| 1. |
- Abazov, V. M., et al.
(författare)
-
Search for a scalar or vector particle decaying into Z gamma in p(p)over-bar collisions at root s=1.96 TeV
- 2009
-
Ingår i: Physics Letters B. - : Elsevier BV. - 0370-2693 .- 1873-2445. ; 671:3, s. 349-355
-
Tidskriftsartikel (refereegranskat)abstract
- We present a search for a narrow scalar or vector resonance decaying into Z gamma with a subsequent Z boson decay into a pair of electrons or moons. The data for this search were collected with the D circle divide detecror at the Fermilab Tevatron p (p) over bar collider at a center of mass energy root s = 1.96 TeV. Using 1.1 (1.0) fb(-1) of data, we observe 49 (50) candidate events in the electron (muon) channel, in good agreement with the standard model prediction. From the combination of both channels, we derive 95% C.L. upper limits on the cross section times branching fraction (sigma x B) into Z gamma. These limits range from 0.19 (0.20) pb for a scalar (vector) resonance mass of 600 GeV/c(2) to 2.5 (3.1) pb for a mass of 140 GeV/c(2).
|
|
| 2. |
- Abdo, A. A., et al.
(författare)
-
The on-orbit calibration of the Fermi Large Area Telescope
- 2009
-
Ingår i: Astroparticle physics. - : Elsevier BV. - 0927-6505 .- 1873-2852. ; 32:3-4, s. 193-219
-
Tidskriftsartikel (refereegranskat)abstract
- The Large Area Telescope (LAT) on-board the Fermi Gamma-ray Space Telescope began its on-orbit operations on June 23, 2008. Calibrations, defined in a generic sense, correspond to synchronization of trigger signals, optimization of delays for latching data, determination of detector thresholds, gains and responses, evaluation of the perimeter of the South Atlantic Anomaly (SAA), measurements of live time, of absolute time, and internal and spacecraft boresight alignments. Here we describe on-orbit calibration results obtained using known astrophysical sources, galactic cosmic rays, and charge injection into the front-end electronics of each detector. Instrument response functions will be described in a separate publication. This paper demonstrates the stability of calibrations and describes minor changes observed since launch. These results have been used to calibrate the LAT datasets to be publicly released in August 2009.
|
|
| 3. |
- Cuni-Sanchez, Aida, et al.
(författare)
-
High aboveground carbon stock of African tropical montane forests
- 2021
-
Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 596:7873, s. 536-542
-
Tidskriftsartikel (refereegranskat)abstract
- Tropical forests store 40–50 per cent of terrestrial vegetation carbon. However, spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests. Owing to climatic and soil changes with increasing elevation, AGC stocks are lower in tropical montane forests compared with lowland forests. Here we assemble and analyse a dataset of structurally intact old-growth forests (AfriMont) spanning 44 montane sites in 12 African countries. We find that montane sites in the AfriMont plot network have a mean AGC stock of 149.4 megagrams of carbon per hectare (95% confidence interval 137.1–164.2), which is comparable to lowland forests in the African Tropical Rainforest Observation Network4 and about 70 per cent and 32 per cent higher than averages from plot networks in montane and lowland forests in the Neotropics, respectively. Notably, our results are two-thirds higher than the Intergovernmental Panel on Climate Change default values for these forests in Africa8. We find that the low stem density and high abundance of large trees of African lowland forests is mirrored in the montane forests sampled. This carbon store is endangered: we estimate that 0.8 million hectares of old-growth African montane forest have been lost since 2000. We provide country-specific montane forest AGC stock estimates modelled from our plot network to help to guide forest conservation and reforestation interventions. Our findings highlight the need for conserving these biodiverse and carbon-rich ecosystems.
|
|
| 4. |
- Anelli, V. W., et al.
(författare)
-
RecSys 2021 challenge workshop : Fairness-aware engagement prediction at scale on Twiter's Home Timeline
- 2021
-
Ingår i: RecSys 2021 - 15th ACM Conference on Recommender Systems. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450384582 ; , s. 819-824
-
Konferensbidrag (refereegranskat)abstract
- The workshop features presentations of accepted contributions to the RecSys Challenge 2021, organized by Politecnico di Bari, ETH Zürich, Jönköping University, and the data set is provided by Twitter. The challenge focuses on a real-world task of tweet engagement prediction in a dynamic environment. For 2021, the challenge considers four different engagement types: Likes, Retweet, Quote, and replies. This year's challenge brings the problem even closer to Twitter's real recommender systems by introducing latency constraints. We also increases the data size to encourage novel methods. Also, the data density is increased in terms of the graph where users are considered to be nodes and interactions as edges. The goal is twofold: to predict the probability of different engagement types of a target user for a set of Tweets based on heterogeneous input data while providing fair recommendations. In fact, multi-goal optimization considering accuracy and fairness is particularly challenging. However, we believed that the recommendation community was nowadays mature enough to face the challenge of providing accurate and, at the same time, fair recommendations. To this end, Twitter has released a public dataset of close to 1 billion data points, > 40 million each day over 28 days. Week 1-3 will be used for training and week 4 for evaluation and testing. Each datapoint contains the tweet along with engagement features, user features, and tweet features. A peculiarity of this challenge is related to keeping the dataset updated with the platform: if a user deletes a Tweet, or their data from Twitter, the dataset is promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics. The challenge was well received with 578 registered users, and 386 submissions.
|
|
