above the threshold for charge of emission. Actually they are also at saturation at a very small 14 00:03:05.870 --> 00:03:34.150 Nicola Nicassio: moment, actually, and the pions, muons, and particles are below this threshold, so that the identification of clusters from gas can be used to univocally label electrons. In this context the refractive index is crucial for the mixture we plan to use, because a low index like this one is good because means high emission threshold for heavy particle species, but at the same time a limited photon yield and cluster size. 15 00:03:34.150 --> 00:04:00.390 Nicola Nicassio: and from dedicated simulation studies we see that the optimal, refractive index we plan to achieve is of the order of 1.0 0 6. So how do we get it? Well, 1st of all, in order to address a proper mixture, we have to rule out some options, such as saturated fluorocarbons, like cf. 4 c. 4 f. 10 that are commonly used in currently 16 00:04:00.390 --> 00:04:28.500 Nicola Nicassio: ring imaging Cherkov systems. But the fish are very large, global warming potential thousands of times, the one of Co. 2, and their production is also being discontinued, so they must certainly be avoided. And so how do we get the target index? Well, as a backup option? We see that the lower possible index is achieved using Co 2. But to get higher indices, we need to employ mixtures involving heavy gases 17 00:04:28.500 --> 00:04:54.390 Nicola Nicassio: and the most promising option in terms of limited global warming potential consists in blending low molar concentration fluorketones like gases like this. C, 5 F, 10 0, with light gases like N 2 or Co. 2. So to get the required optical performance. But with a global warming potential, consistent or even smaller than the one of Co. 2 18 00:04:54.440 --> 00:05:07.209 Nicola Nicassio: making the mixture, of course, means monitoring the molar concentrations of these components. But this is something done or in the past. For instance, in the Lsd detector for the charanco ring imaging 19 00:05:07.580 --> 00:05:11.369 Nicola Nicassio: detector using basically measuring the speed of sound 20 00:05:11.610 --> 00:05:41.190 Nicola Nicassio: in the mixture to monitor and adjust on the run the concentration of the components. But a criticality, I would like to mention is the boiling point of gases like this, C. 5 F. 10 0. About 27 degrees. Compared with the Cpm. Operation temperature, that is, of minus 40 degrees. So it is maybe not the final choice. And further studies on the optimization of this mixture and identifying a proper gas are, of course, still ongoing. 21 00:05:41.210 --> 00:05:51.970 Nicola Nicassio: Nevertheless, in the following, we would like to show you some projections on the expected physics performance, and to do so, I will assume the optical properties of these specific mixtures. 22 00:05:52.440 --> 00:06:20.930 Nicola Nicassio: So, starting from the features of the cluster, we have planned to observe here, you can see a simulation for electrons and basically ions at 3 Gb. Where, as a function of 2 rapidity, so to get the full acceptance of the barrel, rich detector and counting the mean number of fired Cpms in the 2 cases. So you can see that we have a pretty good signal strength with approximately 10 fired Cpms. Per electron cluster. This, assuming the baseline 23 00:06:20.930 --> 00:06:28.369 Nicola Nicassio: photon detection, efficiency of the Cpms. Then you see these structures corresponding one to each of the sectors of the apparatus. 24 00:06:28.370 --> 00:06:47.519 Nicola Nicassio: This mean number follows the thickness of this function gap we get in each of the projective sector of the system. Of course, the larger the gap, the larger the number of emitted photons and fire the Cpms. And we just have some minor losses close to the sector boundaries. The points you see here, just because in this region part of the cluster is simply lost. 25 00:06:48.050 --> 00:07:14.379 Nicola Nicassio: And to give a more detailed focus on the geometry of these clusters, 1st of all, in one thing to take into account is that our system is embedded in a 2 Tesla superconducting solenoid. And this affects the shape of the cluster. And so there is an impact on the reconstruction algorithm. We have to implement, then, as you can see, for instance, at 3 Gv. We have clusters with a radius of the order of 8 26 00:07:14.380 --> 00:07:30.680 Nicola Nicassio: millimeter around the expected impact point with the Cpms featuring more than one photoelectron in the 1st neighbor Cpms to the impact point and second single photoelectron signals in the nearby cells. Of course, without the counting crosstalks and 27 00:07:30.720 --> 00:07:55.790 Nicola Nicassio: correlated noise in the Cpm. Itself. On the right you can see a map for the expected fraction of events with a minimum number of 3rd Cpms. And a minimum number of total required photoelectrons convoluted in the full detector acceptance that is then crucial to define a proper strategy for cluster reconstruction and then identification, and to evaluate the impact on the physics performance in the end. 28 00:07:56.260 --> 00:08:24.760 Nicola Nicassio: So here you can see a projection under identification performance in physics, events in particular, proton-proton, and then I will show lead collisions. The main challenge in this context is rejecting the plus or minus like signal due to the random clustering of background heats where we have a pion to electron ratio that is, of the order of 100. So we need a good rejection for this study. I developed a dedicated machine learning algorithm based on extreme gradient boosting. 29 00:08:24.760 --> 00:08:43.279 Nicola Nicassio: Taking as a feature the track parameters, as well as the number of 3rd Cpms. And photoelectrons in 4 regions in azimutal angle relative to the impinging track direction in the plots below, you can see the resulting purity and efficiency. In particular, the red dots for electrons as a function of momentum. 30 00:08:43.530 --> 00:08:53.829 Nicola Nicassio: And you can see that even accounting the Cpm dark count rate, we're able to ensure excellent identification performance up to the Muon and Pion Threshold in the gas. 31 00:08:53.860 --> 00:09:17.769 Nicola Nicassio: You see, these results look very promising. But are we happy about them? Actually, no, this is not the end of the story, because, as I said, the main target of the Illustra physics is our lead, lead, collisions, and in most central collisions we have a track multiplicity, and its multiplicity is so large that, of course the reconstruction is much more complex. Contamination 32 00:09:17.770 --> 00:09:37.889 Nicola Nicassio: is very large. So this kind of reconstruction techniques has to be optimized even further. But, as you can see, using this approach, even in this more complex situation, we're able to ensure reconstruction, purity and efficiency larger than 90% using this gas mixture in the whole momentum region of interest. 33 00:09:37.890 --> 00:09:56.210 Nicola Nicassio: So this is pretty good, but which is the impact on the physics. Well, here you can see, for instance, one of the physics cases we mentioned, starting from our letter of intent dating back in 2022, where basically, we want to prove the time evolution of the medium produced in 34 00:09:56.210 --> 00:10:23.450 Nicola Nicassio: the collision of a nuclei, the Coulomb plasma. And this is done by reconstructing the electron pairs as a function of reconstructed, invariant mass and transverse momentum. So we need to achieve a result like this, this, of course, a simulation where we need to achieve excellent coverage in terms of mee and pte exceeding the 3 Gv level. 35 00:10:23.490 --> 00:10:48.369 Nicola Nicassio: So this is considering only the baseline configuration. So using only the aerogel where no gas is included. So what they did was then evaluated. The impact on the electron reconstruction efficiency, combining the gas option to the other identification subsystems foreseen for the Ls. 3. Apparatus, and, as you can see, while in the baseline configuration 36 00:10:48.370 --> 00:11:04.430 Nicola Nicassio: the reconstruction is limited to the region close to 3 Gb. Including this threshold-based identification. Using gas, we're able to extend the identification range at 5 Gb. And even higher. 37 00:11:04.510 --> 00:11:32.109 Nicola Nicassio: You can better appreciate this from the one dimensional projection of previous plots for selected the electron pair. Transverse momentum values from one to 4 Gv. Where, as you can see, not only we recover the region at large, the electron transverse momentum, but we're able to also to suppress background in the region. And of course, gaining efficiency in the region at small me, simply because we have 38 00:11:32.110 --> 00:11:35.519 Nicola Nicassio: a cross checker with gas of the information on the aerogel. 39 00:11:35.520 --> 00:11:46.310 Nicola Nicassio: And so this is a double value. And in particular, as you can see from these plots, all these results show very good prospects for the whole at least 3 day electron physics program. 40 00:11:46.810 --> 00:12:02.359 Nicola Nicassio: So, coming to the conclusion I've shown now filling the Barrel Ridge expansion Gap with a proper gas mixture is very promising to announce the electrons plus or minus identification capabilities. 41 00:12:02.360 --> 00:12:16.989 Nicola Nicassio: consistently with the requirements for the l 3 physics. This can be done using an eco-friendly gas mixture by mixing heavy gases with likely fluoroketons like 42 00:12:16.990 --> 00:12:39.789 Nicola Nicassio: with light gas, lighter gases like N 2 and Co. 2, with a limited global warming potential. And in the end I've shown how the stable identification performance are achieved by keeping the same baseline geometry, the same silicon photo, multiplier technology. And this from proton-proton collisions to the most challenging environment of central lead lead. 43 00:12:40.210 --> 00:12:57.520 Nicola Nicassio: Our next steps in this respect are, 1st of all, more detailed studies on the final gas mixtures to be used. If this kind of approach becomes a thing, because, of course, not only it has to provide the required optical properties in terms of refractive index transmitters, and so on. 44 00:12:57.520 --> 00:13:24.159 Nicola Nicassio: but also have features like a boiling point consistent with the expected barrel, reach Cpm. Operation temperature. And finally, we are planning to perform the direct measurement on beam in July and September, testing various gases, and again with the aim of finding a proper option. Best option, or at least a compromise, to get the required physics performance consistently with the Ls 3 physics program. 45 00:13:24.340 --> 00:13:27.310 Nicola Nicassio: So this concludes my presentation. Thank you for your attention. 46 00:13:30.130 --> 00:13:31.430 Stefania Juks: Thank you so much, Nicole. 47 00:13:32.050 --> 00:13:38.180 Stefania Juks: Any questions, Duane. I think that was the 1st time. 48 00:13:39.877 --> 00:13:45.959 Dwayne Spiteri: A very interesting talk. But tap! Perhaps I missed something. I wasn't actually sort of 49 00:13:46.370 --> 00:14:09.009 Dwayne Spiteri: understanding. So the average gel and the gas are different, and the the gas that you tested most of it has a refractive index of 1.0 0 6. What is the refractive index equivalent of the Aero gel. And what's it made out of? Is it much better? Is it a similar thing to the gas? I didn't quite understand that. 50 00:14:09.570 --> 00:14:22.989 Nicola Nicassio: Sorry I didn't get the question. I mean, the average index, as I mentioned here, is in 1.0 0 6. This is the average weighted. Considering the whole molar concentration of the components. 51 00:14:23.170 --> 00:14:33.009 Nicola Nicassio: That is, in this case, for instance, obtained with the 1.0 0 3 of the n. 2, combined with what is about 1.0 0 52 00:14:33.210 --> 00:14:38.310 Nicola Nicassio: 1 6 5 50 of the c. 5 f. 10 o. 53 00:14:38.640 --> 00:14:42.529 Nicola Nicassio: so 1.0 0 6 is the index of the average in the end. 54 00:14:42.720 --> 00:14:43.339 Nicola Nicassio: I don't. 55 00:14:43.340 --> 00:14:50.600 Dwayne Spiteri: My point here is sorry just to just this is the conversation, because you said about Evergel, and I don't. I don't know where 56 00:14:50.830 --> 00:15:00.149 Dwayne Spiteri: where this is here, because this is this is just the gas mixture. Right? So with your Aero Gel. What is the refractive index of that? Or is that made of the same gases. 57 00:15:02.300 --> 00:15:04.630 Nicola Nicassio: Sorry I missed. I really missed the the question. 58 00:15:05.890 --> 00:15:07.240 Dwayne Spiteri: On Slide 13. 59 00:15:08.010 --> 00:15:17.749 Dwayne Spiteri: You have using aerogel only and aerogel plus gas, and we've talked about the gas. But I was wanting to know a bit more about the aerogel. I don't know, so we know if it was about 60 00:15:17.900 --> 00:15:20.490 Dwayne Spiteri: what the properties of that was, or what. 61 00:15:20.490 --> 00:15:25.279 Nicola Nicassio: You missed the word, Irgel? No, the problem is that the irogel is a reflective index of 1.0 3, 62 00:15:25.550 --> 00:15:44.209 Nicola Nicassio: so it would allow to separate. Considering this geometry, this specification to identify the electrons, the single electron at a momentum value no larger than 2 Gb, then, of course, you see values larger. Because then we have the electron in various mass combining both the electron information 63 00:15:44.230 --> 00:16:00.430 Nicola Nicassio: and what the gas does is simply to enhance this region, covering not only the region that is already covered by the arogel. That then, of course, is crucial for Edrons and other species, but extending the identification capabilities up to 4 Gb and more. 64 00:16:00.430 --> 00:16:19.699 Nicola Nicassio: So this is the gain we have with the gas. So basically adding this 1.0 0 6 to the one of the point 0 3. That is the baseline for the aerogel, and that is, of course, fixed because it was optimized for studies related to the identification of Hadrons that is crucial again, for Lstra. 65 00:16:20.140 --> 00:16:22.600 Dwayne Spiteri: Thank you. I was just missing the refractive index of the average. 66 00:16:23.330 --> 00:16:24.120 Nicola Nicassio: Okay. Okay. 67 00:16:27.190 --> 00:16:32.760 Stefania Juks: Thank you so much. Then, Nikolam, I think we can move on in the interest of time to our next.