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Author |
CALICE Collaboration (Lai, S. et al); Irles, A. |
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Title |
Software compensation for highly granular calorimeters using machine learning |
Type |
Journal Article |
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Year |
2024 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
19 |
Issue |
4 |
Pages |
P04037 - 28pp |
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Keywords  |
Large detector-systems performance; Pattern recognition; cluster finding; calibration and fitting methods; Performance of High Energy Physics Detectors |
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Abstract |
A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied. |
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Address |
[Lai, S.; Utehs, J.; Wilhahn, A.] Georg August Univ Gottingen, Phys Inst 2, Friedrich Hund Pl 1, D-37077 Gottingen, Germany, Email: jack.rolph@desy.de |
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Publisher |
IOP Publishing Ltd |
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Language |
English |
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ISSN |
1748-0221 |
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Conference |
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Notes |
WOS:001230094600001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6128 |
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Author |
Yepes, H. |
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Title |
The ANTARES neutrino detector instrumentation |
Type |
Journal Article |
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Year |
2012 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
7 |
Issue |
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Pages |
C01022 - 9pp |
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Keywords |
Large detector-systems performance; Performance of High Energy Physics Detectors; Detector alignment and calibration methods (lasers, sources, particle-beams) |
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Abstract |
ANTARES is actually the fully operational and the largest neutrino telescope in the Northern hemisphere. Located in the Mediterranean Sea, it consists of a 3D array of 885 photomultiplier tubes (PMTs) arranged in 12 detection lines (25 storeys each), able to detect the Cherenkov light induced by upgoing relativistic muons produced in the interaction of high energy cosmic neutrinos with the detector surroundings. Among its physics goals, the search for neutrino astrophysical sources and the indirect detection of dark matter particles coming from the sun are of particular interest. To reach these goals, good accuracy in track reconstruction is mandatory, so several calibration systems for timing and positioning have been developed. In this contribution we will present the design of the detector, calibration systems, associated equipment and its performance on track reconstruction. |
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Address |
Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, E-46071 Valencia, Spain, Email: Harold.Yepes@ific.uv.es |
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Publisher |
Iop Publishing Ltd |
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English |
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ISSN |
1748-0221 |
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Conference |
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Notes |
WOS:000303806200022 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
1041 |
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Author |
Arnalte-Mur, P.; Labatie, A.; Clerc, N.; Martinez, V.J.; Starck, J.L.; Lachieze-Rey, M.; Saar, E.; Paredes, S. |
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Title |
Wavelet analysis of baryon acoustic structures in the galaxy distribution |
Type |
Journal Article |
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Year |
2012 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
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Volume |
542 |
Issue |
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Pages |
A34 - 11pp |
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Keywords |
large-scale structure of Universe; distance scale; galaxies: cluster: general; methods: data analysis; methods: statistical |
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Abstract |
Context. Baryon acoustic oscillations (BAO) are imprinted in the density field by acoustic waves travelling in the plasma of the early universe. Their fixed scale can be used as a standard ruler to study the geometry of the universe. Aims. The BAO have been previously detected using correlation functions and power spectra of the galaxy distribution. We present a new method to detect the real-space structures associated with BAO. These baryon acoustic structures are spherical shells of relatively small density contrast, surrounding high density central regions. Methods. We design a specific wavelet adapted to search for shells, and exploit the physics of the process by making use of two different mass tracers, introducing a specific statistic to detect the BAO features. We show the effect of the BAO signal in this new statistic when applied to the Lambda – cold dark matter (Lambda CDM) model, using an analytical approximation to the transfer function. We confirm the reliability and stability of our method by using cosmological N-body simulations from the MareNostrum Institut de Ciencies de l'Espai (MICE). Results. We apply our method to the detection of BAO in a galaxy sample drawn from the Sloan Digital Sky Survey (SDSS). We use the “main” catalogue to trace the shells, and the luminous red galaxies (LRG) as tracers of the high density central regions. Using this new method, we detect, with a high significance, that the LRG in our sample are preferentially located close to the centres of shell-like structures in the density field, with characteristics similar to those expected from BAO. We show that stacking selected shells, we can find their characteristic density profile. Conclusions. We delineate a new feature of the cosmic web, the BAO shells. As these are real spatial structures, the BAO phenomenon can be studied in detail by examining those shells. |
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Address |
[Arnalte-Mur, P.; Martinez, V. J.] Univ Valencia, Astron Observ, Valencia 46071, Spain, Email: pablo.arnalte-mur@durham.ac.uk |
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Publisher |
Edp Sciences S A |
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English |
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ISSN |
0004-6361 |
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Notes |
WOS:000305803300021 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1088 |
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Author |
de los Rios, M.; Petac, M.; Zaldivar, B.; Bonaventura, N.R.; Calore, F.; Iocco, F. |
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Title |
Determining the dark matter distribution in simulated galaxies with deep learning |
Type |
Journal Article |
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Year |
2023 |
Publication |
Monthly Notices of the Royal Astronomical Society |
Abbreviated Journal |
Mon. Not. Roy. Astron. Soc. |
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Volume |
525 |
Issue |
4 |
Pages |
6015-6035 |
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Keywords |
methods: data analysis; software: simulations; galaxies: general; galaxies: haloes; dark matter |
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Abstract |
We present a novel method of inferring the dark matter (DM) content and spatial distribution within galaxies, using convolutional neural networks (CNNs) trained within state-of-the-art hydrodynamical simulations (Illustris-TNG100). Within the controlled environment of the simulation, the framework we have developed is capable of inferring the DM mass distribution within galaxies of mass similar to 10(11)-10(13)M(circle dot) from the gravitationally baryon-dominated internal regions to the DM-rich, baryon-depleted outskirts of the galaxies, with a mean absolute error always below approximate to 0.25 when using photometrical and spectroscopic information. With respect to traditional methods, the one presented here also possesses the advantages of not relying on a pre-assigned shape for the DM distribution, to be applicable to galaxies not necessarily in isolation, and to perform very well even in the absence of spectroscopic observations. |
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Address |
[de los Rios, Martin] Univ Estadual Paulista, ICTP South Amer Inst Fundamental Res, Inst Fis Teor, BR-01140070 Sao Paulo, SP, Brazil, Email: fabio.iocco.astro@gmail.com |
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Publisher |
Oxford Univ Press |
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English |
Summary Language |
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ISSN |
0035-8711 |
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Notes |
WOS:001072112100006 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5707 |
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Author |
Stoppa, F.; Bhattacharyya, S.; Ruiz de Austri, R.; Vreeswijk, P.; Caron, S.; Zaharijas, G.; Bloemen, S.; Principe, G.; Malyshev, D.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G. |
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Title |
AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information |
Type |
Journal Article |
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Year |
2023 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
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Volume |
680 |
Issue |
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Pages |
A109 - 16pp |
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Keywords |
methods: data analysis; techniques: image processing; astronomical databases: miscellaneous; stars: imaging; Galaxies: statistics |
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Abstract |
Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C's direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy. |
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Address |
[Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl |
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Corporate Author |
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Thesis |
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Publisher |
Edp Sciences S A |
Place of Publication |
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Language |
English |
Summary Language |
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ISSN |
0004-6361 |
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Notes |
WOS:001131898100001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5888 |
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