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Keywords = inner star rule

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8 pages, 588 KiB  
Brief Report
Constraining the Inner Galactic DM Density Profile with H.E.S.S.
by Jaume Zuriaga-Puig
Astronomy 2024, 3(2), 114-121; https://doi.org/10.3390/astronomy3020008 - 11 Apr 2024
Viewed by 1124
Abstract
In this short review, corresponding to a talk given at the conference “Cosmology 2023 in Miramare”, we combine an analysis of five regions observed by H.E.S.S. in the Galactic Center, intending to constrain the Dark Matter (DM) density profile in a WIMP annihilation [...] Read more.
In this short review, corresponding to a talk given at the conference “Cosmology 2023 in Miramare”, we combine an analysis of five regions observed by H.E.S.S. in the Galactic Center, intending to constrain the Dark Matter (DM) density profile in a WIMP annihilation scenario. For the analysis, we include the state-of-the-art Galactic diffuse emission Gamma-optimized model computed with DRAGON and a wide range of DM density profiles from cored to cuspy profiles, including different kinds of DM spikes. Our results are able to constrain generalized NFW profiles with an inner slope γ≳1.3. When considering DM spikes, the adiabatic spike is completely ruled out. However, smoother spikes given by the interactions with the bulge stars are compatible if γ≲0.8, with an internal slope of γsp-stars=1.5. Full article
(This article belongs to the Special Issue Current Trends in Cosmology)
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Figure 1

Figure 1
<p>Five regions of interest considered. <b>Left</b> panel: VIR (in green), <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>&lt;</mo> <mn>0.1</mn> <mo>°</mo> </mrow> </semantics></math> (r ≲ 15 pc); Ridge (in gray), <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>b</mi> <mo>|</mo> <mo>&lt;</mo> <mn>0.3</mn> <mo>°</mo> </mrow> </mrow> </semantics></math> (43 pc) and <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>l</mi> <mo>|</mo> <mo>&lt;</mo> <mn>1.0</mn> <mo>°</mo> </mrow> </mrow> </semantics></math> (145 pc), with some masks applied; Diffuse Region (blue), <math display="inline"><semantics> <mrow> <mn>0.15</mn> <mo>°</mo> <mo>&lt;</mo> <mi>θ</mi> <mo>&lt;</mo> <mn>0.45</mn> <mo>°</mo> </mrow> </semantics></math> (22 pc ≲ r ≲ 65 pc); Halo (red), <math display="inline"><semantics> <mrow> <mn>0.3</mn> <mo>°</mo> <mo>&lt;</mo> <mi>θ</mi> <mo>&lt;</mo> <mn>1.0</mn> <mo>°</mo> </mrow> </semantics></math> (43 pc ≲ r ≲ 145 pc), excluding the latitudes <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> <mi>b</mi> <mo>|</mo> <mo>&lt;</mo> <mn>0.3</mn> <mo>°</mo> </mrow> </mrow> </semantics></math> (the Galactic plane). <b>Right</b> panel: IGS (orange), <math display="inline"><semantics> <mrow> <mn>0.5</mn> <mo>°</mo> <mo>&lt;</mo> <mi>θ</mi> <mo>&lt;</mo> <mn>3.0</mn> <mo>°</mo> </mrow> </semantics></math> (72 pc ≲ r ≲ 434 pc), excluding the Galactic plane and several sources (light grey).</p> Full article ">Figure 2
<p>Comparison of the J-factor <math display="inline"><semantics> <msub> <mrow> <mo>〈</mo> <mi>J</mi> <mo>〉</mo> </mrow> <mrow> <mo>Δ</mo> <mo>Ω</mo> </mrow> </msub> </semantics></math> between the different DM models (first row for generalized NFW, second for adiabatic spike, and star spike in the third one). The fit values and upper limits come from the gamma-ray spectra observed by H.E.S.S. in the regions defined in <a href="#astronomy-03-00008-f001" class="html-fig">Figure 1</a>, assuming the thermal relic cross-section <math display="inline"><semantics> <mrow> <mrow> <mo>〈</mo> <mi>σ</mi> <mi>v</mi> <mo>〉</mo> </mrow> <mo>≃</mo> <mn>2.2</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>26</mn> </mrow> </msup> <msup> <mi>cm</mi> <mn>3</mn> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>. We show in grey the uncertainties of the fit values (1<math display="inline"><semantics> <mi>σ</mi> </semantics></math> and 2<math display="inline"><semantics> <mi>σ</mi> </semantics></math> for VIR and Ridge, 2<math display="inline"><semantics> <mi>σ</mi> </semantics></math> for Diffuse, and 2<math display="inline"><semantics> <mi>σ</mi> </semantics></math> upper limits for Halo). See the text for more details.</p> Full article ">Figure 2 Cont.
<p>Comparison of the J-factor <math display="inline"><semantics> <msub> <mrow> <mo>〈</mo> <mi>J</mi> <mo>〉</mo> </mrow> <mrow> <mo>Δ</mo> <mo>Ω</mo> </mrow> </msub> </semantics></math> between the different DM models (first row for generalized NFW, second for adiabatic spike, and star spike in the third one). The fit values and upper limits come from the gamma-ray spectra observed by H.E.S.S. in the regions defined in <a href="#astronomy-03-00008-f001" class="html-fig">Figure 1</a>, assuming the thermal relic cross-section <math display="inline"><semantics> <mrow> <mrow> <mo>〈</mo> <mi>σ</mi> <mi>v</mi> <mo>〉</mo> </mrow> <mo>≃</mo> <mn>2.2</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>26</mn> </mrow> </msup> <msup> <mi>cm</mi> <mn>3</mn> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>. We show in grey the uncertainties of the fit values (1<math display="inline"><semantics> <mi>σ</mi> </semantics></math> and 2<math display="inline"><semantics> <mi>σ</mi> </semantics></math> for VIR and Ridge, 2<math display="inline"><semantics> <mi>σ</mi> </semantics></math> for Diffuse, and 2<math display="inline"><semantics> <mi>σ</mi> </semantics></math> upper limits for Halo). See the text for more details.</p> Full article ">
21 pages, 11583 KiB  
Article
Real-Time Kinematically Synchronous Planning for Cooperative Manipulation of Multi-Arms Robot Using the Self-Organizing Competitive Neural Network
by Hui Zhang, Hongzhe Jin, Mingda Ge and Jie Zhao
Sensors 2023, 23(11), 5120; https://doi.org/10.3390/s23115120 - 27 May 2023
Cited by 1 | Viewed by 1510
Abstract
This paper presents a real-time kinematically synchronous planning method for the collaborative manipulation of a multi-arms robot with physical coupling based on the self-organizing competitive neural network. This method defines the sub-bases for the configuration of multi-arms to obtain the Jacobian matrix of [...] Read more.
This paper presents a real-time kinematically synchronous planning method for the collaborative manipulation of a multi-arms robot with physical coupling based on the self-organizing competitive neural network. This method defines the sub-bases for the configuration of multi-arms to obtain the Jacobian matrix of common degrees of freedom so that the sub-base motion converges along the direction for the total pose error of the end-effectors (EEs). Such a consideration ensures the uniformity of the EE motion before the error converges completely and contributes to the collaborative manipulation of multi-arms. An unsupervised competitive neural network model is raised to adaptively increase the convergence ratio of multi-arms via the online learning of the rules of the inner star. Then, combining with the defined sub-bases, the synchronous planning method is established to achieve the synchronous movement of multi-arms robot rapidly for collaborative manipulation. Theory analysis proves the stability of the multi-arms system via the Lyapunov theory. Various simulations and experiments demonstrate that the proposed kinematically synchronous planning method is feasible and applicable to different symmetric and asymmetric cooperative manipulation tasks for a multi-arms system. Full article
(This article belongs to the Special Issue New Advances in Robotically Enabled Sensing)
►▼ Show Figures

Figure 1

Figure 1
<p>A type of cooperative manipulation. (<b>a</b>) Carrying. (<b>b</b>) Operating rudder. (<b>c</b>) Operating a wrench. (<b>d</b>) Using pliers. (<b>e</b>) Multi-station operation.</p> Full article ">Figure 2
<p>The diagram for the common features in the cooperative manipulation of multi-arms.</p> Full article ">Figure 3
<p>Simple configuration of multi-arm robot.</p> Full article ">Figure 4
<p>Kinematically synchronous planning for multi-arm robot. <span class="html-italic">U<sub>in</sub></span> = <b>t</b> = (<b>t</b><sub>1</sub>, <b>t</b><sub>2</sub>, …, <b>t</b><sub>N</sub>)<sup>T</sup>. <span class="html-italic">U<sub>out</sub></span> = <b>s</b> = (<b>s</b><sub>1</sub>, <b>s</b><sub>2</sub>, …, <b>s</b><sub>N</sub>)<sup>T</sup>.</p> Full article ">Figure 5
<p>Motion planning and EE motion for the EE with the minimum pose error, <math display="inline"><semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>e</mi> </mstyle> <mrow> <mi>min</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi mathvariant="normal">T</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mover accent="true"> <mi>v</mi> <mo>^</mo> </mover> </mstyle> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi mathvariant="normal">T</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>μ</mi> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>e</mi> </mstyle> <mrow> <mi>min</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi mathvariant="normal">T</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>The configuration of three-arm robot with 15-DoFs.</p> Full article ">Figure 7
<p>Inverse kinematics based on the traditional method in real time. (<b>a</b>) Motion of multi-arms. (<b>b</b>) Joint angles. (<b>c</b>) EE position velocity. (<b>d</b>) EE attitude velocity. (<b>e</b>) EE position error. (<b>f</b>) EE attitude error.</p> Full article ">Figure 8
<p>Inverse kinematics based on the sub-base method in real time. (<b>a</b>) Motion of multi-arms. (<b>b</b>) Joint angles. (<b>c</b>) EE position velocity. (<b>d</b>) EE attitude velocity. (<b>e</b>) EE position error. (<b>f</b>) EE attitude error.</p> Full article ">Figure 9
<p>The configuration of two-arm robot with 13-DoFs.</p> Full article ">Figure 10
<p>The principle of two-arm robot with 13-DoFs.</p> Full article ">Figure 11
<p>Carrying task. (<b>a</b>) Initial configuration. (<b>b</b>) Manipulating process.</p> Full article ">Figure 12
<p>Trajectories for dual arms in carrying task. (<b>a</b>) EE movement. (<b>b</b>) Joint trajectory. (<b>c</b>) EE position. (<b>d</b>) EE attitude. (<b>e</b>) Position velocity. (<b>f</b>) Attitude velocity. (<b>g</b>) Pose velocity error. (<b>h</b>) EE pose error.</p> Full article ">Figure 13
<p>Manipulating pilers. (<b>a</b>) Initial configuration. (<b>b</b>) Manipulating process.</p> Full article ">Figure 14
<p>Trajectories for dual arms in manipulating pilers. (<b>a</b>) EE movement. (<b>b</b>) Joint trajectory. (<b>c</b>) EE position. (<b>d</b>) EE attitude. (<b>e</b>) Position velocity. (<b>f</b>) Attitude velocity. (<b>g</b>) Pose velocity error. (<b>h</b>) EE pose error.</p> Full article ">Figure 15
<p>Manipulating rudder. (<b>a</b>) Initial configuration. (<b>b</b>) Manipulating process.</p> Full article ">Figure 16
<p>Trajectories for dual arms in manipulating rudder. (<b>a</b>) EE movement. (<b>b</b>) Joint trajectory. (<b>c</b>) EE position. (<b>d</b>) EE attitude. (<b>e</b>) Position velocity. (<b>f</b>) Attitude velocity. (<b>g</b>) Pose velocity error. (<b>h</b>) EE pose error.</p> Full article ">Figure 16 Cont.
<p>Trajectories for dual arms in manipulating rudder. (<b>a</b>) EE movement. (<b>b</b>) Joint trajectory. (<b>c</b>) EE position. (<b>d</b>) EE attitude. (<b>e</b>) Position velocity. (<b>f</b>) Attitude velocity. (<b>g</b>) Pose velocity error. (<b>h</b>) EE pose error.</p> Full article ">
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