Electronics and data acquisition system for the ICAL prototype detector of India-based neutrino observatory

A: The India-based Neutrino Observatory (INO) collaboration, as part of its detector R&D program, has developed prototype stacks of resistive plate chambers (RPCs) to study their performance. These stacks have also been used as testbenches for the development of related hardware and software. A crucial parameter in the characterisation of these detectors and other physics studies is the detection efficiency, which is estimated from track fitting of cosmic muons passing through the stack. So far, a simple straight line fit was used for track fitting, which was sensitive to noise hits and led to rejection of events. In this paper, we present our first results of using artificial neural networks (ANN) for track fitting of cosmic muons traversing a stack of RPCs. We present in detail, the simulation framework designed for this purpose and show that ANN offers better track reconstruction efficiency than straight line fitting. We also discuss the influence of noise and detection efficiency of cosmic muons on the track reconstruction efficiency.

show abstract

“…A detailed description of the detector setup and data acquisition system at TIFR can be found in Refs. [6] and [7].…”

Section: The Tifr Prototype Stackmentioning

confidence: 99%

Artificial neural networks-based track fitting of cosmic muons through stacked resistive plate chambers

Samuel

Suresh

2018

J. Inst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The detector will consist of three identical modules each of dimension 16 m × 16 m × 14.5 m placed adjacent to each other as shown in figure 1(a). Each module is made up of 151 horizontal layers of 56 mm thick low carbon iron plates interleaved with 40 mm gaps to house the Resistive Plate Chamber (RPC) units [3].…”

Section: Geometry Of Ical Detectormentioning

confidence: 99%

Online track detection in triggerless mode for INO

et al. 2018

Self Cite

View full text Add to dashboard Cite

A: The India based Neutrino Observatory (INO) is a proposed particle physics research project to study the atmospheric neutrinos. INO-Iron Calorimeter (ICAL) will consist of 28,800 detectors having 3.6 million electronic channels expected to activate with 100 Hz single rate, producing data at a rate of 3 GBps. Data collected contains a few real hits generated by muon tracks and the remaining noise-induced spurious hits. Estimated reduction factor after filtering out data of interest from generated data is of the order of 10 3 . This makes trigger generation critical for efficient data collection and storage. Trigger is generated by detecting coincidence across multiple channels satisfying trigger criteria, within a small window of 200 ns in the trigger region. As the probability of neutrino interaction is very low, track detection algorithm has to be efficient and fast enough to process 5 × 10 6 events-candidates/s without introducing significant dead time, so that not even a single neutrino event is missed out.A hardware based trigger system is presently proposed for on-line track detection considering stringent timing requirements. Though the trigger system can be designed with scalability, a lot of hardware devices and interconnections make it a complex and expensive solution with limited flexibility. A software based track detection approach working on the hit information offers an elegant solution with possibility of varying trigger criteria for selecting various potentially interesting physics events. An event selection approach for an alternative triggerless readout scheme has been developed. The algorithm is mathematically simple, robust and parallelizable. It has been validated by detecting simulated muon events for energies of the range of 1 GeV-10 GeV with 100% efficiency at a processing rate of 60 µs/event on a 16 core machine. The algorithm and result of a proof-of-concept for its faster implementation over multiple cores is presented. The paper 1Corresponding author.

show abstract

“…The total area covered by the paddles was 960 mm × 960 mm, which is effectively the active area of RPC. The hit information of the paddles, along with the hit information of RPCs, was stored in the latch data of the Data Acquisition system of RPC stack [10] for every event. The timing information of the hit was stored in commercial multi-hit TDC [11].…”

Section: Other Sources Of Inefficiencies In CMVmentioning

confidence: 99%

A compact cosmic muon veto detector and possible use with the Iron Calorimeter detector for neutrinos

Panchal¹,

Mohanraj²,

Kumar³

et al. 2017

J. Inst.

Self Cite

View full text Add to dashboard Cite

A: The motivation for a cosmic muon veto (CMV) detector is to explore the possibility of locating the proposed large Iron Calorimeter (ICAL) detector at the India based Neutrino Observatory (INO) at a shallow depth. An initial effort in that direction, through the assembly and testing of a ∼1 m × 1 m × 0.3 m plastic scintillator based detector, is described. The plan for making a CMV detector for a smaller prototype mini-ICAL is also outlined.

show abstract

Electronics and data acquisition system for the ICAL prototype detector of India-based neutrino observatory

Cited by 11 publications

References 6 publications

Artificial neural networks-based track fitting of cosmic muons through stacked resistive plate chambers

Artificial neural networks-based track fitting of cosmic muons through stacked resistive plate chambers

Online track detection in triggerless mode for INO

A compact cosmic muon veto detector and possible use with the Iron Calorimeter detector for neutrinos

Contact Info

Product

Resources

About