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Register a new userUse of Machine Learning for gamma/hadron separation with HAWC
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Tom\\'as Capistr\\'an
Publication date
2021
fields
Physics
and research's language is
English
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Background showers triggered by hadrons represent over 99.9% of all particles arriving at ground-based gamma-ray observatories. An important stage in the data analysis of these observatories, therefore, is the removal of hadron-triggered showers. Currently, the High-Altitude Water Cherenkov (HAWC) gamma-ray observatory employs an algorithm based on a single cut in two variables, unlike other ground-based gamma-ray observatories (e.g. H.E.S.S., VERITAS), which employ a large number of variables to separate the primary particles. In this work, we explore machine learning techniques (Boosted Decision Trees and Neural Networks) to identify the primary particles detected by HAWC. Our new gamma/hadron separation techniques were tested on data from the Crab nebula, the standard reference in Very High Energy astronomy, showing an improvement compared to the standard HAWC background rejection method.
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The High-Altitude Water Cherenkov (HAWC) Observatory is a ground based air-shower array deployed on the slopes of Volcan Sierra Negra in the state of Puebla, Mexico. While HAWC is optimized for the detection of gamma-ray induced air-showers, the background flux of hadronic cosmic-rays is four orders of magnitude greater, making background rejection paramount for gamma-ray observations. On average, gamma-ray and cosmic-ray showers are characterized by different topologies at ground level. We will present a method to identify the primary particle type in an air-shower that uses the spatial relationship of triggered PMTs (or hits) in the detector. For a given event hit-pattern on the HAWC array, we calculate the mean separation distance of the hits for a subset of hit pairs weighted by their charges. By comparing the mean charge and mean separating distance for the selected hits, we infer the identity of the events primary. We will report on the efficiency for identifying gamma-rays and the performance of the technique with simulation.
AGNs are very powerful galaxies characterized by extremely bright emissions coming out from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate important astrophysical problems such as the evolution of the early stars, their formation along with the structure of early galaxies. The redshift determination is challenging because it requires detailed follow-up of multi-wavelength observations, often involving various astronomical facilities. Here, we employ machine learning algorithms to estimate redshifts from the observed gamma-ray properties and photometric data of gamma-ray loud AGN from the Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearner algorithm, using LASSO selected set of predictors. We obtain a tight correlation, with a Pearson Correlation Coefficient of 71.3% between the inferred and the observed redshifts, an average {Delta}z_norm = 11.6 x 10^-4. We stress that notwithstanding the small sample of gamma-ray loud AGNs, we obtain a reliable predictive model using Superlearner, which is an ensemble of several machine learning models.
We present a new catalog of TeV gamma-ray sources using 1523 days of data from the High Altitude Water Cherenkov (HAWC) observatory. The catalog represents the most sensitive survey of the Northern gamma-ray sky at energies above several TeV, with three times the exposure compared to the previous HAWC catalog, 2HWC. We report 65 sources detected at $geq$ 5 sigma significance, along with the positions and spectral fits for each source. The catalog contains eight sources that have no counterpart in the 2HWC catalog, but are within $1^circ$ of previously detected TeV emitters, and twenty sources that are more than $1^circ$ away from any previously detected TeV source. Of these twenty new sources, fourteen have a potential counterpart in the fourth textit{Fermi} Large Area Telescope catalog of gamma-ray sources. We also explore potential associations of 3HWC sources with pulsars in the ATNF pulsar catalog and supernova remnants in the Galactic supernova remnant catalog.
Studying gamma-ray emission by Galactic objects is key to understanding the origins and acceleration mechanisms of Galactic cosmic ray electrons and hadrons. The HAWC observatory provides an unprecedented view of the gamma-ray sky at TeV energies and is particularly suited for the study of Galactic objects. However, the interpretation of the measured data poses several challenges. The high density of sources and source candidates can cause source confusion and make it harder to disentangle the origin of the emission. The relatively low angular resolution of HAWC, compared to instruments in optical or radio wavelengths, can further cause the emission of neighboring sources to bleed into each other or even make them look like one extended source. On the other hand, with its wide field of view, HAWC is uniquely suited for the study of extended sources. However, this requires the simultaneous modeling of both their morphology and emission spectrum. Joint likelihood fits to data taken over a larger range of energies can help overcome these challenges and achieve the full potential of the HAWC detector. In this presentation, we will discuss how systematic uncertainties related to joint likelihood fits can affect the measurements.
We study individual pulses of Vela (PSR B0833-45,/,J0835-4510) from daily observations of over three hours (around 120,000 pulses per observation), performed simultaneously with the two radio telescopes at the Argentine Institute of Radioastronomy. We select 4 days of observations in January-March 2021 and study their statistical properties with machine learning techniques. We first use density based DBSCAN clustering techniques, associating pulses mainly by amplitudes, and find a correlation between higher amplitudes and earlier arrival times. We also find a weaker (polarization dependent) correlation with the mean width of the pulses. We identify clusters of the so-called mini-giant pulses, with $sim10times$ the average pulse amplitude. We then perform an independent study, with Self-Organizing Maps (SOM) clustering techniques. We use Variational AutoEncoder (VAE) reconstruction of the pulses to separate them clearly from the noise and select one of the days of observation to train VAE and apply it to thre rest of the observations. We use SOM to determine 4 clusters of pulses per day per radio telescope and conclude that our main results are robust and self-consistent. These results support models for emitting regions at different heights (separated each by roughly a hundred km) in the pulsar magnetosphere. We also model the pulses amplitude distribution with interstellar scintillation patterns at the inter-pulses time-scale finding a characterizing exponent $n_{mathrm{ISS}}sim7-10$. In the appendices we discuss independent checks of hardware systematics with the simultaneous use of the two radio telescopes in different one-polarization / two-polarizations configurations. We also provide a detailed analysis of the processes of radio-interferences cleaning and individual pulse folding.
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