Classification of Fermi-LAT blazars with Bayesian neural networks

Butter, Anja; Finke, Thorben; Keil, Felicitas; Krämer, M.; Manconi, Silvia

doi:10.1088/1475-7516/2022/04/023

Cited by 9 publications

(11 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the final results, we use the full dataset for training. The lower end of this accuracy range corresponds to the setups with m DM = 30 GeV and m DM = 80 GeV, and is comparable to the accuracy achieved when learning classification between different classes within the 4FGL catalog, as in [34]. This further supports our approach and shows that the bias from comparing synthetic data with real data is negligible given the realistic simulation approach we have set up.…”

Section: Jcap07(2023)033supporting

confidence: 74%

“…Machine learning classifiers have been used in recent years to study UNID sources [28][29][30][31][32][33][34][35] and to search for potential dark subhalos. Using XGBoost classifiers on sources in the third Fermi-LAT catalog, ref.…”

Section: Jcap07(2023)033mentioning

confidence: 99%

“…We have implemented a Bayesian Neural Network (BNN) [70][71][72] based on the setup in [34] where the advantages of using such a BNN on the energy spectra of γ-ray sources were shown for classifying blazars of uncertain type. Most notable is the benefit of the uncertainty in the network prediction that can be inferred from the network output.…”

Section: Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Searching for dark matter subhalos in the Fermi-LAT catalog with Bayesian neural networks

Butter

Krämer

Manconi

et al. 2023

J. Cosmol. Astropart. Phys.

Self Cite

View full text Add to dashboard Cite

About a third of the γ-ray sources detected by the Fermi Large Area Telescope (Fermi-LAT) remain unidentified, and some of these could be exotic objects such as dark matter subhalos. We present a search for these sources using Bayesian neural network classification methods applied to the latest 4FGL-DR3 Fermi-LAT catalog. We first simulate the γ-ray properties of dark matter subhalos using models from N-body simulations and semi-analytical approaches to the subhalo distribution. We then assess the detectability of this sample in the 4FGL-DR3 catalog using the Fermi-LAT analysis tools. We train our Bayesian neural network to identify candidate dark matter subhalos among the unidentified sources in the 4FGL-DR3 catalog. Our results allow us to derive conservative bounds on the dark matter annihilation cross section by excluding unidentified sources classified as astrophysical-like by our networks. We estimate the number of candidate dark matter subhalos for different dark matter masses and provide a publicly available list for further investigation. Our bounds on the dark matter annihilation cross section are comparable to previous results and become particularly competitive at high dark matter masses.

show abstract

Section: Jcap07(2023)033supporting

confidence: 74%

Section: Jcap07(2023)033mentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Searching for dark matter subhalos in the Fermi-LAT catalog with Bayesian neural networks

Butter

Krämer

Manconi

et al. 2023

J. Cosmol. Astropart. Phys.

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the abovementioned studies predominantly use machine-learning methods of the unsupervised type, where only the observed features of the GRBs are inputted into the models, but not the labels (the GRBs' physical classes being Type I or II). On the other hand, the other type of machinelearning methods, supervised methods, are also commonly employed by astronomy researchers in the classification of other astronomical objects (e.g., Luo et al 2023;Zhu-Ge et al 2023;Connor & van Leeuwen 2018;Butter et al 2022;Coronado-Blázquez 2022;de Beurs et al 2022;Fan et al 2022;Villa-Ortega et al 2022;Yang et al 2022a;Kaur et al 2023), although studies on the application of supervised methods on GRB are scarce. Since supervised methods take both features and labels as input, and can produce deterministic predictions of the class of new GRBs, they can be helpful in identifying the true physical origin of intermingled GRBs.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Physical Origin of Gamma-Ray Bursts with Supervised Machine Learning

Luo,

Wang,

Zhu-Ge

et al. 2023

ApJ

View full text Add to dashboard Cite

The empirical classification of gamma-ray bursts (GRBs) into long and short GRBs based on their durations is already firmly established. This empirical classification is generally linked to the physical classification of GRBs originating from compact binary mergers and GRBs originating from massive star collapses, or Type I and II GRBs, with the majority of short GRBs belonging to Type I and the majority of long GRBs belonging to Type II. However, there is a significant overlap in the duration distributions of long and short GRBs. Furthermore, some intermingled GRBs, i.e., short-duration Type II and long-duration Type I GRBs, have been reported. A multiparameter classification scheme of GRBs is evidently needed. In this paper, we seek to build such a classification scheme with supervised machine-learning methods, chiefly XGBoost. We utilize the GRB Big Table and Greiner’s GRB catalog and divide the input features into three subgroups: prompt emission, afterglow, and host galaxy. We find that the prompt emission subgroup performs the best in distinguishing between Type I and II GRBs. We also find the most important distinguishing features in prompt emission to be T 90, the hardness ratio, and fluence. After building the machine-learning model, we apply it to the currently unclassified GRBs to predict their probabilities of being either GRB class, and we assign the most probable class of each GRB to be its possible physical class.

show abstract

“…The unique advantages of the acoustic signal recognition method can be fully leveraged to characterize fluid flow inside columns. 16 In addition, supervised learning methods, such as artificial neural network, 17 decision tree, 18 Bayesian networks, 19 K-nearest neighbor, 20 and support vector machine (SVM), 21 with a continuous update property, can be combined with sound processing technologies to achieve fluid state recognition and fault classification. In this study, sieve tray distillation columns were investigated.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Signal-Based Method for Recognizing Fluid Flow States in Distillation Columns

Zhang

Wang

Yang

et al. 2022

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Real-time monitoring of the running states of processing equipment is essential for the stability and safety of unit operations in chemical engineering. In this study, an acoustic signalbased method is proposed for recognizing fluid flow states in distillation columns. This method could accurately identify various fluid flow states in distillation columns, including dispersion, flooding, and weeping states. First, acoustic signals corresponding to various fluid flow states were recorded in various columns. Then, the characteristic parameters of these acoustic signals, including the short-time-averaged energy (STAE), linear predictive cepstral coefficients, and Mel frequency cepstral coefficients (MFCCs), were extracted and fused from the time, frequency, and cepstrum domains. Finally, a multi-classification support vector machine (MSVM) model was coupled with the characteristic parameters to recognize the fluid flow states in distillation columns. The MSVM based on the STAE + MFCC characteristics yielded the highest identification accuracy (97.25%).

show abstract

Classification of Fermi-LAT blazars with Bayesian neural networks

Cited by 9 publications

References 48 publications

Searching for dark matter subhalos in the Fermi-LAT catalog with Bayesian neural networks

Searching for dark matter subhalos in the Fermi-LAT catalog with Bayesian neural networks

Identifying the Physical Origin of Gamma-Ray Bursts with Supervised Machine Learning

Acoustic Signal-Based Method for Recognizing Fluid Flow States in Distillation Columns

Contact Info

Product

Resources

About