1 00:00:00,000 --> 00:00:00,630 You have left 2 00:00:01,620 --> 00:00:09,420 Okay, thank you for introduction and I will start with the outline of the 3 00:00:09,420 --> 00:00:16,320 presentation. So, so, I will I would like to present four measurements you know 4 00:00:16,320 --> 00:00:22,860 start with the left arm measurement which is focused on production production and 5 00:00:22,950 --> 00:00:29,910 electronic furniture distribution in certain events, then we will continue with 6 00:00:30,120 --> 00:00:35,310 some past checks differential measurements, measurement is focused on 7 00:00:35,310 --> 00:00:42,210 differential measurement cross sections and gathered on the doctor sections and 8 00:00:42,570 --> 00:00:48,750 even with all atonic measurements which is focused on differential section. So, let's 9 00:00:48,750 --> 00:00:56,190 stop it go to measurement. This measurement is done in a new channel. This 10 00:00:56,190 --> 00:01:04,680 means that it is required exactly one elect And random neon opposites charges in 11 00:01:04,680 --> 00:01:12,000 event selection and this major event is entered into the proton proton collisions 12 00:01:14,130 --> 00:01:19,770 taken in 2015 and 2016. So, this corresponds to the monitor to the basics. 13 00:01:22,800 --> 00:01:30,870 And the strategy is to extract the DT value section from the set of the two 14 00:01:32,100 --> 00:01:38,220 equations which are on this slide and, and this cross section is extracted together 15 00:01:38,220 --> 00:01:44,250 with the parameter epsilon Venus is the probability that we required from the 16 00:01:44,250 --> 00:01:54,540 electronic decay is constructed as jet and so, these two equations the first equation 17 00:01:54,540 --> 00:02:03,720 is with events with exactly one v jets and the second To switch to technique jets and 18 00:02:04,680 --> 00:02:12,300 and to the me also, because this is the right hand side of this equation. So, then 19 00:02:12,330 --> 00:02:18,990 you have to take that times cross section this will give us total number of events 20 00:02:18,990 --> 00:02:24,660 and then it is multiplied by efficiencies. So, the efficiency of immune 21 00:02:25,170 --> 00:02:30,750 reconstruction This includes also branching ratio to immune channel and that 22 00:02:30,750 --> 00:02:37,080 is this epsilon V which is already described and CB corresponds to small 23 00:02:37,080 --> 00:02:43,020 correlation between lagging states and you can see that it is very close to one when 24 00:02:43,800 --> 00:02:50,970 one corresponds to no correlations, no correlation. So, now let's have a look on 25 00:02:51,210 --> 00:03:00,840 results of this measurement. So, the central value is 126.4. ico ban And 26 00:03:01,200 --> 00:03:07,830 uncertainties are classified into four sources. and here we can see that the 27 00:03:07,920 --> 00:03:17,520 uncertainty related to luminosity is dominant. And in order to know in order to 28 00:03:17,520 --> 00:03:25,470 reduce this uncertainty we we are this measure this session was measured in in 29 00:03:25,980 --> 00:03:33,000 2015 to 17 2016 separately in the first step and then these two measurements were 30 00:03:33,000 --> 00:03:39,420 combined using best linear unbiased estimator. And this reduced the final 31 00:03:39,420 --> 00:03:49,170 artifact certainty from about 2.7% to 4%. And this makes matters very precision. It 32 00:03:49,170 --> 00:03:59,760 is I think, it is the best from topless experiment and under on the right we can 33 00:03:59,760 --> 00:04:05,790 see Comparison with next text ad go through plus next next week predictions. 34 00:04:06,540 --> 00:04:12,870 Comparison with the last results and agreement is very good. Now Now let's have 35 00:04:12,870 --> 00:04:18,600 a look on differential cross sections. These sections were measured in the finish 36 00:04:18,630 --> 00:04:26,760 and finish off a space at particle level. And here we can see left on et. And the 37 00:04:26,760 --> 00:04:33,660 plot on the left shows the shape of measured data. This is indicated by like 38 00:04:33,870 --> 00:04:39,060 points and distributions are compared with various predictions with different 39 00:04:39,060 --> 00:04:45,690 configurations and with different parameters and with different data sets. 40 00:04:46,680 --> 00:04:54,990 And in general, we see that they are dancing in variations over data ratios and 41 00:04:55,410 --> 00:05:00,000 this is also confirmed in chi square test for web development. 42 00:05:01,590 --> 00:05:10,350 Below 0.05 for all predictions, except the one at this seminar, last week I had a PDF 43 00:05:10,350 --> 00:05:18,660 set. So so this is for the electron measurement. And let's continue with 44 00:05:18,660 --> 00:05:23,550 differential measurement in lepton politics channel. This measurement use 45 00:05:23,880 --> 00:05:29,340 uses exactly the same data set but it is demographic channel and then two 46 00:05:29,640 --> 00:05:32,700 topologies which were used to reconstruct the 47 00:05:33,990 --> 00:05:35,700 reconstructed DVR system. 48 00:05:38,010 --> 00:05:43,320 This is the full TT VR system reconstruction. So both both of blacks are 49 00:05:43,320 --> 00:05:50,790 reconstructed results. pathology is focused on long term PT and where three 50 00:05:50,790 --> 00:05:59,460 quads from top decay are concerning you is in three different jets while the boosted 51 00:05:59,460 --> 00:06:07,380 topology Focus on high entropy theory regime where we have the top and make up 52 00:06:07,380 --> 00:06:14,730 the case by itself very similar directions and direction and they are reconstructed 53 00:06:14,730 --> 00:06:22,260 with the with the single large radius jet distributions aren't fully to particle and 54 00:06:22,260 --> 00:06:28,320 bottom level. So let's move on can fiducial face spaces outside particular 55 00:06:29,160 --> 00:06:36,210 those are other elective at spectra result on the left and boosted on the right. You 56 00:06:36,210 --> 00:06:42,180 can see different ranges of spectra result in the rest of the project we start at 57 00:06:42,210 --> 00:06:56,520 zero at boosted we started in 350 gV, but then shift to the ipts much the tail in is 58 00:06:56,520 --> 00:06:58,560 much longer in the boosted so 59 00:07:00,000 --> 00:07:01,860 So, these measurements can 60 00:07:04,020 --> 00:07:11,310 provide different information and the trends in in the ratio plots are similar 61 00:07:11,700 --> 00:07:19,680 in both both cases in both reconstructions but they are actually consistent. They are 62 00:07:19,680 --> 00:07:27,570 not significant according to PayScale dot test. Our predictions are actually good 63 00:07:29,280 --> 00:07:30,030 data. 64 00:07:32,370 --> 00:07:36,870 Now let's have a look on double differential sections. This is TT bad PT 65 00:07:36,870 --> 00:07:45,000 in bits of at band mass. So, on the left we have pt, pt spectra in various bands of 66 00:07:45,000 --> 00:07:53,190 TT bar mass. And this is the information about Vinson sim diligent and also we can 67 00:07:53,430 --> 00:07:59,970 also we spend this internal PT but PT distributions are scaled by factor 68 00:08:00,000 --> 00:08:09,180 syndicated in Beckett's you know the to avoid some overlaps and on the right hand 69 00:08:09,180 --> 00:08:15,870 side then there is they are they show plots for individual bins of Tiki Bar mass 70 00:08:16,800 --> 00:08:22,320 and you can see the differences between the predictions and also differences 71 00:08:22,320 --> 00:08:29,790 between predictions and data are very large. So, so, it is very hard to validate 72 00:08:29,820 --> 00:08:36,870 such differential sections and actually our predictions fail to describe this 73 00:08:37,500 --> 00:08:46,260 according to chi square test if you are inside of a better logo below 0.01. Now, 74 00:08:46,800 --> 00:08:51,330 this measurement also measured at the bottom this bottom number and there it is, 75 00:08:51,390 --> 00:08:55,320 this was used also to compare them with next next to median for the predictions 76 00:08:56,040 --> 00:09:04,110 and on the left that is pretty bad at Distribution Center in this competitive 77 00:09:04,110 --> 00:09:09,510 market must be tightened also with this next next reading or the predictions and 78 00:09:10,050 --> 00:09:15,720 but we see that here we have a good agreement of the magic spectrum with 79 00:09:15,720 --> 00:09:22,080 predictions and the plot on the right is particularly interesting because it is to 80 00:09:22,080 --> 00:09:26,730 start to test sensitivity to different next week's reading for the PDF sets and 81 00:09:26,730 --> 00:09:34,380 also possible addition of next meeting or directory corrections and and you can see 82 00:09:34,380 --> 00:09:41,400 here the different points corresponding to different predictions with with or without 83 00:09:41,400 --> 00:09:46,920 actually corrections etc. But they are very similar and the conclusion is that 84 00:09:46,920 --> 00:09:54,060 sensitivity is very low in kinematic tential based measurement. Okay, so now 85 00:09:54,060 --> 00:10:01,320 let's continue with this is all from differential measurement. composition and 86 00:10:02,340 --> 00:10:08,730 continuity includes projection measurement. And this is done with a with 87 00:10:08,730 --> 00:10:16,530 float on two data sets eight to 10 1318 months one and two. And, but but results 88 00:10:16,530 --> 00:10:24,660 are only determined at a scale and strategy is to classify selected events 89 00:10:24,660 --> 00:10:30,450 into four signal regions according to number of jets and number of big jets as I 90 00:10:30,450 --> 00:10:37,980 said as shown in the table. And then, in these three regions we have Yes, 91 00:10:38,250 --> 00:10:43,200 differential variables which are then using which are used to extract cross 92 00:10:43,200 --> 00:10:43,920 sections 93 00:10:45,750 --> 00:10:48,480 by using simultaneous profiling to fit. 94 00:10:49,740 --> 00:10:54,300 So, let's have a look on this distributions differential distributions 95 00:10:54,330 --> 00:11:00,000 on the top if you have breakfast distributions in difference 96 00:11:00,000 --> 00:11:07,170 acknowledgments and also defended in each region views the different different 97 00:11:07,170 --> 00:11:15,270 variable which has the best sensitivity to or has seen them best for for the 98 00:11:15,270 --> 00:11:21,750 measurement of the debarker section and the parameters of interest, this parameter 99 00:11:21,750 --> 00:11:26,160 of interest this ratio of the tobacco section over the standard model prediction 100 00:11:26,160 --> 00:11:34,170 and all sources of uncertainties, they can assess as parameters in the fits below we 101 00:11:34,170 --> 00:11:42,210 can see posterior distributions which and they are the agreement is improved after 102 00:11:42,210 --> 00:11:51,360 faith. Now, this can be seen. Just notice that the survey mentioned the ratio plots 103 00:11:51,360 --> 00:11:52,320 is different. 104 00:11:53,820 --> 00:11:55,440 So, okay 105 00:11:57,060 --> 00:12:06,330 I think this is an offer For this and I will just continue with the results. So, 106 00:12:06,330 --> 00:12:15,390 also the measurement measure session in the English, but in the full face basis, 107 00:12:15,570 --> 00:12:26,340 it doesn't need an 3140 coupon, they can certainly uncertainties about 4.46% in 108 00:12:26,340 --> 00:12:30,210 photo finisher, last section that uncertainty is a little bit smaller this 109 00:12:30,210 --> 00:12:38,700 4.1% because there is not so much extrapolation and and this this results 110 00:12:38,700 --> 00:12:43,110 are with particular language model predictions and also with other published 111 00:12:43,110 --> 00:12:49,920 measurements. And this measurement also provides dependency of the tobacco 112 00:12:49,920 --> 00:12:57,510 sections of the mass which can be used to measure of mass in in the future. 113 00:12:58,140 --> 00:12:59,550 You have three minutes left 114 00:13:00,030 --> 00:13:09,330 Okay, thank you. So, the last is organic, last measurement is organic. And here I 115 00:13:09,330 --> 00:13:13,890 would like to point out that this is for the first time in that experiment, we 116 00:13:13,890 --> 00:13:20,310 measured organic differential plus a section in another channel in the rest of 117 00:13:20,310 --> 00:13:27,930 topology that is still relevant in the in the event selection it is requested at 118 00:13:27,930 --> 00:13:33,360 least six jets in an event in order to be able to reconstruct for Tiki Bar system, 119 00:13:33,990 --> 00:13:39,150 and exactly two of these jets must be detected in order to reduce Multijet 120 00:13:39,150 --> 00:13:47,310 background and Ashtanga toccata is applied in order to reduce the other events from 121 00:13:47,310 --> 00:13:55,200 other channels. And in order to reconstruct the device's system it is 122 00:13:55,200 --> 00:14:05,250 needed to fit through the combination of light jets And this is done with the 123 00:14:05,250 --> 00:14:12,810 combination minimizes the chi square this describes which is shown on this slide. So 124 00:14:12,810 --> 00:14:19,050 let's have a look on the results. So on the sorry, nearly everything of PT the 125 00:14:19,050 --> 00:14:25,800 products are, according to PT. So under Latvia detector distribution, here we can 126 00:14:25,800 --> 00:14:30,840 see how much they don't have in basic knowledge and that the dominant background 127 00:14:30,870 --> 00:14:38,880 originates is from Multijet processes. And it is, it is determined using data driven 128 00:14:38,880 --> 00:14:44,400 techniques called ABCD method. In the plot in the middle we have future particle face 129 00:14:44,400 --> 00:14:52,530 base distribution. And here you can see that the uncertainties are around 10%. So, 130 00:14:52,800 --> 00:15:01,560 so this is actually good results with the fact that mentioned In this channel source 131 00:15:01,560 --> 00:15:03,930 for basic tinnitus challenging and now we have 132 00:15:05,190 --> 00:15:10,410 nice results and also on the right we have we have 133 00:15:12,059 --> 00:15:18,179 measurement from face by face distribution and you can see that Transcender 134 00:15:18,179 --> 00:15:27,209 predictions over data are similar in in in fiducial and face space but but it's 135 00:15:27,509 --> 00:15:34,349 expect this shows that that predictions are still in good agreement except of the 136 00:15:34,349 --> 00:15:43,259 Sherpa and pilots plus be tired with with relation down Okay. So now I would like to 137 00:15:43,259 --> 00:15:48,449 continue in double differential intersections here df dT by dt in bins of 138 00:15:48,479 --> 00:15:54,689 number of jets So, so this it was possible to measure differential motion even in 139 00:15:54,989 --> 00:15:58,919 translate nine or more jets and 140 00:15:59,040 --> 00:16:01,920 I'm afraid you Have to speed up the time 141 00:16:02,939 --> 00:16:04,199 good. So, this is 142 00:16:05,549 --> 00:16:08,009 sorry okay. So, I just 143 00:16:09,750 --> 00:16:15,300 say that the chi square test shows very, very bad agreement between prediction data 144 00:16:15,300 --> 00:16:20,820 and this assertion is also sensitive and I will try to summarize the presentation. 145 00:16:20,820 --> 00:16:29,580 So, so, so the printed measurements are in all three main entity back channel. The 146 00:16:29,580 --> 00:16:34,320 darker sections were measured in lepton patch sets and in depth on channel a bit 147 00:16:34,350 --> 00:16:40,080 improve pedic decision or Atlas. Differential measurements now includes a 148 00:16:41,520 --> 00:16:45,720 double referential cross section which are monsen more sensitive to the level that 149 00:16:45,720 --> 00:16:54,600 predictions and also other or MC modelling can PDF it and it is important to mention 150 00:16:54,630 --> 00:16:59,220 also the techniques leading up to the etheric corrections that are tested in 151 00:16:59,490 --> 00:17:05,850 electronics. It's gentle and it was founded that it has almost no impact on in 152 00:17:05,850 --> 00:17:15,930 the in the given kinematic range. And, and since I present it or other measurement 153 00:17:15,960 --> 00:17:21,750 now I will skip this part and so think Thank you. Thank you for your attention. 154 00:17:21,750 --> 00:17:27,330 And I'm sorry, I'm, I was a bit longer with this presentation and I don't have a 155 00:17:27,330 --> 00:17:35,670 link to the zoom but I will try to create this this zoom link now and attach it to 156 00:17:35,670 --> 00:17:37,710 the slides or to the agenda directly. 157 00:17:39,060 --> 00:17:41,340 Okay, perfect. Thank you very much. 158 00:17:42,750 --> 00:17:45,420 Are there any questions to this presentation? 159 00:17:48,570 --> 00:17:49,440 I have one. 160 00:17:51,450 --> 00:17:56,400 I was I was wondering if I look at slide 11. If Can you go back to your slide 11 161 00:17:56,400 --> 00:18:01,020 where you show profit profit distributions. I was impressed by the 162 00:18:01,020 --> 00:18:06,630 amount of constraints you get in in the systematic uncertainties, could you 163 00:18:06,630 --> 00:18:13,860 elaborate on that which which systematic uncertainty in particular get constraints, 164 00:18:14,580 --> 00:18:19,320 it is clear from the above distribute the rescue plots that the systematic 165 00:18:19,320 --> 00:18:25,260 uncertainty seems to be an overestimation. But I wonder what that sources or what are 166 00:18:25,260 --> 00:18:26,070 the sources? 167 00:18:27,150 --> 00:18:38,010 Yeah, we have actually some sources of of the uncertainties on the slide. Those are 168 00:18:38,010 --> 00:18:46,680 the main sources and some of them are are significant are reduced. That's right. But 169 00:18:46,680 --> 00:18:51,480 that could be also other effects that we are exactly to the same distributions, 170 00:18:51,960 --> 00:18:55,830 which are used in the fate and that could be some other effects of this has to be 171 00:18:55,830 --> 00:19:02,550 also validated with some different distribution and it was validated, but I 172 00:19:02,550 --> 00:19:09,840 don't have these plots, but so, there could be some other effect of this. But 173 00:19:09,870 --> 00:19:14,160 here we can see that what are the main sources 174 00:19:15,840 --> 00:19:22,320 so, I'm not familiar reading of this, this kind of clock, but should I read the data 175 00:19:22,320 --> 00:19:27,300 points that shrink down which shows the constraint on the 176 00:19:28,080 --> 00:19:31,290 data that is the profit is 177 00:19:32,940 --> 00:19:35,280 or the profit is valid. 178 00:19:37,980 --> 00:19:45,960 mentioned display shows actually how this parameter the change in the parameter. So 179 00:19:45,960 --> 00:19:52,530 for example, for the sixth level responses parameter is shifted the last in the fate. 180 00:19:53,460 --> 00:19:57,090 So it is shower modal migration that 181 00:19:57,720 --> 00:20:05,580 is showing auto migration So constraint a lot. I think there's this Chevron modeling 182 00:20:05,580 --> 00:20:13,950 concern of certainty. It is actually to two sources one of them is constraint but 183 00:20:13,950 --> 00:20:15,540 other one not too much. 184 00:20:16,980 --> 00:20:18,240 I see the two months 185 00:20:19,980 --> 00:20:20,490 yes and 186 00:20:20,520 --> 00:20:24,960 yes. So, I would like to explain that for the brief it in a post with uncertainties 187 00:20:24,960 --> 00:20:29,250 most of the reduction actually does not come from the constraints of the 188 00:20:29,250 --> 00:20:32,940 systematic uncertainties but by taking into account the correlations of the 189 00:20:32,940 --> 00:20:36,450 numerous parameters that are estimated during the fitting procedure. 190 00:20:41,940 --> 00:20:44,520 Okay, can you go back to slide 11 again, 191 00:20:46,080 --> 00:20:55,380 so, I'm comparing the band and the above and the bottom. So you're saying that some 192 00:20:55,380 --> 00:20:57,750 of the correlations are not taking into account at the above? 193 00:20:58,200 --> 00:21:01,290 Well in the above which assume that they are datasets parameters aren't 194 00:21:01,290 --> 00:21:04,710 uncorrelated, so we added them in quadrature. But in the post feed, we 195 00:21:04,710 --> 00:21:07,590 actually can get the correlations of the nuisance parameters for these 196 00:21:07,590 --> 00:21:12,960 distributions. And we can build the plots, taking into account the correlations and 197 00:21:12,960 --> 00:21:17,430 this is by far the dominant source of the reduction of the uncertainty. 198 00:21:18,810 --> 00:21:22,680 Okay, okay. Thank you. You're welcome. Thank you. 199 00:21:25,380 --> 00:21:30,750 Okay, great, thanks to one. And then I think we have to move on. Thanks, Peter. 200 00:21:30,750 --> 00:21:36,600 And if you can make webinar linked on your slides and re upload your slides for later 201 00:21:36,630 --> 00:21:37,380 that would be nice.