Search for low mass dark matter in DarkSide-50: the bayesian network approach
Search for low mass dark matter in DarkSide-50: the bayesian network approach
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DOI：
10.1140/epjc/s10052-023-11410-4
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发表时间：
2023-02
期刊：
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通讯作者：
T. D. C. P. Agnes;I. Albuquerque;T. Alexander;A. Alton;M. Ave;H. Back;G. Batignani;K. Biery;V. Bocci;W. Bonivento;B. Bottino;S. Bussino;M. Cadeddu;M. Cadoni;F. Calaprice;A. Caminata;M. Campos;N. Canci;M. Caravati;N. Cargioli;M. Cariello;M. Carlini;V. Cataudella;P. Cavalcante;S. Cavuoti;S. Chashin;A. Chepurnov;C. Cicalò;G. Covone;D. D’Angelo;S. Davini;A. Candia;S. Cecco;G. Filippis;G. Rosa;A. Derbin;A. Devoto;M. D’Incecco;C. Dionisi;F. Dordei;M. Downing;D. D’Urso;M. Fairbairn;G. Fiorillo;D. Franco;F. Gabriele;C. Galbiati;C. Ghiano;C. Giganti;G. Giovanetti;A. Goretti;G. G. Cortona-G.;A. Grobov;M. Gromov;M. Guan;M. Gulino;B. Hackett;K. Herner;T. Hessel;B. Hosseini;F. Hubaut;E. Hungerford;A. Ianni;V. Ippolito;K. Keeter;C. Kendziora;M. Kimura;I. Kochanek;D. Korablev;G. Korga;A. Kubankin;M. Kuss;M. Commara;M. Lai;X. Li;M. Lissia;G. Longo;O. Lychagina;I. Machulin;L. Mapelli;S. Mari;J. Maricic;A. Messina;R. Milincic;J. Monroe;M. Morrocchi;X. Mougeot;V. Muratova;P. Musico;A. Nozdrina;A. Oleinik;F. Ortica;L. Pagani;M. Pallavicini;L. Pandola;E. Pantic;E. Paoloni;K. Pelczar;N. Pelliccia;S. Piacentini;A. Pocar;D. Poehlmann;S. Pordes;S. Poudel;P. Pralavorio;D. Price;F. Ragusa;M. Razeti;A. Razeto;A. Renshaw;M. Rescigno;J. Rode;A. Romani;D. Sablone;O. Samoylov;E. Sandford;W. Sands;S. Sanfilippo;C. Savarese;B. Schlitzer;D. Semenov;A. Shchagin;A. Sheshukov;M. Skorokhvatov;O. Smirnov;A. Sotnikov;S. Stracka;Y. Suvorov;R. Tartaglia;G. Testera;A. Tonazzo;E. Unzhakov;A. Vishneva;R. Vogelaar;M. Wada;H. Wang;Y. Wang;S. Westerdale;M. Wójcik;X. Xiao;C. Yang;G. Zuzel
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文献类型：
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作者：
T. D. C. P. Agnes;I. Albuquerque;T. Alexander;A. Alton;M. Ave;H. Back;G. Batignani;K. Biery;V. Bocci;W. Bonivento;B. Bottino;S. Bussino;M. Cadeddu;M. Cadoni;F. Calaprice;A. Caminata;M. Campos;N. Canci;M. Caravati;N. Cargioli;M. Cariello;M. Carlini;V. Cataudella;P. Cavalcante;S. Cavuoti;S. Chashin;A. Chepurnov;C. Cicalò;G. Covone;D. D’Angelo;S. Davini;A. Candia;S. Cecco;G. Filippis;G. Rosa;A. Derbin;A. Devoto;M. D’Incecco;C. Dionisi;F. Dordei;M. Downing;D. D’Urso;M. Fairbairn;G. Fiorillo;D. Franco;F. Gabriele;C. Galbiati;C. Ghiano;C. Giganti;G. Giovanetti;A. Goretti;G. G. Cortona-G.;A. Grobov;M. Gromov;M. Guan;M. Gulino;B. Hackett;K. Herner;T. Hessel;B. Hosseini;F. Hubaut;E. Hungerford;A. Ianni;V. Ippolito;K. Keeter;C. Kendziora;M. Kimura;I. Kochanek;D. Korablev;G. Korga;A. Kubankin;M. Kuss;M. Commara;M. Lai;X. Li;M. Lissia;G. Longo;O. Lychagina;I. Machulin;L. Mapelli;S. Mari;J. Maricic;A. Messina;R. Milincic;J. Monroe;M. Morrocchi;X. Mougeot;V. Muratova;P. Musico;A. Nozdrina;A. Oleinik;F. Ortica;L. Pagani;M. Pallavicini;L. Pandola;E. Pantic;E. Paoloni;K. Pelczar;N. Pelliccia;S. Piacentini;A. Pocar;D. Poehlmann;S. Pordes;S. Poudel;P. Pralavorio;D. Price;F. Ragusa;M. Razeti;A. Razeto;A. Renshaw;M. Rescigno;J. Rode;A. Romani;D. Sablone;O. Samoylov;E. Sandford;W. Sands;S. Sanfilippo;C. Savarese;B. Schlitzer;D. Semenov;A. Shchagin;A. Sheshukov;M. Skorokhvatov;O. Smirnov;A. Sotnikov;S. Stracka;Y. Suvorov;R. Tartaglia;G. Testera;A. Tonazzo;E. Unzhakov;A. Vishneva;R. Vogelaar;M. Wada;H. Wang;Y. Wang;S. Westerdale;M. Wójcik;X. Xiao;C. Yang;G. Zuzel
我们提出了一种新的方法来搜索暗物质的DarkSide-50实验，依赖于贝叶斯网络。该方法将检测器响应模型结合到似然函数中，明确地保持与感兴趣的量的连接。不需要关于问题的线性或概率分布函数的形状的假设，并且不需要将信号和背景光谱变形为干扰参数的函数。通过表示问题的贝叶斯网络，我们已经开发了一个推理算法的基础上马尔可夫链蒙特卡罗计算后验概率。一个巧妙的描述检测器响应模型的参数矩阵，使我们能够研究任何参数的系统变化对最终结果的影响。我们的方法不仅提供了所需的信息感兴趣的参数，但也对响应模型的潜在约束。我们的研究结果与最近发表的分析结果是一致的，并进一步完善了检测器响应模型的参数。
We present a novel approach for the search of dark matter in the DarkSide-50 experiment, relying on Bayesian Networks. This method incorporates the detector response model into the likelihood function, explicitly maintaining the connection with the quantity of interest. No assumptions about the linearity of the problem or the shape of the probability distribution functions are required, and there is no need to morph signal and background spectra as a function of nuisance parameters. By expressing the problem in terms of Bayesian Networks, we have developed an inference algorithm based on a Markov Chain Monte Carlo to calculate the posterior probability. A clever description of the detector response model in terms of parametric matrices allows us to study the impact of systematic variations of any parameter on the final results. Our approach not only provides the desired information on the parameter of interest, but also potential constraints on the response model. Our results are consistent with recent published analyses and further refine the parameters of the detector response model.